+
### External contributions
All external contributions are welcome.
From 2512afd6a7a76781b1d5f5ce98d6bdc64177690b Mon Sep 17 00:00:00 2001
From: AdrienTaylor <10559960+AdrienTaylor@users.noreply.github.com>
Date: Thu, 12 Mar 2026 19:27:04 +0100
Subject: [PATCH 04/12] whatsnew
---
docs/source/whatsnew.rst | 1 +
docs/source/whatsnew/0.5.1.rst | 17 +++++++++++++++++
2 files changed, 18 insertions(+)
create mode 100644 docs/source/whatsnew/0.5.1.rst
diff --git a/docs/source/whatsnew.rst b/docs/source/whatsnew.rst
index 4334ce05..5df7ca0e 100644
--- a/docs/source/whatsnew.rst
+++ b/docs/source/whatsnew.rst
@@ -13,3 +13,4 @@ What's new in PEPit
whatsnew/0.3.3
whatsnew/0.4.0
whatsnew/0.5.0
+ whatsnew/0.5.1
diff --git a/docs/source/whatsnew/0.5.1.rst b/docs/source/whatsnew/0.5.1.rst
new file mode 100644
index 00000000..b8c33e1f
--- /dev/null
+++ b/docs/source/whatsnew/0.5.1.rst
@@ -0,0 +1,17 @@
+What's new in PEPit 0.4.1
+=========================
+
+New features:
+-------------
+
+-
+
+New demo:
+---------
+
+- The file `ressources/demo/PEPit_demo_algorithm_design.ipynb` contains examples for algorithm design with PEPit.
+
+Fixes:
+------
+
+- Improved constraint naming within the exact linesearch procedure.
From 9bdf6d08608d7c13462d47bd7a72ee19d92b5ba3 Mon Sep 17 00:00:00 2001
From: Daniel Berg Thomsen
Date: Thu, 12 Mar 2026 21:59:04 +0100
Subject: [PATCH 05/12] fix some typos, latex rendering
---
.../demo/PEPit_demo_algorithm_design.ipynb | 29 ++++++++-----------
1 file changed, 12 insertions(+), 17 deletions(-)
diff --git a/ressources/demo/PEPit_demo_algorithm_design.ipynb b/ressources/demo/PEPit_demo_algorithm_design.ipynb
index a65b1e99..7ca80e0b 100644
--- a/ressources/demo/PEPit_demo_algorithm_design.ipynb
+++ b/ressources/demo/PEPit_demo_algorithm_design.ipynb
@@ -27,7 +27,7 @@
"- design via the subspace-search elimination procedure [5, 6],\n",
"- design via nonlinear optimization (working example using the approach from [8]; see also [9]).\n",
"\n",
- "**Worked examples:** vanilla gradient descent, silver steps [4], an optimal method for nonsmooth convex minimization [6], and the optimized gradient method [1, 3]. The optimal gradient method is presented through the lens of conjugate gradient-like methods [5], but were originally discovered via convex relaxations [1, 3]."
+ "**Worked examples:** vanilla gradient descent, silver steps [4], an optimal method for nonsmooth convex minimization [6], and the optimized gradient method [1, 3]. The optimized gradient method is presented through the lens of conjugate gradient-like methods [5], but it was originally discovered via convex relaxations [1, 3]."
]
},
{
@@ -1383,8 +1383,8 @@
"source": [
"## 2. Numerical design via ideal algorithms \n",
"\n",
- "In this section, we perform algorithm design via [5]. That is, we study *ideal* algorithm that are allowed to do exact linesearches\n",
- "(and even spansearches), and deduce, from their analyses and worst-case bounds, algorithm that do not require any such operations.\n",
+ "In this section, we perform algorithm design via [5]. That is, we study *ideal* algorithms that are allowed to perform exact line searches\n",
+ "(and even span searches), and deduce, from their analyses and worst-case bounds, algorithms that do not require any such operations.\n",
"\n",
"### 2.1. Back to gradient descent, exact line-search\n",
"\n",
@@ -1556,13 +1556,13 @@
"tags": []
},
"source": [
- "### 2.3. Algorithm design for nonsmooth convex minimization \n",
+ "### 2.3. Algorithm design for nonsmooth convex minimization\n",
"\n",
"The idea of this section is to apply the same technique to the class of (possibly nonsmooth) convex optimization problems\n",
"$$ \\min_{x\\in\\mathbb{R}^d} f(x),$$\n",
"where $f$ is convex and $M$-Lipschitz: $|f(x)-f(y)|\\leq M\\|x-y\\|_2$ for all $x,y\\in\\mathbb{R}^d$ (i.e., for all $x\\in\\mathbb{R}^d$ and $g_i\\in\\partial f(x)$ we have $\\|g_i\\|_2\\leq M$).\n",
"\n",
- "**Differentiable $f$.** Assume for now that $f$ is differentiable. To design an (hopefully optimal) algorithm for this class, we start by analyzing the following \"conjugate gradient-like\" method:\n",
+ "**Differentiable $f$.** Assume for now that $f$ is differentiable. To design a (hopefully optimal) algorithm for this class, we start by analyzing the following \"conjugate gradient-like\" method:\n",
"\n",
"$$ x_{k+1} \\in \\mathrm{argmin}_x\\big\\{ f(x)\\,:\\, x\\in x_0+\\mathrm{span}\\{\\nabla f(x_0),\\nabla f(x_1),\\ldots,\\nabla f(x_k)\\}\\big\\}.$$\n",
"\n",
@@ -1574,7 +1574,7 @@
"\\end{aligned}$$\n",
"\n",
"\n",
- "Reorganizing those terms, and resorting on relaxations arguments similar to those used for gradient descent with exact linesearch, one can come up with the following constraint relaxation:\n",
+ "Reorganizing those terms, and resorting to relaxation arguments similar to those used for gradient descent with exact line search, one can come up with the following constraint relaxation:\n",
"\n",
"$$\\left\\langle \\nabla f(w_i),\\sum_{j=1}^i \\beta_{i,j}(w_j-w_0)+\\sum_{j=0}^{i-1} \\gamma_{i,j}\\nabla f(w_j)\\right\\rangle=0 \\quad \\text{for all } i=1,\\ldots,n,$$\n",
"\n",
@@ -1929,9 +1929,9 @@
"source": [
"So it looks like we can collect the coefficients and form nice constraints:\n",
"\n",
- "denote by $gi\\in\\partial f(x_i)$; all methods satisfying\n",
+ "denote by $g_i\\in\\partial f(x_i)$; all methods satisfying\n",
"$$ 0=\\langle g_i,x_i-\\left[\\frac{i}{i+1}x_{i-1}+\\frac{1}{i+1}x_0-\\frac{1}{i+1} \\frac{R}{M\\sqrt{n+1}}\\sum_{j=0}^{i-1}g_j \\right]\\rangle$$ \n",
- "benefits from the worst-case guarantee\n",
+ "benefit from the worst-case guarantee\n",
"$$ f(x_n)-f_\\star \\leq \\frac{M R}{\\sqrt{n+1}}.$$ \n",
"Below a PEPit implementation of the resulting algorithm (sometimes referred to as a \"quasi-monotone subgradient method\" [7], given that the worst-case guarantee is monotone)."
]
@@ -2016,7 +2016,7 @@
"id": "4830b9d7-2513-480d-959f-2006a46bac4c",
"metadata": {},
"source": [
- "## 3. Back to smooth convex minimization: the optimized gradient method\n",
+ "## 3. Back to smooth convex minimization: the optimized gradient method \n",
"\n",
"The idea for this section is to apply the same technique to the class of smooth convex optimization problems\n",
"$$ \\min_{x\\in\\mathbb{R}^d} f(x),$$\n",
@@ -2336,12 +2336,7 @@
"$$\n",
"\\phi_{k}\\leq \\phi_{k-1},\n",
"$$\n",
- "with $\\phi_k\\triangleq 2\\theta_{k,N}^2\n",
- "\\left(\n",
- "f(y_k) - f_\\star\n",
- "- \\frac{1}{2L}\\lVert \\nabla f(y_k) \\rVert_2^2\n",
- "\\right)\n",
- "+ \\frac{L}{2}\\|z_{k+1} - x_\\star\\|_2^2$."
+ "with $$\\phi_k\\triangleq 2\\theta_{k,N}^2 \\left(f(y_k) - f_\\star - \\frac{1}{2L}\\lVert \\nabla f(y_k) \\rVert_2^2 \\right) + \\frac{L}{2}\\|z_{k+1} - x_\\star\\|_2^2.$$"
]
},
{
@@ -2471,7 +2466,7 @@
"id": "c3a5f27e-e2f0-4bd8-be52-6673b8db75df",
"metadata": {},
"source": [
- "**Summary.** The inequality can be obtained is obtained from the same potential as that used for the OGM, obtained from further inequalities. That is, we first perform a weighted sum of the following inequalities (this is actually for conjugate gradients, but works for OGM).\n",
+ "**Summary.** The inequality can be obtained from the same potential as that used for OGM, itself obtained from further inequalities. That is, we first perform a weighted sum of the following inequalities (this is actually for conjugate gradients, but works for OGM).\n",
"\n",
"- **Smoothness and convexity of $f$ between $y_{k-1}$ and $y_k$ with weight $\\lambda_1 = 2\\theta_{k-1,N}^2$:**\n",
"\n",
@@ -2627,7 +2622,7 @@
"\n",
"[10] D. Kim and J. A. Fessler, *Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions*, Journal of Optimization Theory and Applications, 2021. [arXiv:1803.06600](https://arxiv.org/abs/1803.06600)\n",
"\n",
- "[11] Y. Drori and A. B. Taylor, *An optimal first-order method for smooth strongly convex minimization*, Mathematical Programming, 2022. [arXiv:2004.11271](https://arxiv.org/abs/2101.09741)\n",
+ "[11] Y. Drori and A. B. Taylor, *An optimal gradient method for smooth strongly convex minimization*, Mathematical Programming, 2022. [arXiv:2101.09741](https://arxiv.org/abs/2101.09741)\n",
"\n",
"[12] U. Jang, S. Das Gupta, and E. K. Ryu, *Computer-Assisted Design of Accelerated Composite Optimization Methods: OptISTA*, Mathematical Programming, 2025. [arXiv:2305.15704](https://arxiv.org/abs/2305.15704)"
]
From 012f4d6cbaef2141c0fb7504097b71ce5934f7bd Mon Sep 17 00:00:00 2001
From: AdrienTaylor <10559960+AdrienTaylor@users.noreply.github.com>
Date: Thu, 12 Mar 2026 22:56:36 +0100
Subject: [PATCH 06/12] readme
---
README.md | 14 ++++++--------
1 file changed, 6 insertions(+), 8 deletions(-)
diff --git a/README.md b/README.md
index efb8f9b2..d2dc0bb0 100644
--- a/README.md
+++ b/README.md
@@ -276,14 +276,12 @@ PEPit supports the following [operator classes](https://pepit.readthedocs.io/en/
### Creators, internal contributors and maintainers
-This toolbox has been created by
-
-- [**Baptiste Goujaud**](https://www.linkedin.com/in/baptiste-goujaud-b60060b3/)
-- [**Céline Moucer**](https://cmoucer.github.io)
-- [**Julien Hendrickx**](https://perso.uclouvain.be/julien.hendrickx/index.html)
-- [**François Glineur**](https://perso.uclouvain.be/francois.glineur/)
-- [**Adrien Taylor**](https://adrientaylor.github.io/)
-- [**Aymeric Dieuleveut**](http://www.cmap.polytechnique.fr/~aymeric.dieuleveut/)
+- [**Baptiste Goujaud**](https://bgoujaud.github.io/) (creator and maintainer)
+- [**Céline Moucer**](https://cmoucer.github.io) (creator)
+- [**Julien Hendrickx**](https://perso.uclouvain.be/julien.hendrickx/index.html) (creator)
+- [**François Glineur**](https://perso.uclouvain.be/francois.glineur/) (creator)
+- [**Adrien Taylor**](https://adrientaylor.github.io/) (creator and maintainer)
+- [**Aymeric Dieuleveut**](http://www.cmap.polytechnique.fr/~aymeric.dieuleveut/) (creator and maintainer)
- [**Daniel Berg Thomsen**](https://bergthomsen.com/) (internal contributor)
From f8c7f14ebcca6dfed39f59d96381fa1e77e15762 Mon Sep 17 00:00:00 2001
From: Daniel Berg Thomsen
Date: Fri, 13 Mar 2026 09:30:03 +0100
Subject: [PATCH 07/12] fix some grammar and flow
---
.../demo/PEPit_demo_algorithm_design.ipynb | 110 +++++++++---------
1 file changed, 55 insertions(+), 55 deletions(-)
diff --git a/ressources/demo/PEPit_demo_algorithm_design.ipynb b/ressources/demo/PEPit_demo_algorithm_design.ipynb
index 7ca80e0b..66552aa5 100644
--- a/ressources/demo/PEPit_demo_algorithm_design.ipynb
+++ b/ressources/demo/PEPit_demo_algorithm_design.ipynb
@@ -7,7 +7,7 @@
"source": [
"# Minicourse on performance estimation problems, part 3: algorithm design\n",
"\n",
- "The goal of this notebook is to introduce a few techniques for algorithm design based on the performance estimation framework. More precisely, treating performance estimation problems as black boxes capable of providing tight worst-case certificates, a natural way forward is to design algorithms with optimized worst-case performance.\n",
+ "The goal of this notebook is to introduce a few techniques for algorithm design based on the performance estimation framework. More precisely, we treat performance estimation problems as black boxes capable of providing tight worst-case certificates, and then use them to design algorithms with optimized worst-case performance.\n",
"\n",
"For instance, consider the optimization problem\n",
"$$\\min_{x\\in\\mathbb{R}^d} f(x),$$\n",
@@ -19,13 +19,13 @@
"&\\vdots\\\\\n",
"y_N &= y_{N-1} - \\sum_{i=0}^{N-1} h_{N,i} \\nabla f(y_i),\n",
"\\end{aligned} \\tag{1}\\label{eq:ogm_start}$$\n",
- "for which one may want to choose $\\{h_{i,j}\\}$ as a solution to the minimax problem:\n",
+ "and aim to choose $\\{h_{i,j}\\}$ as a solution to the minimax problem:\n",
"$$\\min_{\\{h_{i,j}\\}}\\,\\, \\max_{f\\in\\mathcal{F}_{0,L}} \\left\\{ \\frac{f(y_N)-f_\\star}{\\|y_0-x_\\star\\|^2_2} \\, : \\, y_N \\text{ obtained from } \\eqref{eq:ogm_start} \\text{ and } y_0 \\right\\}. \\tag{2}\\label{eq:pep_opt_h}$$\n",
- "One natural approach is to choose the step sizes as a solution to $\\eqref{eq:pep_opt_h}$. Of course, methods of the form $\\eqref{eq:ogm_start}$ are impractical in general, since they require keeping track of all previous gradients, and we will try to *factor* them into more convenient forms. This notebook illustrates the following approaches:\n",
+ "One natural approach is to choose the step sizes by solving $\\eqref{eq:pep_opt_h}$. Of course, methods of the form $\\eqref{eq:ogm_start}$ are impractical in general, since they require keeping track of all previous gradients, and we will try to *factor* them into more convenient forms. This notebook illustrates the following approaches:\n",
"\n",
"- design by exploration and structural properties,\n",
"- design via the subspace-search elimination procedure [5, 6],\n",
- "- design via nonlinear optimization (working example using the approach from [8]; see also [9]).\n",
+ "- design via nonlinear optimization (working example based on the approach in [8]; see also [9]).\n",
"\n",
"**Worked examples:** vanilla gradient descent, silver steps [4], an optimal method for nonsmooth convex minimization [6], and the optimized gradient method [1, 3]. The optimized gradient method is presented through the lens of conjugate gradient-like methods [5], but it was originally discovered via convex relaxations [1, 3]."
]
@@ -81,9 +81,9 @@
"source": [
"### 1.1. Base gradient descent\n",
"\n",
- "We first consider a direct approach: fix the class parameters $L, \\mu$ (chosen below) and experiment with different step size values $\\gamma$. Verify that the obtained convergence rate is smaller than one only when $\\gamma \\in \\left(0, \\frac{2}{L}\\right)$, and that it matches the well-known bound $\\max\\left\\{(1-\\gamma L)^2,\\, (1-\\gamma\\mu)^2\\right\\}$.\n",
+ "We first consider a direct approach: fix the class parameters $L, \\mu$ (chosen below) and experiment with different step sizes $\\gamma$. Verify that the resulting convergence rate is smaller than one only when $\\gamma \\in \\left(0, \\frac{2}{L}\\right)$, and that it matches the well-known bound $\\max\\left\\{(1-\\gamma L)^2,\\, (1-\\gamma\\mu)^2\\right\\}$.\n",
"\n",
- "The following code is standard for you and can be found in the previous exercise sessions."
+ "The following code is standard and was already introduced in the previous exercise sessions."
]
},
{
@@ -229,7 +229,7 @@
"id": "d68869af-f3db-4660-a26e-66c8b56de067",
"metadata": {},
"source": [
- "Identify inequality pattern: does it make sense/can you explain it in view of the PEP theory you are aware of?"
+ "Identify the pattern in the inequalities: does it make sense, and can you explain it in light of the PEP theory you know?"
]
},
{
@@ -294,7 +294,7 @@
"id": "0fa2034c-71a3-4d14-b832-8b8438b18e87",
"metadata": {},
"source": [
- "Now, our goal is to compute the optimal point (assuming we don't know its parametric value is 2/(L+mu)) by observing a few properties of the corresponding PEP objects. In particular, observe the value of the residual matrix (at the best point of the grid) and compare it with the value for other step size. Can you explain this intuitively? Does it give you a hint on how to algebraically characterize this optimal stepsize?"
+ "Now, our goal is to compute the optimal point by observing a few properties of the corresponding PEP objects, assuming we do not already know that its parametric value is $2/(L+\\mu)$. In particular, observe the value of the residual matrix (at the best point of the grid) and compare it with its value for other step sizes. Can you explain this intuitively? Does it give you a hint on how to algebraically characterize this optimal step size?"
]
},
{
@@ -330,7 +330,7 @@
"id": "64f82c2f-1095-4aa7-b418-206522b4856c",
"metadata": {},
"source": [
- "Now that you understood some algebraic properties of the optimal step size, let us try to compute it directly via symbolic computations (using SymPy)."
+ "Now that you understand some algebraic properties of the optimal step size, let us try to compute it directly via symbolic computations (using SymPy)."
]
},
{
@@ -389,7 +389,7 @@
"id": "136669a9-7097-47fa-8fa2-7094db30a7a6",
"metadata": {},
"source": [
- "One can now proceed step-by-step to cancel out both terms (the LMI/residual and the linear constraint) --- we show below how to do it all-at-once, but step-by-step is nice pedagogically and to get used to SymPy:"
+ "We can now proceed step by step to cancel both terms, namely the LMI/residual and the linear constraint. We show below how to do everything at once, but the step-by-step approach is pedagogically useful and helps one get used to SymPy:"
]
},
{
@@ -572,8 +572,8 @@
"id": "441f3e90-f0de-4b2b-872f-d3b8c628ffcc",
"metadata": {},
"source": [
- "Nice: this seems pretty much in line with one could have expected\n",
- "The next lines do the exact same operations but all-at-once!"
+ "Nice: this is very much in line with what one would expect.\n",
+ "The next lines perform exactly the same operations, but all at once."
]
},
{
@@ -657,7 +657,7 @@
"tags": []
},
"source": [
- "Now, a natural question is that of finding a proof for this result, based on our findings:"
+ "A natural next question is how to turn these findings into a proof of the result:"
]
},
{
@@ -667,12 +667,12 @@
"tags": []
},
"source": [
- "Method: $x_1=x_0-\\gamma \\nabla f(x_0)$. Optimal step size is $\\tfrac{2}{L+\\mu}$, proof is as follows. Weighted sum of\n",
+ "Method: $x_1=x_0-\\gamma \\nabla f(x_0)$. The optimal step size is $\\tfrac{2}{L+\\mu}$, and the proof is as follows. Consider the weighted sum of\n",
"\n",
"- interpolation between $x_0$ and $x_\\star$ with weight $\\lambda_1=2\\gamma (1-\\gamma\\mu)$\n",
"- interpolation between $x_\\star$ and $x_0$ with weight $\\lambda_2=\\lambda_1$\n",
"\n",
- "the weighted sum gives (by completing the squares; a.k.a., \"analytical Cholesky\"):\n",
+ "The weighted sum gives (by completing the squares; a.k.a., \"analytical Cholesky\"):\n",
"$$\\|x_1-x_\\star\\|^2\\leq (1-\\gamma \\mu)^2 \\|x_0-x_\\star\\|^2 - \\tfrac{\\gamma(2-\\gamma(L+\\mu))}{L-\\mu}\\|\\mu (x_0-x_\\star)-\\nabla f(x_0)\\|^2.$$\n",
"\n",
"Cancelling the last term, we obtain the value for $\\gamma$, and the rate $\\left(\\frac{L-\\mu}{L+\\mu}\\right)^2$."
@@ -683,7 +683,7 @@
"id": "196d7691-b9bd-4542-b49b-dbeb4346e5b6",
"metadata": {},
"source": [
- "### 1.2. Multistep gradient descent (aka Silver stepsize schedule [4])"
+ "### 1.2. Multistep gradient descent (aka the Silver step-size schedule [4])"
]
},
{
@@ -703,9 +703,9 @@
"$$\\min_{\\gamma_1,\\gamma_2}\\,\\, \\max_{f\\in\\mathcal{F}_{\\mu,L}} \\left\\{ \\frac{\\|x_2-x_\\star\\|^2_2}{\\|x_0-x_\\star\\|^2_2} \\, : \\, x_2 \\text{ obtained from } x_{1} = x_0 - \\gamma_1 \\nabla f(x_0),\\,x_{2} = x_1 - \\gamma_2 \\nabla f(x_1) \\right\\}.$$\n",
"\n",
"\n",
- "To do this, we will follow similar steps as for vanilla GD.\n",
+ "To do this, we will follow steps similar to those used for vanilla GD.\n",
"\n",
- "For simplicity, let us again do the analysis based on specific values of the parameters."
+ "For simplicity, let us again carry out the analysis for specific parameter values."
]
},
{
@@ -725,7 +725,7 @@
"id": "e495ced0-5117-4878-a195-bbe1b8ca48c6",
"metadata": {},
"source": [
- "The following lines provide helper functions to perform grid search and to plot grids."
+ "The following lines provide helper functions for performing the grid search and plotting the results."
]
},
{
@@ -845,7 +845,7 @@
"id": "a6c6d47f-782b-49b6-91c6-f172fb7cedcd",
"metadata": {},
"source": [
- "Using the previous pieces of code, perform a grid search on the step sizes and plot the corresponding convergence rate values."
+ "Using the previous pieces of code, perform a grid search over the step sizes and plot the corresponding convergence rate values."
]
},
{
@@ -1096,7 +1096,7 @@
"id": "7acffe1e-6c59-4fd7-9ae8-59494636e238",
"metadata": {},
"source": [
- "Using the (guessed) algebraic characterization, let us try to get a closed-form for all the parameters/dual variables! (This will take a few minutes to run)"
+ "Using the (guessed) algebraic characterization, let us try to obtain closed-form expressions for all the parameters and dual variables. (This will take a few minutes to run.)"
]
},
{
@@ -1116,7 +1116,7 @@
"id": "83066b71-4d97-4852-af0f-18c319891a91",
"metadata": {},
"source": [
- "How many solutions did SymPy find? Can you use the numerical experiments to try to identify which one is relevant for us?"
+ "How many solutions did SymPy find? Can you use the numerical experiments to identify which one is relevant here?"
]
},
{
@@ -1237,7 +1237,7 @@
"id": "a1dea7a1-59de-491a-9c23-b02b28df3b65",
"metadata": {},
"source": [
- "How would you summarize your findings? Do you observe an improvement as compared to the classical optimal convergence rate of gradient descent?"
+ "How would you summarize your findings? Do you observe an improvement compared with the classical optimal convergence rate of gradient descent?"
]
},
{
@@ -1253,7 +1253,7 @@
"0\\geq f(x_j) -f(x_i) +\\langle \\nabla f(x_j);x_i-x_j\\rangle + \\tfrac{1}{2L}\\|\\nabla f(x_i)-\\nabla f(x_j)\\|^2+\\tfrac{\\mu}{2(1-\\mu/L)} \\|x_i-x_j-\\tfrac1L (\\nabla f(x_i)-\\nabla f(x_j))\\|^2.\n",
"$$\n",
"\n",
- "It seems that we must use the following nonzero values for those weights\n",
+ "It seems that we must use the following nonzero values for these weights:\n",
"\n",
"$$\n",
"\\begin{array}{c|ccc}\n",
@@ -1272,7 +1272,7 @@
"\\end{align*}\n",
"with $\\kappa=\\tfrac{\\mu}{L}$.\n",
"\n",
- "The proof consists in performing a weighted sum of inequalities:\n",
+ "The proof consists of performing a weighted sum of inequalities:\n",
"\n",
"- interpolation inequality with $i\\leftarrow\\star $ and $j\\leftarrow0$ and multiplier $\\lambda_1=\\tfrac{2 \\gamma_1 (\\kappa -1) ((2 \\gamma_2-1) \\kappa -1)}{\\kappa (\\gamma_1 (-\\kappa )+\\gamma_1+1)+1}$\n",
"\n",
@@ -1284,7 +1284,7 @@
"\n",
"- interpolation inequality with $i\\leftarrow 1$ and $j\\leftarrow0$ and multiplier $\\lambda_5=\\tfrac{2 (\\gamma_1-1) \\gamma_1 (\\kappa -1) ((2 \\gamma_2-1) \\kappa -1)}{(\\gamma_1 \\kappa +\\gamma_1-2) (\\kappa (\\gamma_1 (\\kappa -1)-1)-1)}$\n",
"\n",
- "summing up all those inequalities can be reformulated as\n",
+ "Summing all these inequalities can be reformulated as\n",
"\n",
"\\begin{equation*}\n",
"\\begin{aligned}\n",
@@ -1292,7 +1292,7 @@
"\\end{aligned}\n",
"\\end{equation*}\n",
"\n",
- "Evaluating this expression $\\gamma_1$, $\\gamma_2$ as chosen earlier, we get that the two residual terms identify to $0$.\n",
+ "Evaluating this expression at the previously chosen values of $\\gamma_1$ and $\\gamma_2$, we find that the two residual terms are equal to $0$.\n",
"\n",
"\n",
"\n",
@@ -1390,30 +1390,30 @@
"\n",
"Start as follows:\n",
"$$ x_{k+1}\\in\\ \\underset{x\\in \\mathbb{R}^d}{\\mathrm{argmin}} \\left\\{f(x):\\; x\\in x_k+\\mathrm{span}\\{\\nabla f(x_k)\\}\\right\\}.$$\n",
- "We will design an optimal stepsize for gradient descent that does not require linesearch.\n",
+ "We will design an optimal step size for gradient descent that does not require line search.\n",
"\n",
- "We proceed as follows. First, define the target convergence guarantee for gradient descent with exact linesearch:\n",
+ "We proceed as follows. First, define the target convergence guarantee for gradient descent with exact line search:\n",
"$$\\rho \\triangleq \\max_{x_0,x_1, f\\in\\mathcal{F}_{\\mu,L}}\\frac{f(x_{1})-f_\\star}{f(x_0)-f_\\star} \\text{ s.t. } \\langle \\nabla f(x_1), \\nabla f(x_0)\\rangle = 0,\\ \\langle \\nabla f(x_1), x_1-x_0\\rangle = 0.$$\n",
"\n",
"Then, a natural upper bound from a Lagrangian relaxation with $\\lambda_1,\\lambda_2\\in\\mathbb{R}$ is\n",
"\n",
"$$\\rho \\leq \\bar{\\rho}(\\lambda_1,\\lambda_2) \\triangleq \\max_{x_0, x_1, f\\in\\mathcal{F}_{\\mu,L}} \\left\\{\\frac{f(x_{1})-f_\\star}{f(x_0)-f_\\star} +\\lambda_1 \\langle \\nabla f(x_1), \\nabla f(x_0)\\rangle+\\lambda_2 \\langle \\nabla f(x_1), x_1-x_0\\rangle\\right\\}.$$\n",
"\n",
- "A similar upper bound can be obtained by instead combining the inequalities (which is less of a relaxation as compared to directly put them in the objective):\n",
+ "A similar upper bound can be obtained by combining the inequalities instead (which is a weaker relaxation than putting them directly in the objective):\n",
"\n",
"$$\\rho \\leq \\max_{x_0,x_1, f\\in\\mathcal{F}_{\\mu,L}}\\left\\{\\frac{f(x_{1})-f_\\star}{f(x_0)-f_\\star} \\text{ s.t. } \\lambda_1\\langle \\nabla f(x_1), \\nabla f(x_0)\\rangle +\\lambda_2 \\langle \\nabla f(x_1), x_1-x_0\\rangle = 0\\right\\} \\leq \\bar{\\rho}(\\lambda_1,\\lambda_2).$$\n",
"\n",
"So, for any pair $\\lambda_1,\\lambda_2\\in\\mathbb{R}$ we get:\n",
"- an upper bound $\\bar{\\rho}(\\lambda_1,\\lambda_2)$ (possibly $+\\infty$) on $\\rho$,\n",
"- all methods satisfying $\\langle \\nabla f(x_1), \\lambda_1 \\nabla f(x_0)+ \\lambda_2 (x_1-x_0)\\rangle = 0$ have convergence rate at most $\\bar{\\rho}(\\lambda_1,\\lambda_2)$.\n",
- "- In particular, gradient descent with stepsize $\\frac{\\lambda_1}{\\lambda_2}$ also benefits from the rate $\\bar{\\rho}(\\lambda_1,\\lambda_2)$.\n",
+ "- In particular, gradient descent with step size $\\frac{\\lambda_1}{\\lambda_2}$ also benefits from the rate $\\bar{\\rho}(\\lambda_1,\\lambda_2)$.\n",
"\n",
"**Bonus:** there exists a choice $\\lambda_1^\\star,\\lambda_2^\\star$ such that $\\rho=\\bar{\\rho}(\\lambda_1^\\star,\\lambda_2^\\star)$.\n",
"\n",
"#### 2.1.1. Designing an optimal gradient method\n",
"\n",
- "Assuming again we are not aware of the (worst-case) optimal stepsize tuning, the goal here is to obtain it from the perspective above.\n",
- "Again, the motivation is that it allows designing methods beyond gradient descent, though the technique can already be fully understood just by applying it on gradient descent."
+ "Assuming again that we do not know the (worst-case) optimal step-size tuning, the goal here is to recover it from the perspective above.\n",
+ "Again, the motivation is that this perspective allows us to design methods beyond gradient descent, even though the technique can already be fully understood in the gradient-descent setting."
]
},
{
@@ -1502,7 +1502,7 @@
}
],
"source": [
- "# verify that LS provides you with the appropriate stepsize strategy!\n",
+ "# verify that LS provides the appropriate step-size strategy!\n",
"L, mu = 1, .1\n",
"pepit_tau, list_of_constraints, dual_tab, dual_residual = wc_gradient_ELS(L=L,mu=mu)\n",
"\n",
@@ -1558,7 +1558,7 @@
"source": [
"### 2.3. Algorithm design for nonsmooth convex minimization\n",
"\n",
- "The idea of this section is to apply the same technique to the class of (possibly nonsmooth) convex optimization problems\n",
+ "The goal of this section is to apply the same technique to the class of (possibly nonsmooth) convex optimization problems\n",
"$$ \\min_{x\\in\\mathbb{R}^d} f(x),$$\n",
"where $f$ is convex and $M$-Lipschitz: $|f(x)-f(y)|\\leq M\\|x-y\\|_2$ for all $x,y\\in\\mathbb{R}^d$ (i.e., for all $x\\in\\mathbb{R}^d$ and $g_i\\in\\partial f(x)$ we have $\\|g_i\\|_2\\leq M$).\n",
"\n",
@@ -1566,8 +1566,8 @@
"\n",
"$$ x_{k+1} \\in \\mathrm{argmin}_x\\big\\{ f(x)\\,:\\, x\\in x_0+\\mathrm{span}\\{\\nabla f(x_0),\\nabla f(x_1),\\ldots,\\nabla f(x_k)\\}\\big\\}.$$\n",
"\n",
- "To implement this algorithm within PEPit, we use the [linesearch](https://pepit.readthedocs.io/en/latest/api/steps.html#exact-line-search-step) operation, that will essentially enforce orthogonality between consecutive search directions and gradients.\n",
- "That is, it imposes (we directly associate those constraints to some dual variables):\n",
+ "To implement this algorithm within PEPit, we use the [line-search](https://pepit.readthedocs.io/en/latest/api/steps.html#exact-line-search-step) operation, which essentially enforces orthogonality between consecutive search directions and gradients.\n",
+ "That is, it imposes the following constraints (which we directly associate with dual variables):\n",
"$$\\begin{aligned}\n",
"&\\langle \\nabla f(x_i), \\nabla f(x_j)\\rangle=0, &&\\text{for all } 0\\leq j\n",
"\n",
- "The idea for this section is to apply the same technique to the class of smooth convex optimization problems\n",
+ "The goal of this section is to apply the same technique to the class of smooth convex optimization problems\n",
"$$ \\min_{x\\in\\mathbb{R}^d} f(x),$$\n",
"\n",
- "and to verify that the resulting algorithm matches the optimized gradient method (OGM) [1,3].\n",
+ "and to verify that the resulting algorithm matches the optimized gradient method (OGM) [1, 3].\n",
"\n",
"### 3.1. Conjugate gradient method"
]
@@ -2277,7 +2277,7 @@
"id": "72c3d96b-1560-4fbd-a9b9-abddff8550b6",
"metadata": {},
"source": [
- "What we do next consists in *unrolling* the algorithm (the conjugate gradient-based and the optimized gradient method) to form their respective matrices of coefficients $\\{h_{i,j}\\}$:\n",
+ "What we do next is to *unroll* the algorithms (the conjugate-gradient-based one and the optimized gradient method) to form their respective coefficient matrices $\\{h_{i,j}\\}$:\n",
"$$\\begin{aligned}\n",
"x_1 &= x_0 - h_{1,0} \\nabla f(x_0),\\\\\n",
"x_2 &= x_1 - h_{2,0} \\nabla f(x_0) - h_{2,1} \\nabla f(x_1),\\\\\n",
@@ -2285,7 +2285,7 @@
"&\\vdots\\\\\n",
"x_N &= x_{N-1} - \\sum_{i=0}^{N-1} h_{N,i} \\nabla f(x_i),\n",
"\\end{aligned}$$\n",
- "and to compare that they match."
+ "and compare them to check that they match."
]
},
{
@@ -2314,13 +2314,13 @@
"source": [
"### 3.3. Missing details.\n",
"\n",
- "In order to *construct* those numerical algorithms, one actually generally just observe those matrices of coefficients $\\{h_{i,j}\\}$ (think of what would happen if you were actually solving the minimax optimization problem over the coefficients of the algorithm).\n",
- "In this case, one typically resorts on additional numerical experiments that aims to understood the structural properties of the algorithm:\n",
+ "In order to *construct* these numerical algorithms, one often simply observes the coefficient matrices $\\{h_{i,j}\\}$ (think of what would happen if you were actually solving the minimax optimization problem over the coefficients of the algorithm).\n",
+ "In this case, one typically resorts to additional numerical experiments aimed at understanding the structural properties of the algorithm:\n",
"- what are the key inequalities to be used in the corresponding worst-case analysis?\n",
"- Can the method be *factorized* in a nice way (that does not require storing all past gradients)?\n",
"- Can we identify a compact proof (e.g., Lyapunov-based) that would make the analysis simpler?\n",
"\n",
- "It turns out that the answers to those questions are (perhaps luckily) relatively nice in the case of the optimized gradient method.\n",
+ "It turns out that the answers to these questions are, perhaps luckily, relatively nice in the case of the optimized gradient method.\n",
"We illustrate a potential/Lyapunov-based analysis of OGM below."
]
},
@@ -2331,7 +2331,7 @@
"source": [
"### 3.4. Potential/Lyapunov proof for the optimized gradient method\n",
"\n",
- "Use PEPit to verify the following inequality (for $k\n",
"\n",
- "- Algorithm design based on nonlinear optimization, see for instance [8, 9]. Recent algorithms designed using such techniques were provided in [9, 10, 12].\n",
- "A few easy-to-interface solvers are likely arriving through CVXPY as well (e.g., [DNLP](https://github.com/cvxgrp/dnlp-examples/blob/main/nlp_examples/trimmed_log_reg.py)).\n",
+ "- Algorithm design based on nonlinear optimization; see, for instance, [8, 9]. Recent algorithms designed using such techniques include those in [9, 10, 12].\n",
+ "A few easy-to-interface solvers may also become available through CVXPY (e.g., [DNLP](https://github.com/cvxgrp/dnlp-examples/blob/main/nlp_examples/trimmed_log_reg.py)).\n",
"- Design based on convex relaxations, see, e.g., constructions in [1, 3, 11].\n",
"- Algebraic techniques for solving linear matrix inequalities (more to come!)\n",
"\n",
From 131d1cf29df835190c1933dc213818b1b8cae169 Mon Sep 17 00:00:00 2001
From: Daniel Berg Thomsen
Date: Fri, 13 Mar 2026 10:25:48 +0100
Subject: [PATCH 08/12] add OGM comparison plot
---
.../demo/PEPit_demo_algorithm_design.ipynb | 87 ++++++++++++++++++-
1 file changed, 84 insertions(+), 3 deletions(-)
diff --git a/ressources/demo/PEPit_demo_algorithm_design.ipynb b/ressources/demo/PEPit_demo_algorithm_design.ipynb
index 66552aa5..e82485b5 100644
--- a/ressources/demo/PEPit_demo_algorithm_design.ipynb
+++ b/ressources/demo/PEPit_demo_algorithm_design.ipynb
@@ -2295,8 +2295,78 @@
"metadata": {},
"outputs": [],
"source": [
- "# 3) using dual variables, recover unrolled method that achieves the same as CG\n",
- "# Unroll OGM + compare the coefficients!"
+ "import numpy as np\n",
+ "\n",
+ "def plot_ogm_step_sizes(step_sizes):\n",
+ " \"\"\"\n",
+ " Compare a user-provided lower-triangular coefficient table against the\n",
+ " unrolled OGM coefficients from Kim--Fessler (equations (6.16) and (7.1)).\n",
+ "\n",
+ " Args:\n",
+ " step_sizes (array-like): either\n",
+ " - a list of rows, where row i contains [h_{i+1,0}, ..., h_{i+1,i}], or\n",
+ " - a square lower-triangular array whose upper triangle is ignored.\n",
+ "\n",
+ " Returns:\n",
+ " np.ndarray: the OGM coefficient table as an n x n lower-triangular array.\n",
+ " \"\"\"\n",
+ " if isinstance(step_sizes, np.ndarray):\n",
+ " user_steps = np.asarray(step_sizes, dtype=float)\n",
+ " else:\n",
+ " user_steps = np.asarray(step_sizes, dtype=object)\n",
+ "\n",
+ " if user_steps.ndim == 2 and user_steps.shape[0] == user_steps.shape[1]:\n",
+ " n = user_steps.shape[0]\n",
+ " user_table = np.full((n, n), np.nan)\n",
+ " for i in range(n):\n",
+ " user_table[i, :i + 1] = user_steps[i, :i + 1]\n",
+ " else:\n",
+ " n = len(step_sizes)\n",
+ " user_table = np.full((n, n), np.nan)\n",
+ " for i, row in enumerate(step_sizes):\n",
+ " row = np.asarray(row, dtype=float).ravel()\n",
+ " if row.size != i + 1:\n",
+ " raise ValueError(\n",
+ " f\"Row {i} should contain {i + 1} coefficients, got {row.size}.\"\n",
+ " )\n",
+ " user_table[i, :i + 1] = row\n",
+ "\n",
+ " theta = [1.0]\n",
+ " for i in range(n):\n",
+ " if i < n - 1:\n",
+ " theta.append((1 + np.sqrt(1 + 4 * theta[-1] ** 2)) / 2)\n",
+ " else:\n",
+ " theta.append((1 + np.sqrt(1 + 8 * theta[-1] ** 2)) / 2)\n",
+ "\n",
+ " ogm_table = np.full((n, n), np.nan)\n",
+ " previous_row = None\n",
+ " for i in range(n):\n",
+ " row = np.zeros(i + 1)\n",
+ " if i >= 2:\n",
+ " row[:i - 1] = (theta[i] - 1) / theta[i + 1] * previous_row[:i - 1]\n",
+ " if i >= 1:\n",
+ " row[i - 1] = (theta[i] - 1) / theta[i + 1] * (previous_row[i - 1] - 1)\n",
+ " row[i] = 1 + (2 * theta[i] - 1) / theta[i + 1]\n",
+ " ogm_table[i, :i + 1] = row\n",
+ " previous_row = row\n",
+ "\n",
+ " cmap = plt.cm.viridis.copy()\n",
+ " cmap.set_bad(color=\"white\")\n",
+ " values = np.concatenate((user_table[~np.isnan(user_table)], ogm_table[~np.isnan(ogm_table)]))\n",
+ " vmin, vmax = values.min(), values.max()\n",
+ "\n",
+ " fig, axes = plt.subplots(1, 2, figsize=(12, 4), constrained_layout=True)\n",
+ " for ax, table, title in zip(axes, [user_table, ogm_table], [\"Your coefficients\", \"OGM coefficients\"]):\n",
+ " im = ax.imshow(table, origin=\"upper\", aspect=\"auto\", cmap=cmap, vmin=vmin, vmax=vmax)\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel(r\"Gradient index $j$\")\n",
+ " ax.set_ylabel(r\"Step $i+1$\")\n",
+ " ax.set_xticks(range(n))\n",
+ " ax.set_yticks(range(n))\n",
+ " ax.set_yticklabels(range(1, n + 1))\n",
+ "\n",
+ " fig.colorbar(im, ax=axes, shrink=0.85, label=r\"Coefficient $h_{i,j}$\")\n",
+ " return ogm_table"
]
},
{
@@ -2305,7 +2375,18 @@
"id": "16d01a3d-3b19-436d-a872-46ab9c189700",
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "# Put your unrolled coefficients here as a lower-triangular list of rows,\n",
+ "# for example [[h_1,0], [h_2,0, h_2,1], [h_3,0, h_3,1, h_3,2]].\n",
+ "# Then run this cell to compare them with the OGM coefficients.\n",
+ "\n",
+ "your_step_sizes = [\n",
+ " [1.0],\n",
+ " [1.0, 1.0],\n",
+ "]\n",
+ "\n",
+ "plot_ogm_step_sizes(your_step_sizes);"
+ ]
},
{
"cell_type": "markdown",
From f25498c3e02ef6a5cda1a9321aa30d3cd69bbd6b Mon Sep 17 00:00:00 2001
From: baptiste
Date: Mon, 16 Mar 2026 14:26:09 +0100
Subject: [PATCH 09/12] Fix whatsnew version
---
docs/source/whatsnew/0.5.1.rst | 7 +------
1 file changed, 1 insertion(+), 6 deletions(-)
diff --git a/docs/source/whatsnew/0.5.1.rst b/docs/source/whatsnew/0.5.1.rst
index b8c33e1f..fa2ae8f9 100644
--- a/docs/source/whatsnew/0.5.1.rst
+++ b/docs/source/whatsnew/0.5.1.rst
@@ -1,11 +1,6 @@
-What's new in PEPit 0.4.1
+What's new in PEPit 0.5.1
=========================
-New features:
--------------
-
--
-
New demo:
---------
From f13779dd96a410a0fc820f667daf1bd855800ef1 Mon Sep 17 00:00:00 2001
From: baptiste
Date: Mon, 16 Mar 2026 14:26:27 +0100
Subject: [PATCH 10/12] Minor fix on a markdown cell in the notebook
---
.../demo/PEPit_demo_algorithm_design.ipynb | 112 +-----------------
1 file changed, 4 insertions(+), 108 deletions(-)
diff --git a/ressources/demo/PEPit_demo_algorithm_design.ipynb b/ressources/demo/PEPit_demo_algorithm_design.ipynb
index e82485b5..c993c39e 100644
--- a/ressources/demo/PEPit_demo_algorithm_design.ipynb
+++ b/ressources/demo/PEPit_demo_algorithm_design.ipynb
@@ -2,7 +2,6 @@
"cells": [
{
"cell_type": "markdown",
- "id": "8f984402-f6c5-4de8-b62d-6100aa55c23a",
"metadata": {},
"source": [
"# Minicourse on performance estimation problems, part 3: algorithm design\n",
@@ -32,7 +31,6 @@
},
{
"cell_type": "markdown",
- "id": "e848e4be-38ea-4513-9a89-511326928b10",
"metadata": {
"tags": []
},
@@ -48,7 +46,6 @@
},
{
"cell_type": "markdown",
- "id": "149cf658",
"metadata": {},
"source": [
"## 1. Optimal algorithm design via naive exploration \n",
@@ -59,7 +56,6 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "a05363d6",
"metadata": {
"tags": []
},
@@ -76,7 +72,6 @@
},
{
"cell_type": "markdown",
- "id": "e7829ab0-da71-4fb3-babb-7704e3edcb52",
"metadata": {},
"source": [
"### 1.1. Base gradient descent\n",
@@ -89,7 +84,6 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "e67aa70b-864b-49b2-b52e-546776052026",
"metadata": {
"tags": []
},
@@ -137,7 +131,6 @@
},
{
"cell_type": "markdown",
- "id": "06e66fb0",
"metadata": {},
"source": [
"We now verify the rate for $(\\mu, L) = (0.1, 1)$ over a grid of step sizes."
@@ -146,7 +139,6 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "ffe8f863-c7ef-4fd4-abf2-a1827d07dfcf",
"metadata": {
"tags": []
},
@@ -194,7 +186,6 @@
},
{
"cell_type": "markdown",
- "id": "52a9b95b-c4c2-4b4f-a52a-75cf67f45c30",
"metadata": {},
"source": [
"Verify the output for a few step sizes:"
@@ -203,7 +194,6 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "6236c542-7690-4eea-865e-367bcde5ba99",
"metadata": {
"tags": []
},
@@ -226,7 +216,6 @@
},
{
"cell_type": "markdown",
- "id": "d68869af-f3db-4660-a26e-66c8b56de067",
"metadata": {},
"source": [
"Identify the pattern in the inequalities: does it make sense, and can you explain it in light of the PEP theory you know?"
@@ -235,7 +224,6 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "e3e75058-cd7b-4cfb-8e6e-5843f6bdda03",
"metadata": {
"tags": []
},
@@ -258,7 +246,6 @@
},
{
"cell_type": "markdown",
- "id": "d9632962-01e3-46b5-bbe3-3488248d91a4",
"metadata": {},
"source": [
"Are there other active constraints? (note: you can ignore the \"performance metric\" constraint for now)"
@@ -267,7 +254,6 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "b3b7ee08-48d6-424c-bedc-ff025d7f86f6",
"metadata": {
"tags": []
},
@@ -291,7 +277,6 @@
},
{
"cell_type": "markdown",
- "id": "0fa2034c-71a3-4d14-b832-8b8438b18e87",
"metadata": {},
"source": [
"Now, our goal is to compute the optimal point by observing a few properties of the corresponding PEP objects, assuming we do not already know that its parametric value is $2/(L+\\mu)$. In particular, observe the value of the residual matrix (at the best point of the grid) and compare it with its value for other step sizes. Can you explain this intuitively? Does it give you a hint on how to algebraically characterize this optimal step size?"
@@ -300,7 +285,6 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "2c665f34-ec8d-465e-b1b1-451c3667f693",
"metadata": {
"tags": []
},
@@ -327,7 +311,6 @@
},
{
"cell_type": "markdown",
- "id": "64f82c2f-1095-4aa7-b418-206522b4856c",
"metadata": {},
"source": [
"Now that you understand some algebraic properties of the optimal step size, let us try to compute it directly via symbolic computations (using SymPy)."
@@ -336,7 +319,6 @@
{
"cell_type": "code",
"execution_count": 41,
- "id": "0899cd27-fe19-4357-8567-453ba5f782dc",
"metadata": {
"tags": []
},
@@ -386,7 +368,6 @@
},
{
"cell_type": "markdown",
- "id": "136669a9-7097-47fa-8fa2-7094db30a7a6",
"metadata": {},
"source": [
"We can now proceed step by step to cancel both terms, namely the LMI/residual and the linear constraint. We show below how to do everything at once, but the step-by-step approach is pedagogically useful and helps one get used to SymPy:"
@@ -395,7 +376,6 @@
{
"cell_type": "code",
"execution_count": 42,
- "id": "6764977b-ec54-4604-8254-46abfbd113fe",
"metadata": {
"tags": []
},
@@ -423,7 +403,6 @@
{
"cell_type": "code",
"execution_count": 43,
- "id": "1793cf5b-dca7-4404-a44a-ec31383eb61c",
"metadata": {
"tags": []
},
@@ -449,7 +428,6 @@
{
"cell_type": "code",
"execution_count": 44,
- "id": "eecf9071-02cb-4224-b944-6e8e08637f82",
"metadata": {
"tags": []
},
@@ -479,7 +457,6 @@
{
"cell_type": "code",
"execution_count": 45,
- "id": "8a661352-2663-4385-9a0a-b9bbdd35a66e",
"metadata": {
"tags": []
},
@@ -510,7 +487,6 @@
{
"cell_type": "code",
"execution_count": 46,
- "id": "f255fef4-7283-45d5-ab4c-0962409986e9",
"metadata": {
"tags": []
},
@@ -541,7 +517,6 @@
{
"cell_type": "code",
"execution_count": 47,
- "id": "64690874-0a8b-4d81-b007-d1319fb8bae3",
"metadata": {
"tags": []
},
@@ -569,7 +544,6 @@
},
{
"cell_type": "markdown",
- "id": "441f3e90-f0de-4b2b-872f-d3b8c628ffcc",
"metadata": {},
"source": [
"Nice: this is very much in line with what one would expect.\n",
@@ -579,7 +553,6 @@
{
"cell_type": "code",
"execution_count": 48,
- "id": "db4a9c04-fd7e-4492-bdae-c01b1a00b7c0",
"metadata": {
"tags": []
},
@@ -627,7 +600,6 @@
{
"cell_type": "code",
"execution_count": 51,
- "id": "06dbb93f-4b44-43b0-b1c5-ad96f2c9f0e5",
"metadata": {
"tags": []
},
@@ -652,7 +624,6 @@
},
{
"cell_type": "markdown",
- "id": "477434ca-15d2-4bff-b6f6-cc82c6e402b3",
"metadata": {
"tags": []
},
@@ -662,7 +633,6 @@
},
{
"cell_type": "markdown",
- "id": "c09c5f95-d156-4454-927b-62a210679472",
"metadata": {
"tags": []
},
@@ -680,7 +650,6 @@
},
{
"cell_type": "markdown",
- "id": "196d7691-b9bd-4542-b49b-dbeb4346e5b6",
"metadata": {},
"source": [
"### 1.2. Multistep gradient descent (aka the Silver step-size schedule [4])"
@@ -688,7 +657,6 @@
},
{
"cell_type": "markdown",
- "id": "0555e948-f6e3-4d4c-a293-2a44b33b53d6",
"metadata": {},
"source": [
"Let us now consider the same optimization setup: we want to minimize a smooth strongly convex function\n",
@@ -711,7 +679,6 @@
{
"cell_type": "code",
"execution_count": 52,
- "id": "5cd994bb-bac2-446c-b9f5-7ebc4b422589",
"metadata": {
"tags": []
},
@@ -722,7 +689,6 @@
},
{
"cell_type": "markdown",
- "id": "e495ced0-5117-4878-a195-bbe1b8ca48c6",
"metadata": {},
"source": [
"The following lines provide helper functions for performing the grid search and plotting the results."
@@ -731,7 +697,6 @@
{
"cell_type": "code",
"execution_count": 53,
- "id": "9b54e7b5-36d1-4f11-9ff3-080681da9476",
"metadata": {
"tags": []
},
@@ -842,7 +807,6 @@
},
{
"cell_type": "markdown",
- "id": "a6c6d47f-782b-49b6-91c6-f172fb7cedcd",
"metadata": {},
"source": [
"Using the previous pieces of code, perform a grid search over the step sizes and plot the corresponding convergence rate values."
@@ -851,7 +815,6 @@
{
"cell_type": "code",
"execution_count": 54,
- "id": "ac9a474a-ca4f-4262-8a96-c91bc1f01d61",
"metadata": {
"tags": []
},
@@ -878,7 +841,6 @@
{
"cell_type": "code",
"execution_count": 55,
- "id": "fafa169a-12fa-4cd9-bfbd-bb68638108bf",
"metadata": {
"tags": []
},
@@ -909,7 +871,6 @@
},
{
"cell_type": "markdown",
- "id": "92c800bf-7ea6-4851-9bd2-12dbc0904503",
"metadata": {},
"source": [
"As we want to find properties of the optimal point, let us refine the grid around the target point!"
@@ -918,7 +879,6 @@
{
"cell_type": "code",
"execution_count": 56,
- "id": "fe7a2d5c-5c48-442c-8ac6-ed9150f97ad2",
"metadata": {
"tags": []
},
@@ -964,7 +924,6 @@
},
{
"cell_type": "markdown",
- "id": "8e9c60b1-6e5f-4461-afc1-91a4801017f6",
"metadata": {},
"source": [
"What algebraic property do you empirically observe at (or around) this optimal point?"
@@ -973,7 +932,6 @@
{
"cell_type": "code",
"execution_count": 57,
- "id": "630042d6-6a71-4050-97b4-dba4f2013778",
"metadata": {
"tags": []
},
@@ -997,7 +955,6 @@
},
{
"cell_type": "markdown",
- "id": "dcd18851-5c0e-44ab-968a-4813d87a31bb",
"metadata": {},
"source": [
"In order to help SymPy, we also have to identify the sparsity pattern for the dual variables. What do you observe and how would you choose it?"
@@ -1006,7 +963,6 @@
{
"cell_type": "code",
"execution_count": 58,
- "id": "541e2bdc-d657-432e-91d0-ad23e900efe4",
"metadata": {
"tags": []
},
@@ -1028,7 +984,6 @@
},
{
"cell_type": "markdown",
- "id": "5932a9dc-5a0c-4bc7-8c6a-74e16a60c067",
"metadata": {},
"source": [
"Use SymPy to try to compute the optimal step sizes."
@@ -1037,7 +992,6 @@
{
"cell_type": "code",
"execution_count": 59,
- "id": "27f4341f-354c-40cb-ba32-6afc6c947d23",
"metadata": {
"tags": []
},
@@ -1093,7 +1047,6 @@
},
{
"cell_type": "markdown",
- "id": "7acffe1e-6c59-4fd7-9ae8-59494636e238",
"metadata": {},
"source": [
"Using the (guessed) algebraic characterization, let us try to obtain closed-form expressions for all the parameters and dual variables. (This will take a few minutes to run.)"
@@ -1102,7 +1055,6 @@
{
"cell_type": "code",
"execution_count": 60,
- "id": "1ccf4acc-6be1-4826-acb7-0f1af295d4e4",
"metadata": {
"tags": []
},
@@ -1113,7 +1065,6 @@
},
{
"cell_type": "markdown",
- "id": "83066b71-4d97-4852-af0f-18c319891a91",
"metadata": {},
"source": [
"How many solutions did SymPy find? Can you use the numerical experiments to identify which one is relevant here?"
@@ -1122,7 +1073,6 @@
{
"cell_type": "code",
"execution_count": 61,
- "id": "20ff734f-c725-4096-91a2-79a26381586c",
"metadata": {
"tags": []
},
@@ -1147,7 +1097,6 @@
{
"cell_type": "code",
"execution_count": 62,
- "id": "b69ff2c0-fb31-4fcf-8c32-8613ab5460ed",
"metadata": {
"tags": []
},
@@ -1176,7 +1125,6 @@
},
{
"cell_type": "markdown",
- "id": "5b0ccc27-1470-43a9-82d3-e9b3c21e95f7",
"metadata": {},
"source": [
"What are the corresponding expressions for the step sizes?"
@@ -1185,7 +1133,6 @@
{
"cell_type": "code",
"execution_count": 63,
- "id": "a9bb0591-7332-4bcf-b4c2-d157d56f1e3b",
"metadata": {
"tags": []
},
@@ -1209,7 +1156,6 @@
{
"cell_type": "code",
"execution_count": 64,
- "id": "8312e354-0c8e-4144-bb95-70882cfaaaad",
"metadata": {
"tags": []
},
@@ -1234,7 +1180,6 @@
},
{
"cell_type": "markdown",
- "id": "a1dea7a1-59de-491a-9c23-b02b28df3b65",
"metadata": {},
"source": [
"How would you summarize your findings? Do you observe an improvement compared with the classical optimal convergence rate of gradient descent?"
@@ -1242,7 +1187,6 @@
},
{
"cell_type": "markdown",
- "id": "99e34d24-040d-42a0-8ae8-7bf16c9513ea",
"metadata": {},
"source": [
"#### Summary on Silver steps ($n=2$)\n",
@@ -1301,7 +1245,6 @@
},
{
"cell_type": "markdown",
- "id": "6146d68e",
"metadata": {},
"source": [
"### Try this pattern on a simple quadratic problem and compare it to vanilla gradient descent\n"
@@ -1310,7 +1253,6 @@
{
"cell_type": "code",
"execution_count": 61,
- "id": "66b8486c-a20a-4741-8762-b2d8726a1cf1",
"metadata": {
"tags": []
},
@@ -1376,7 +1318,6 @@
},
{
"cell_type": "markdown",
- "id": "bba66f2f-bc44-485f-a4cb-f87b404ec342",
"metadata": {
"tags": []
},
@@ -1419,7 +1360,6 @@
{
"cell_type": "code",
"execution_count": 64,
- "id": "64f71cdd-df9c-4a88-afcc-754320572e78",
"metadata": {
"tags": []
},
@@ -1447,7 +1387,7 @@
" g0, f0 = func.oracle(x0)\n",
"\n",
" # Set the initial constraint on function accuracy\n",
- " problem.set_initial_condition( f0 - fs <= 1)\n",
+ " problem.set_initial_condition(f0 - fs <= 1)\n",
"\n",
" # Run n steps of the Conjugate Gradient method\n",
" x_new = x0\n",
@@ -1469,7 +1409,6 @@
{
"cell_type": "code",
"execution_count": 65,
- "id": "1f8e03b2-f119-488d-9ae4-005c5ebe881d",
"metadata": {
"tags": []
},
@@ -1516,7 +1455,6 @@
{
"cell_type": "code",
"execution_count": 66,
- "id": "c4b67234-1903-4f94-9bcc-9ccd079e0db8",
"metadata": {
"tags": []
},
@@ -1540,7 +1478,6 @@
{
"cell_type": "code",
"execution_count": 63,
- "id": "7b96284a-ce68-4303-8fc0-2b785d96cfc0",
"metadata": {
"tags": []
},
@@ -1551,7 +1488,6 @@
},
{
"cell_type": "markdown",
- "id": "77f33c96-151d-45a0-85d2-5ea477d70c9b",
"metadata": {
"tags": []
},
@@ -1598,7 +1534,6 @@
{
"cell_type": "code",
"execution_count": 47,
- "id": "3311515b-3835-4f96-8422-1c41cf3934d5",
"metadata": {
"tags": []
},
@@ -1658,7 +1593,6 @@
{
"cell_type": "code",
"execution_count": 48,
- "id": "54185951-4392-4bf1-b38b-e5b54a7f17f2",
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1754,7 +1688,6 @@
},
{
"cell_type": "markdown",
- "id": "26a2727d-7b3c-425d-a16b-32349f1ab036",
"metadata": {
"tags": []
},
@@ -1767,7 +1700,6 @@
{
"cell_type": "code",
"execution_count": 49,
- "id": "3c0cca81-af25-40fc-a78a-8fae97b2fd3d",
"metadata": {
"tags": []
},
@@ -1796,7 +1728,6 @@
{
"cell_type": "code",
"execution_count": 50,
- "id": "7a7757b3-ae2a-4fd8-87a0-a3b1fdc49543",
"metadata": {
"tags": []
},
@@ -1807,7 +1738,6 @@
},
{
"cell_type": "markdown",
- "id": "3f37f080-5ede-4a73-bb0a-5395f22f94ba",
"metadata": {},
"source": [
"#### 2.3.3. General $n$\n",
@@ -1818,7 +1748,6 @@
{
"cell_type": "code",
"execution_count": 51,
- "id": "a29e948b-ca32-4eb8-9a2c-1709a7fb3b49",
"metadata": {
"tags": []
},
@@ -1864,7 +1793,6 @@
},
{
"cell_type": "markdown",
- "id": "c26ae2db-7501-4d99-a34b-008feb3b6503",
"metadata": {
"tags": []
},
@@ -1875,7 +1803,6 @@
{
"cell_type": "code",
"execution_count": 52,
- "id": "b48209f2-8298-49b8-b28d-a9dddd32c44e",
"metadata": {
"tags": []
},
@@ -1887,7 +1814,6 @@
{
"cell_type": "code",
"execution_count": 53,
- "id": "1db18261-c89e-4471-9108-65b3fd20ef08",
"metadata": {
"tags": []
},
@@ -1924,13 +1850,12 @@
},
{
"cell_type": "markdown",
- "id": "e8e75353-ea66-4901-a0ae-5bd9a6e8fed6",
"metadata": {},
"source": [
"It therefore looks like we can collect the coefficients and obtain clean constraints:\n",
"\n",
"Denote by $g_i\\in\\partial f(x_i)$; then all methods satisfying\n",
- "$$ 0=\\langle g_i,x_i-\\left[\\frac{i}{i+1}x_{i-1}+\\frac{1}{i+1}x_0-\\frac{1}{i+1} \\frac{R}{M\\sqrt{n+1}}\\sum_{j=0}^{i-1}g_j \\right]\\rangle$$ \n",
+ "$$ 0=\\left< g_i,x_i-\\left[\\frac{i}{i+1}x_{i-1}+\\frac{1}{i+1}x_0-\\frac{1}{i+1} \\frac{R}{M\\sqrt{n+1}}\\sum_{j=0}^{i-1}g_j \\right]\\right> $$ \n",
"benefit from the worst-case guarantee\n",
"$$ f(x_n)-f_\\star \\leq \\frac{M R}{\\sqrt{n+1}}.$$ \n",
"Below is a PEPit implementation of the resulting algorithm (sometimes referred to as a \"quasi-monotone subgradient method\" [7], since the worst-case guarantee is monotone)."
@@ -1939,7 +1864,6 @@
{
"cell_type": "code",
"execution_count": 54,
- "id": "645ae713-b1bb-420a-96cd-3783bb5f5f72",
"metadata": {
"tags": []
},
@@ -1985,7 +1909,6 @@
{
"cell_type": "code",
"execution_count": 56,
- "id": "30e767ad-d457-4f3b-9c67-5a328e304cdc",
"metadata": {
"tags": []
},
@@ -2013,7 +1936,6 @@
},
{
"cell_type": "markdown",
- "id": "4830b9d7-2513-480d-959f-2006a46bac4c",
"metadata": {},
"source": [
"## 3. Back to smooth convex minimization: the optimized gradient method \n",
@@ -2029,7 +1951,6 @@
{
"cell_type": "code",
"execution_count": 57,
- "id": "c0f0dcf6-1028-49df-8dce-8b8b5997e3cc",
"metadata": {
"tags": []
},
@@ -2089,7 +2010,6 @@
},
{
"cell_type": "markdown",
- "id": "952cd2a3-7b9a-46f2-9492-a92e2657fb7a",
"metadata": {},
"source": [
"Experiment with small values of the time horizon (e.g., $n=1,2$) and try to come up with the coefficients of the method."
@@ -2098,7 +2018,6 @@
{
"cell_type": "code",
"execution_count": 58,
- "id": "44a3cf6d-7797-4b70-9559-ea39d6bf760f",
"metadata": {
"tags": []
},
@@ -2145,7 +2064,6 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "2f134460-2c70-4902-b91c-df1828a93c66",
"metadata": {
"tags": []
},
@@ -2186,7 +2104,6 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "0098f01f-0e11-4adc-825b-cafcea92c6a7",
"metadata": {},
"outputs": [],
"source": [
@@ -2195,7 +2112,6 @@
},
{
"cell_type": "markdown",
- "id": "72b0a13b-4624-4998-b046-f5533a497f39",
"metadata": {},
"source": [
"### 3.2. The optimized gradient method (OGM)\n",
@@ -2206,7 +2122,6 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "c2687690-806d-4208-8486-e2fd14022776",
"metadata": {},
"outputs": [],
"source": [
@@ -2265,7 +2180,6 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "137c9071-4808-41aa-a165-d9bd266bbb84",
"metadata": {
"tags": []
},
@@ -2274,7 +2188,6 @@
},
{
"cell_type": "markdown",
- "id": "72c3d96b-1560-4fbd-a9b9-abddff8550b6",
"metadata": {},
"source": [
"What we do next is to *unroll* the algorithms (the conjugate-gradient-based one and the optimized gradient method) to form their respective coefficient matrices $\\{h_{i,j}\\}$:\n",
@@ -2291,7 +2204,6 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "25526a2b-97ca-45d7-9758-1a84bc3a027f",
"metadata": {},
"outputs": [],
"source": [
@@ -2372,7 +2284,6 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "16d01a3d-3b19-436d-a872-46ab9c189700",
"metadata": {},
"outputs": [],
"source": [
@@ -2390,7 +2301,6 @@
},
{
"cell_type": "markdown",
- "id": "b9fe133b-b8da-4e2f-9cfa-1923a160ca8a",
"metadata": {},
"source": [
"### 3.3. Missing details.\n",
@@ -2407,7 +2317,6 @@
},
{
"cell_type": "markdown",
- "id": "40aee914-3fee-4cd6-9222-7f85085e7560",
"metadata": {},
"source": [
"### 3.4. Potential/Lyapunov proof for the optimized gradient method\n",
@@ -2423,7 +2332,6 @@
{
"cell_type": "code",
"execution_count": 29,
- "id": "62a57400-df52-4d12-969a-c75c855a0838",
"metadata": {
"tags": []
},
@@ -2479,7 +2387,6 @@
{
"cell_type": "code",
"execution_count": 31,
- "id": "9347b036-cca9-476d-b92e-5cadf1250b6d",
"metadata": {
"tags": []
},
@@ -2531,7 +2438,6 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "e513859b-3da9-4298-ad4b-d9848486e8b4",
"metadata": {
"tags": []
},
@@ -2544,7 +2450,6 @@
},
{
"cell_type": "markdown",
- "id": "c3a5f27e-e2f0-4bd8-be52-6673b8db75df",
"metadata": {},
"source": [
"**Summary.** The inequality can be obtained from the same potential used for OGM, itself derived from additional inequalities. That is, we first perform a weighted sum of the following inequalities (this is actually for conjugate gradients, but it also works for OGM).\n",
@@ -2669,7 +2574,6 @@
},
{
"cell_type": "markdown",
- "id": "8b59b702-acce-45e2-98be-ad9d7caf14ed",
"metadata": {},
"source": [
"### 4. Going further \n",
@@ -2707,19 +2611,11 @@
"\n",
"[12] U. Jang, S. Das Gupta, and E. K. Ryu, *Computer-Assisted Design of Accelerated Composite Optimization Methods: OptISTA*, Mathematical Programming, 2025. [arXiv:2305.15704](https://arxiv.org/abs/2305.15704)"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "064a6462-3eb7-49f3-a133-9aa1b64ff047",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -2733,7 +2629,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.7.9"
}
},
"nbformat": 4,
From bdb0865faf1ec79b796aa2cc509660df78e20633 Mon Sep 17 00:00:00 2001
From: baptiste
Date: Mon, 16 Mar 2026 14:33:10 +0100
Subject: [PATCH 11/12] Add notebook to the tutorial part of the documentation
---
docs/source/tutorials.rst | 3 ++-
1 file changed, 2 insertions(+), 1 deletion(-)
diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst
index ddf1ae17..d5a4c20f 100644
--- a/docs/source/tutorials.rst
+++ b/docs/source/tutorials.rst
@@ -6,5 +6,6 @@ Tutorials
:caption: Contents:
Finding worst-case guarantees
- Extracting a proof
Extracting a worst-case example
+ Extracting a proof
+ Designing an algorithm
From f3a3a2b9013eb46b68472d85fa80e759594745fe Mon Sep 17 00:00:00 2001
From: baptiste
Date: Mon, 16 Mar 2026 14:53:57 +0100
Subject: [PATCH 12/12] Add the outputs of all the cells in the notebook
---
.../demo/PEPit_demo_algorithm_design.ipynb | 353 +++++++++---------
1 file changed, 181 insertions(+), 172 deletions(-)
diff --git a/ressources/demo/PEPit_demo_algorithm_design.ipynb b/ressources/demo/PEPit_demo_algorithm_design.ipynb
index c993c39e..bd81d9c0 100644
--- a/ressources/demo/PEPit_demo_algorithm_design.ipynb
+++ b/ressources/demo/PEPit_demo_algorithm_design.ipynb
@@ -55,7 +55,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 1,
"metadata": {
"tags": []
},
@@ -152,12 +152,14 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
@@ -193,7 +195,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {
"tags": []
},
@@ -201,10 +203,10 @@
{
"data": {
"text/plain": [
- "[0.8100000006611664]"
+ "[0.8100001805827125]"
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -223,7 +225,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 5,
"metadata": {
"tags": []
},
@@ -232,9 +234,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "IC_Function_0 xs x0\n",
- "xs 0.00000 1.80001\n",
- "x0 1.80001 0.00000\n"
+ "IC_Function_0 xs x0\n",
+ "xs 0.0 1.8\n",
+ "x0 1.8 0.0\n"
]
}
],
@@ -253,7 +255,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 6,
"metadata": {
"tags": []
},
@@ -262,10 +264,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Constraint \"Performance metric 1\" value: 1.0\n",
- "Constraint \"Initial condition\" value: 0.8100000006611664\n",
- "Constraint \"IC_Function_0_smoothness_strong_convexity(xs, x0)\" value: 1.800009898700675\n",
- "Constraint \"IC_Function_0_smoothness_strong_convexity(x0, xs)\" value: 1.800009898700675\n"
+ "Constraint \"Performance metric 1\" value: 0.9999999999866084\n",
+ "Constraint \"Initial condition\" value: 0.8100001805827125\n",
+ "Constraint \"IC_Function_0_smoothness_strong_convexity(xs, x0)\" value: 1.7999998617137993\n",
+ "Constraint \"IC_Function_0_smoothness_strong_convexity(x0, xs)\" value: 1.7999998617138078\n"
]
}
],
@@ -284,7 +286,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 7,
"metadata": {
"tags": []
},
@@ -293,9 +295,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[[ 5.31817505e-05 -5.31798584e-05 5.31383717e-04]\n",
- " [-5.31798584e-05 5.31817505e-05 -5.31383717e-04]\n",
- " [ 5.31383717e-04 -5.31383717e-04 5.31014805e-03]]\n"
+ "[[ 5.30968577e-05 -5.30968577e-05 5.30953574e-04]\n",
+ " [-5.30968577e-05 5.30968577e-05 -5.30953574e-04]\n",
+ " [ 5.30953574e-04 -5.30953574e-04 5.30938571e-03]]\n"
]
}
],
@@ -318,7 +320,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 8,
"metadata": {
"tags": []
},
@@ -375,7 +377,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 9,
"metadata": {
"tags": []
},
@@ -391,7 +393,7 @@
"[ (-L*lambda_1/2 + gamma*(L - mu) - lambda_2*mu/2)/(L - mu), (2*gamma**2*(-L + mu) + lambda_1 + lambda_2)/(2*(L - mu))]])"
]
},
- "execution_count": 42,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -402,7 +404,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 10,
"metadata": {
"tags": []
},
@@ -416,7 +418,7 @@
"lambda_1 - lambda_2"
]
},
- "execution_count": 43,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -427,7 +429,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 11,
"metadata": {
"tags": []
},
@@ -443,7 +445,7 @@
"[(-L*lambda_1/2 + gamma*(L - mu) - lambda_1*mu/2)/(L - mu), (2*gamma**2*(-L + mu) + 2*lambda_1)/(2*(L - mu))]])"
]
},
- "execution_count": 44,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -456,7 +458,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 12,
"metadata": {
"tags": []
},
@@ -472,7 +474,7 @@
"[(-L*lambda_1/2 + gamma*(L - mu) - lambda_1*mu/2)/(L - mu), (gamma**2*(-L + mu) + lambda_1)/(L - mu)]])"
]
},
- "execution_count": 45,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -486,7 +488,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 13,
"metadata": {
"tags": []
},
@@ -502,7 +504,7 @@
"[0, gamma*(-gamma*(L + mu) + 2)/(L + mu)]])"
]
},
- "execution_count": 46,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -516,7 +518,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 14,
"metadata": {
"tags": []
},
@@ -552,7 +554,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 15,
"metadata": {
"tags": []
},
@@ -599,7 +601,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 16,
"metadata": {
"tags": []
},
@@ -613,7 +615,7 @@
" 2/(L + mu))"
]
},
- "execution_count": 51,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -678,7 +680,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 17,
"metadata": {
"tags": []
},
@@ -696,7 +698,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 18,
"metadata": {
"tags": []
},
@@ -814,7 +816,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 19,
"metadata": {
"tags": []
},
@@ -840,19 +842,21 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 20,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
},
{
@@ -878,7 +882,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 21,
"metadata": {
"tags": []
},
@@ -892,20 +896,22 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Best grid index: (i,j) = (2, 6)\n",
- "gamma1 = 1.3837, gamma2 = 2.6502\n",
+ "Best grid index: (i,j) = (1, 7)\n",
+ "gamma1 = 1.3838, gamma2 = 2.6501\n",
"tau = 4.0105e-01\n"
]
}
@@ -931,7 +937,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 22,
"metadata": {
"tags": []
},
@@ -940,10 +946,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "[[ 6.30881589e-07 -6.29076105e-07 3.14310383e-06 3.50857270e-06]\n",
- " [-6.29076105e-07 6.30880714e-07 -3.14310387e-06 -3.50857831e-06]\n",
- " [ 3.14310383e-06 -3.14310387e-06 1.03059809e-04 -8.41503948e-05]\n",
- " [ 3.50857270e-06 -3.50857831e-06 -8.41503948e-05 1.38271517e-04]]\n"
+ "[[ 5.83099974e-06 -5.83099974e-06 2.20037443e-05 4.21374901e-05]\n",
+ " [-5.83099974e-06 5.83099974e-06 -2.20037443e-05 -4.21374901e-05]\n",
+ " [ 2.20037443e-05 -2.20037443e-05 8.30328905e-05 1.59009192e-04]\n",
+ " [ 4.21374901e-05 -4.21374901e-05 1.59009192e-04 3.04504914e-04]]\n"
]
}
],
@@ -962,7 +968,7 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 23,
"metadata": {
"tags": []
},
@@ -972,9 +978,9 @@
"output_type": "stream",
"text": [
"IC_Function_0 xs x0 x1\n",
- "xs 0.000000 1.159250 4.231348\n",
- "x0 0.000020 0.000000 2.090086\n",
- "x1 5.390578 0.930856 0.000000\n"
+ "xs 0.000000 1.159393 4.231237\n",
+ "x0 0.000206 0.000000 2.089818\n",
+ "x1 5.390423 0.930632 0.000000\n"
]
}
],
@@ -991,7 +997,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 24,
"metadata": {
"tags": []
},
@@ -1054,7 +1060,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 25,
"metadata": {
"tags": []
},
@@ -1072,7 +1078,7 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 26,
"metadata": {
"tags": []
},
@@ -1096,7 +1102,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 27,
"metadata": {
"tags": []
},
@@ -1114,7 +1120,7 @@
" 2.65027877285182]"
]
},
- "execution_count": 62,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
@@ -1132,7 +1138,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 28,
"metadata": {
"tags": []
},
@@ -1155,7 +1161,7 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 29,
"metadata": {
"tags": []
},
@@ -1169,7 +1175,7 @@
"(-mu + sqrt(2*L**2 - 2*L*mu + mu**2))/(L*(L - mu))"
]
},
- "execution_count": 64,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
@@ -1252,19 +1258,21 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 30,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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\n",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
@@ -1359,7 +1367,7 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 31,
"metadata": {
"tags": []
},
@@ -1408,7 +1416,7 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": 32,
"metadata": {
"tags": []
},
@@ -1417,25 +1425,25 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Constraint \"Performance metric 1\" value: 1.0\n",
- "Constraint \"Initial condition\" value: 0.6694213971784003\n",
- "Constraint \"IC_f_smoothness_strong_convexity(xs, x0)\" value: 0.1487883274482235\n",
- "Constraint \"IC_f_smoothness_strong_convexity(xs, x1)\" value: 0.18190268078194294\n",
- "Constraint \"IC_f_smoothness_strong_convexity(x0, xs)\" value: 5.7321746190466013e-05\n",
- "Constraint \"IC_f_smoothness_strong_convexity(x0, x1)\" value: 0.8182519833717163\n",
- "Constraint \"IC_f_smoothness_strong_convexity(x1, xs)\" value: 5.509838576432942e-05\n",
- "Constraint \"IC_f_smoothness_strong_convexity(x1, x0)\" value: 9.958049103411866e-05\n",
- "Constraint \"exact_linesearch(f)_to_x1_from_x0\" value: 1.0001315097226635\n",
- "Constraint \"exact_linesearch(f)_to_x1_from_x0_in_direction_grad(x0)\" value: 1.818420729117687\n"
+ "Constraint \"Performance metric 1\" value: 1.0000000000005818\n",
+ "Constraint \"Initial condition\" value: 0.6694218823210809\n",
+ "Constraint \"IC_f_smoothness_strong_convexity(xs, x0)\" value: 0.14876101613030243\n",
+ "Constraint \"IC_f_smoothness_strong_convexity(xs, x1)\" value: 0.18181918706640812\n",
+ "Constraint \"IC_f_smoothness_strong_convexity(x0, xs)\" value: 1.094540561619177e-06\n",
+ "Constraint \"IC_f_smoothness_strong_convexity(x0, x1)\" value: 0.8181812237581545\n",
+ "Constraint \"IC_f_smoothness_strong_convexity(x1, xs)\" value: 0.0\n",
+ "Constraint \"IC_f_smoothness_strong_convexity(x1, x0)\" value: 0.0\n",
+ "Constraint \"exact_linesearch(f)_to_x1_from_x0\" value: 0.9999991045753641\n",
+ "Constraint \"exact_linesearch(f)_to_x1_from_x0_in_direction_grad(x0)\" value: 1.8181806454530542\n"
]
},
{
"data": {
"text/plain": [
- "0.6694213971784003"
+ "0.6694218823210809"
]
},
- "execution_count": 65,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -1454,7 +1462,7 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 33,
"metadata": {
"tags": []
},
@@ -1462,10 +1470,10 @@
{
"data": {
"text/plain": [
- "1.8181816205570156"
+ "1.8181822734982545"
]
},
- "execution_count": 66,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -1477,7 +1485,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 34,
"metadata": {
"tags": []
},
@@ -1533,7 +1541,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 35,
"metadata": {
"tags": []
},
@@ -1592,7 +1600,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 36,
"metadata": {
"jupyter": {
"source_hidden": true
@@ -1699,7 +1707,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 37,
"metadata": {
"tags": []
},
@@ -1707,10 +1715,10 @@
{
"data": {
"text/plain": [
- "[0.5773502439817165, 0.5773502691896258]"
+ "[0.5773502841545819, 0.5773502691896258]"
]
},
- "execution_count": 49,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
@@ -1727,7 +1735,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 38,
"metadata": {
"tags": []
},
@@ -1747,7 +1755,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 39,
"metadata": {
"tags": []
},
@@ -1757,16 +1765,16 @@
"output_type": "stream",
"text": [
"gamma[i,j] table (dual of == 0):\n",
- "[[ 0. 0. 0. 0. 0. ]\n",
- " [ 0. 0.4 0. 0. 0. ]\n",
- " [ 0. -0.4 0.6001 0. 0. ]\n",
- " [ 0. 0. -0.6001 0.8 0. ]\n",
- " [ 0. 0. 0. -0.8 1. ]]\n",
+ "[[ 0. 0. 0. 0. 0. ]\n",
+ " [ 0. 0.4 0. 0. 0. ]\n",
+ " [ 0. -0.4 0.6 0. 0. ]\n",
+ " [ 0. 0. -0.6 0.8 0. ]\n",
+ " [ 0. 0. 0. -0.8 1. ]]\n",
"\n",
"beta[i,j] table (dual of == 0):\n",
"[[0. 0. 0. 0. 0. ]\n",
- " [0.0895 0. 0. 0. 0. ]\n",
- " [0.0895 0.0894 0. 0. 0. ]\n",
+ " [0.0894 0. 0. 0. 0. ]\n",
+ " [0.0894 0.0894 0. 0. 0. ]\n",
" [0.0894 0.0894 0.0894 0. 0. ]\n",
" [0.0894 0.0894 0.0894 0.0894 0. ]]\n"
]
@@ -1802,7 +1810,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 40,
"metadata": {
"tags": []
},
@@ -1813,7 +1821,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 41,
"metadata": {
"tags": []
},
@@ -1863,7 +1871,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 42,
"metadata": {
"tags": []
},
@@ -1908,7 +1916,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 43,
"metadata": {
"tags": []
},
@@ -1916,10 +1924,10 @@
{
"data": {
"text/plain": [
- "[0.30151131999862985, 0.3015113073952192]"
+ "[0.30150888838186735, 0.30151276363482477]"
]
},
- "execution_count": 56,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
@@ -1950,7 +1958,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 44,
"metadata": {
"tags": []
},
@@ -2017,7 +2025,7 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 45,
"metadata": {
"tags": []
},
@@ -2038,19 +2046,16 @@
"(PEPit) Setting up the problem: size of the Gram matrix: 7x7\n",
"(PEPit) Compiling SDP\n",
"(PEPit) Calling SDP solver\n",
- "(PEPit) Solver status: optimal (wrapper:cvxpy, solver: MOSEK); optimal value: 0.06189419648705603\n",
- "\u001b[96m(PEPit) Postprocessing: solver's output is not entirely feasible (smallest eigenvalue of the Gram matrix is: -4.09e-09 < 0).\n",
- " Small deviation from 0 may simply be due to numerical error. Big ones should be deeply investigated.\n",
- " In any case, from now the provided values of parameters are based on the projection of the Gram matrix onto the cone of symmetric semi-definite matrix.\u001b[0m\n",
+ "(PEPit) Solver status: optimal (wrapper:cvxpy, solver: SCS); optimal value: 0.06189419366127077\n",
"(PEPit) Primal feasibility check:\n",
- "\t\tThe solver found a Gram matrix that is positive semi-definite up to an error of 4.094038165079508e-09\n",
- "\t\tAll the primal scalar constraints are verified up to an error of 7.438133547022635e-09\n",
+ "\t\tThe solver found a Gram matrix that is positive semi-definite\n",
+ "\t\tAll the primal scalar constraints are verified up to an error of 8.378963981675591e-08\n",
"(PEPit) Dual feasibility check:\n",
- "\t\tThe solver found a residual matrix that is positive semi-definite\n",
+ "\t\tThe solver found a residual matrix that is positive semi-definite up to an error of 9.336887945304327e-16\n",
"\t\tAll the dual scalar values associated with inequality constraints are nonnegative\n",
- "(PEPit) The worst-case guarantee proof is perfectly reconstituted up to an error of 8.571101560090838e-08\n",
- "(PEPit) Final upper bound (dual): 0.06189420236651673 and lower bound (primal example): 0.06189419648705603 \n",
- "(PEPit) Duality gap: absolute: 5.879460696078809e-09 and relative: 9.49921160590296e-08\n"
+ "(PEPit) The worst-case guarantee proof is perfectly reconstituted up to an error of 2.3806796613766964e-07\n",
+ "(PEPit) Final upper bound (dual): 0.061894195562268266 and lower bound (primal example): 0.06189419366127077 \n",
+ "(PEPit) Duality gap: absolute: 1.9009974983053013e-09 and relative: 3.0713664495072307e-08\n"
]
}
],
@@ -2063,7 +2068,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 46,
"metadata": {
"tags": []
},
@@ -2072,26 +2077,26 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Constraint \"Performance metric 1\" value: 1.0\n",
- "Constraint \"Initial condition\" value: 0.06189420236651673\n",
- "Constraint \"IC_f_smoothness_convexity(xs, x0)\" value: 0.24757675521442954\n",
- "Constraint \"IC_f_smoothness_convexity(xs, x1)\" value: 0.40058764497076227\n",
- "Constraint \"IC_f_smoothness_convexity(xs, x2)\" value: 0.3518357706846017\n",
- "Constraint \"IC_f_smoothness_convexity(x0, xs)\" value: 3.130885866045864e-08\n",
- "Constraint \"IC_f_smoothness_convexity(x0, x1)\" value: 0.2475767074961847\n",
- "Constraint \"IC_f_smoothness_convexity(x0, x2)\" value: 6.098125308138602e-08\n",
- "Constraint \"IC_f_smoothness_convexity(x1, xs)\" value: 5.575532891566776e-08\n",
- "Constraint \"IC_f_smoothness_convexity(x1, x0)\" value: 2.0736230625164014e-08\n",
- "Constraint \"IC_f_smoothness_convexity(x1, x2)\" value: 0.6481643204466593\n",
- "Constraint \"IC_f_smoothness_convexity(x2, xs)\" value: 9.604987080243874e-08\n",
- "Constraint \"IC_f_smoothness_convexity(x2, x0)\" value: 2.3835614747188193e-08\n",
- "Constraint \"IC_f_smoothness_convexity(x2, x1)\" value: 4.4471209779381225e-08\n",
- "Constraint \"exact_linesearch(f)_on_x0\" value: 0.6481643503743536\n",
- "Constraint \"exact_linesearch(f)_on_x0_in_direction_grad(x0)\" value: 1.0487518733509804\n",
- "Constraint \"exact_linesearch(f)_on_x1\" value: 1.0000001430664525\n",
- "Constraint \"exact_linesearch(f)_on_x1_in_direction_grad(x0)\" value: 0.7036713748191955\n",
- "Constraint \"exact_linesearch(f)_on_x1_in_direction_grad(x1)\" value: 1.786728618380482\n",
- "Constraint \"exact_linesearch(f)_on_x1_in_direction_x0-x1\" value: -0.3518358027526827\n"
+ "Constraint \"Performance metric 1\" value: 1.000000000000051\n",
+ "Constraint \"Initial condition\" value: 0.061894195562268266\n",
+ "Constraint \"IC_f_smoothness_convexity(xs, x0)\" value: 0.24757674917197692\n",
+ "Constraint \"IC_f_smoothness_convexity(xs, x1)\" value: 0.40058761240389895\n",
+ "Constraint \"IC_f_smoothness_convexity(xs, x2)\" value: 0.35183569826399763\n",
+ "Constraint \"IC_f_smoothness_convexity(x0, xs)\" value: 0.0\n",
+ "Constraint \"IC_f_smoothness_convexity(x0, x1)\" value: 0.2475767204719302\n",
+ "Constraint \"IC_f_smoothness_convexity(x0, x2)\" value: 0.0\n",
+ "Constraint \"IC_f_smoothness_convexity(x1, xs)\" value: 0.0\n",
+ "Constraint \"IC_f_smoothness_convexity(x1, x0)\" value: 0.0\n",
+ "Constraint \"IC_f_smoothness_convexity(x1, x2)\" value: 0.6481642905905665\n",
+ "Constraint \"IC_f_smoothness_convexity(x2, xs)\" value: 0.0\n",
+ "Constraint \"IC_f_smoothness_convexity(x2, x0)\" value: 0.0\n",
+ "Constraint \"IC_f_smoothness_convexity(x2, x1)\" value: 0.0\n",
+ "Constraint \"exact_linesearch(f)_to_new_x_from_x0\" value: 0.6481643368849084\n",
+ "Constraint \"exact_linesearch(f)_to_new_x_from_x0_in_direction_grad(x0)\" value: 1.048751940770472\n",
+ "Constraint \"exact_linesearch(f)_to_new_x_from_x1\" value: 1.0000000014726265\n",
+ "Constraint \"exact_linesearch(f)_to_new_x_from_x1_in_direction_grad(x0)\" value: 0.7036714565770738\n",
+ "Constraint \"exact_linesearch(f)_to_new_x_from_x1_in_direction_grad(x1)\" value: 1.7867285711947292\n",
+ "Constraint \"exact_linesearch(f)_to_new_x_from_x1_in_direction_x0-x1\" value: -0.35183573177467986\n"
]
}
],
@@ -2103,7 +2108,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
@@ -2121,7 +2126,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
@@ -2177,15 +2182,6 @@
" return pepit_tau, theoretical_tau"
]
},
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"metadata": {},
@@ -2203,7 +2199,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
@@ -2283,9 +2279,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 50,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "'ListedColormap' object has no attribute 'copy'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m ]\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplot_ogm_step_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myour_step_sizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36mplot_ogm_step_sizes\u001b[0;34m(step_sizes)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mprevious_row\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviridis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 57\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_bad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"white\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_table\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muser_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mogm_table\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mogm_table\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'ListedColormap' object has no attribute 'copy'"
+ ]
+ }
+ ],
"source": [
"# Put your unrolled coefficients here as a lower-triangular list of rows,\n",
"# for example [[h_1,0], [h_2,0, h_2,1], [h_3,0, h_3,1, h_3,2]].\n",
@@ -2331,7 +2340,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 51,
"metadata": {
"tags": []
},
@@ -2386,7 +2395,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 52,
"metadata": {
"tags": []
},
@@ -2405,24 +2414,24 @@
"(PEPit) Setting up the problem: size of the Gram matrix: 5x5\n",
"(PEPit) Compiling SDP\n",
"(PEPit) Calling SDP solver\n",
- "(PEPit) Solver status: optimal (wrapper:cvxpy, solver: MOSEK); optimal value: 1.000000001380967\n",
+ "(PEPit) Solver status: optimal (wrapper:cvxpy, solver: SCS); optimal value: 0.9999999335394925\n",
"(PEPit) Primal feasibility check:\n",
"\t\tThe solver found a Gram matrix that is positive semi-definite\n",
- "\t\tAll the primal scalar constraints are verified up to an error of 2.823064004786602e-10\n",
+ "\t\tAll the primal scalar constraints are verified up to an error of 1.6263634718960418e-06\n",
"(PEPit) Dual feasibility check:\n",
- "\t\tThe solver found a residual matrix that is positive semi-definite\n",
- "\t\tAll the dual scalar values associated with inequality constraints are nonnegative up to an error of 3.921214759585488e-10\n",
- "(PEPit) The worst-case guarantee proof is perfectly reconstituted up to an error of 5.188665952512774e-09\n",
- "(PEPit) Final upper bound (dual): 1.0000000017034396 and lower bound (primal example): 1.000000001380967 \n",
- "(PEPit) Duality gap: absolute: 3.2247271519736387e-10 and relative: 3.224727147520397e-10\n",
- "Constraint \"Performance metric 1\" value: 1.0\n",
- "Constraint \"Initial condition\" value: 1.0000000017034396\n",
- "Constraint \"IC_Function_0_smoothness_convexity(xs, y_{k-1})\" value: 3.705732440347329e-11\n",
- "Constraint \"IC_Function_0_smoothness_convexity(xs, y_k)\" value: 3.2360679841230504\n",
- "Constraint \"IC_Function_0_smoothness_convexity(y_{k-1}, xs)\" value: -3.921214759585488e-10\n",
- "Constraint \"IC_Function_0_smoothness_convexity(y_{k-1}, y_k)\" value: 2.0000000046714836\n",
- "Constraint \"IC_Function_0_smoothness_convexity(y_k, xs)\" value: 1.2147824750871294e-08\n",
- "Constraint \"IC_Function_0_smoothness_convexity(y_k, y_{k-1})\" value: 8.354183338341928e-10\n"
+ "\t\tThe solver found a residual matrix that is positive semi-definite up to an error of 5.864649733113111e-17\n",
+ "\t\tAll the dual scalar values associated with inequality constraints are nonnegative\n",
+ "(PEPit) The worst-case guarantee proof is perfectly reconstituted up to an error of 2.55927411539812e-06\n",
+ "(PEPit) Final upper bound (dual): 0.9999995330241119 and lower bound (primal example): 0.9999999335394925 \n",
+ "(PEPit) Duality gap: absolute: -4.005153805275441e-07 and relative: -4.005154071460013e-07\n",
+ "Constraint \"Performance metric 1\" value: 0.999999999999415\n",
+ "Constraint \"Initial condition\" value: 0.9999995330241119\n",
+ "Constraint \"IC_Function_0_smoothness_convexity(xs, y_{k-1})\" value: 0.0\n",
+ "Constraint \"IC_Function_0_smoothness_convexity(xs, y_k)\" value: 3.2360683934302226\n",
+ "Constraint \"IC_Function_0_smoothness_convexity(y_{k-1}, xs)\" value: 0.0\n",
+ "Constraint \"IC_Function_0_smoothness_convexity(y_{k-1}, y_k)\" value: 1.9999995201447833\n",
+ "Constraint \"IC_Function_0_smoothness_convexity(y_k, xs)\" value: 0.0\n",
+ "Constraint \"IC_Function_0_smoothness_convexity(y_k, y_{k-1})\" value: 0.0\n"
]
}
],
@@ -2437,7 +2446,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 53,
"metadata": {
"tags": []
},