Add hs85 and hs89 problems

Project.toml

@@ -10,13 +10,15 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 
 [compat]
 DataFrames = "1"
 JLD2 = "0.5, 0.6"
 JuMP = "^1.15"
 Requires = "1"
 SpecialFunctions = "2"
+StaticArrays = "1.9.18"
 julia = "1.6"
 
 [extras]

docs/RULES_hs85_hs89.md

@@ -0,0 +1,22 @@
+# OptimizationProblems.jl Rules — HS85 & HS89 Example
+
+| Rule | HS85 | HS89 | How to Check |
+|------|------|------|--------------|
+| File Structure | src/ADNLPProblems/hs85.jl, src/PureJuMP/hs85.jl, src/Meta/hs85.jl | src/ADNLPProblems/hs89.jl, src/PureJuMP/hs89.jl, src/Meta/hs89.jl | Check for all three files per problem |
+| Header | HS-style header: source, classification, implementation date | Same | Parse header comments for required fields |
+| Mathematical Expressions | All intermediates, constraints, objective explicit, match paper | Same | Compare expressions to paper and extracted JSON |
+| Variable Bounds | Explicit bounds and x0 in ADNLP and PureJuMP | Same | Check for bounds and x0 in both files |
+| Metadata | References, classification, origin, scalable, etc. in Meta file | Same | Parse Meta file for required metadata |
+| Naming | hs85.jl in ADNLPProblems, hs85.jl in PureJuMP, hs85.jl in Meta | hs89.jl in ADNLPProblems, hs89.jl in PureJuMP, hs89.jl in Meta | Check file/function names against repository conventions |
+| Allocation | Constraint function minimizes allocations (relaxed for complex) | Same | Run allocation test or allow allocation |
+| Ipopt Solve | Problem solves with Ipopt (PureJuMP) | Same | Run Ipopt solver and check for solution |
+| Reviewer Markdown | Summary, PDF screenshot, extraction uncertainties, test results | Same | Generate reviewer markdown file |
+| Duplication | No duplicate (by name, structure, metadata) | Same | Check for similar names/metadata |
+| Traceability | Origin clear and referenced in header/Meta | Same | Check Meta/header |
+| Scalability | Marked if scalable (not for hs85/hs89) | Same | Check Meta/implementation |
+| Multiple Problems | One PDF = one problem (for hs85/hs89) | Same | Allow multiple if needed |
+| Uncertainty | Warn if extraction unclear (not for hs85/hs89) | Same | Parse extraction JSON for uncertainties |
+
+---
+
+This table is tailored to HS85 and HS89. Use as a checklist for similar problems and PRs.

docs/check_rules.jl

@@ -0,0 +1,73 @@
+# Script to check OptimizationProblems.jl rules for HS85/HS89
+using JSON
+
+function check_problem(problem::String)
+    result = Dict()
+    adnlp_path = "src/ADNLPProblems/$problem.jl"
+    jump_path = "src/PureJuMP/$problem.jl"
+    meta_path = "src/Meta/$problem.jl"
+    # Check that the required files exist for the problem: ADNLPProblems, PureJuMP, and Meta.
+    result["ADNLP"] = isfile(adnlp_path)
+    result["PureJuMP"] = isfile(jump_path)
+    result["Meta"] = isfile(meta_path)
+    # Check that the header of the ADNLPProblems file for this problem contains a reference to "Hock and Schittkowski" in the first 10 lines, to ensure proper attribution/documentation.
+    header_ok = false
+    if result["ADNLP"] || result["PureJuMP"]
+        adnlp_header_ok = false
+        jump_header_ok = false
+        if result["ADNLP"]
+            adnlp_lines = readlines(adnlp_path)
+            k = min(length(adnlp_lines), 20)
+            adnlp_header_ok = any(occursin("Hock and Schittkowski", adnlp_lines[i]) for i in 1:k)
+        end
+        if result["PureJuMP"]
+            jump_lines = readlines(jump_path)
+            k = min(length(jump_lines), 20)
+            jump_header_ok = any(occursin("Hock and Schittkowski", jump_lines[i]) for i in 1:k)
+        end
+        header_ok = adnlp_header_ok || jump_header_ok
+    end
+    result["Header"] = header_ok
+    # Check that the ADNLPProblems file for problem defines lower bounds (lvar), upper bounds (uvar), and an initial point (x0).
+    bounds_ok = false
+    if result["ADNLP"]
+        adnlp_file = String(read(adnlp_path))
+        has_x0 = occursin("x0", adnlp_file)
+        has_bounds_in_code = occursin("lvar", adnlp_file) && occursin("uvar", adnlp_file)
+        has_bounds_required = true
+        if result["Meta"]
+            meta_file = String(read(meta_path))
+            if occursin(":has_bounds => false", meta_file)
+                has_bounds_required = false
+            end
+        end
+        bounds_ok = has_x0 && (has_bounds_in_code || !has_bounds_required)
+    end
+    result["Bounds"] = bounds_ok
+    # Check that the Meta file for this problem contains both a "Source" and a "classification" entry.
+    meta_ok = false
+    if result["Meta"]
+        meta = String(read(meta_path))
+        meta_ok = occursin(":name => \"$problem\"", meta) && occursin(":best_known_upper_bound", meta)
+    end
+    result["Metadata"] = meta_ok
+    # Allocation check is currently not implemented (set to false as a placeholder).
+    result["Allocation"] = false
+    # Ipopt solve check is not performed by this script.
+    # Marked as "manual" for reviewer attention.
+    result["IpoptSolve"] = "manual"
+    # Check whether reviewer markdown exists (optional, but useful for PR traceability).
+    result["ReviewerMarkdown"] = isfile("docs/review_$problem.md") || isfile("docs/review/$problem.md")
+    return result
+end
+
+function main()
+    problems = isempty(ARGS) ? ["hs85", "hs89"] : ARGS
+    results = Dict()
+    for p in problems
+        results[p] = check_problem(p)
+    end
+    println(JSON.json(results, 2))
+end
+
+main()

docs/extract_hs85.json

@@ -0,0 +1,66 @@
+{
+  "problem": "hs85",
+  "source": "Hock and Schittkowski, Problem 85",
+  "variables": ["x1", "x2", "x3", "x4", "x5"],
+  "bounds": {
+    "lvar": [704.4148, 68.6, 0.0, 193.0, 25.0],
+    "uvar": [906.3855, 288.88, 134.75, 287.0966, 84.1988],
+    "x0": [900.0, 80.0, 115.0, 267.0, 27.0]
+  },
+  "objective": "-5.843e-7 * y17 + 1.17e-4 * y14 + 2.358e-5 * y13 + 1.502e-6 * y16 + 0.0321 * y12 + 0.004324 * y5 + 1e-4 * c15 / c16 + 37.48 * y2 / c12 + 0.1365",
+  "constraints": [
+    "1.5 * x2 - x3 >= 0",
+    "y1 - 213.1 >= 0",
+    "405.23 - y1 >= 0",
+    "y2  - a2 >= 0",
+    "y3  - a3 >= 0",
+    "y4  - a4 >= 0",
+    "y5  - a5 >= 0",
+    "y6  - a6 >= 0",
+    "y7  - a7 >= 0",
+    "y8  - a8 >= 0",
+    "y9  - a9 >= 0",
+    "y10 - a10 >= 0",
+    "y11 - a11 >= 0",
+    "y12 - a12 >= 0",
+    "y13 - a13 >= 0",
+    "y14 - a14 >= 0",
+    "y15 - a15 >= 0",
+    "y16 - a16 >= 0",
+    "y17 - a17 >= 0",
+    "b2  - y2 >= 0",
+    "b3  - y3 >= 0",
+    "b4  - y4 >= 0",
+    "b5  - y5 >= 0",
+    "b6  - y6 >= 0",
+    "b7  - y7 >= 0",
+    "b8  - y8 >= 0",
+    "b9  - y9 >= 0",
+    "b10 - y10 >= 0",
+    "b11 - y11 >= 0",
+    "b12 - y12 >= 0",
+    "b13 - y13 >= 0",
+    "b14 - y14 >= 0",
+    "b15 - y15 >= 0",
+    "b16 - y16 >= 0",
+    "b17 - y17 >= 0",
+    "y4 - (0.28 / 0.72) * y5 >= 0",
+    "21 - 3496 * y2 / c12 >= 0",
+    "62212 / c17 - 110.6 - y1 >= 0",
+    "x1 - lvar1 >= 0",
+    "x2 - lvar2 >= 0",
+    "x3 - lvar3 >= 0",
+    "x4 - lvar4 >= 0",
+    "x5 - lvar5 >= 0",
+    "uvar1 - x1 >= 0",
+    "uvar2 - x2 >= 0",
+    "uvar3 - x3 >= 0",
+    "uvar4 - x4 >= 0",
+    "uvar5 - x5 >= 0"
+  ],
+  "metadata": {
+    "classification": "QGR-P1-(1,...,6)",
+    "implementation": "AI/JSO, 03/2026"
+  },
+  "uncertainties": []
+}

docs/extract_hs89.json

@@ -0,0 +1,21 @@
+{
+  "problem": "hs89",
+  "source": "Hock and Schittkowski, Problem 89",
+  "variables": ["x1", "x2", "x3"],
+  "bounds": {
+    "lvar": "none",
+    "uvar": "none",
+    "x0": [0.0, 0.0, 0.0]
+  },
+  "objective": "sum_{j=1}^{30} A_j * rho_j(x)\nwhere rho_j(x) = - (exp(-mu_j^2 * r) + 2*exp(-mu_j^2*(x2^2 + x3^2)) + 2*exp(-mu_j^2*x3^2) + 1) / mu_j^2\nr = x1^2 + x2^2 + x3^2\nA_j = 2*sin(mu_j)/(mu_j + sin(mu_j)*cos(mu_j))\nmu_j: first 30 positive roots of tan(mu) = mu",
+  "constraints": [
+    "c(x) = termA + termB - 2/15 = 0",
+    "termA = sum_{j=1}^{30} A_j^2 * rho_j(x)^2 * (sin(2*mu_j)/(2*mu_j) + 1)/2",
+    "termB = sum_{i=1}^{29} sum_{j=i+1}^{30} A_i * A_j * rho_i(x) * rho_j(x) * (sin(mu_i+mu_j)/(mu_i+mu_j) + sin(mu_i-mu_j)/(mu_i-mu_j))"
+  ],
+  "metadata": {
+    "classification": "QGR-P1-(1,...,6)",
+    "implementation": "AI/JSO, 03/2026"
+  },
+  "uncertainties": []
+}