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Christos Efstathiou
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Simple Examples Synced
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‎simple_examples/baseresults/a00aaje.r‎

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Implementation title: Linux, 64-bit, Intel C/C++ (32-bit integers)
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Precision: double precision
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Product Code: CLL6I262CL
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Mark: 27.2.0 (self-contained)
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Mark: 27.3.0 (self-contained)
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*** End of NAG Library implementation details ***

‎simple_examples/baseresults/e04kfje.r‎

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E04KFJ Example Program Results
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E04KF, First order method for bound-constrained problems
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Begin of Options
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Print File = 6 * d
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Print Level = 1 * U
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Print Options = Yes * d
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Print Solution = All * U
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Monitoring File = 9 * U
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Monitoring Level = 3 * U
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Foas Monitor Frequency = 0 * d
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Foas Print Frequency = 5 * U
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Infinite Bound Size = 1.00000E+20 * d
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Task = Minimize * d
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Stats Time = No * d
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Time Limit = 1.00000E+06 * d
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Verify Derivatives = No * d
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Foas Estimate Derivatives = No * d
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Foas Finite Diff Interval = 1.05367E-08 * d
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Foas Iteration Limit = 10000000 * d
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Foas Memory = 11 * d
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Foas Progress Tolerance = 1.08158E-12 * d
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Foas Rel Stop Tolerance = 1.08158E-12 * d
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Foas Restart Factor = 6.00000E+00 * d
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Foas Slow Tolerance = 1.01316E-02 * d
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Foas Stop Tolerance = 1.00000E-06 * d
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Foas Tolerance Norm = Infinity * d
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End of Options
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Status: converged, an optimal solution was found
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Value of the objective 4.00000E-02
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Norm of inactive gradient 0.00000E+00
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Norm of projected direction 0.00000E+00
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Primal variables:
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idx Lower bound Value Upper bound
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1 -1.00000E+00 8.00000E-01 8.00000E-01
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2 -2.00000E+00 6.40000E-01 2.00000E+00
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Box bounds dual variables:
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idx Lower bound Value Upper bound Value
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1 -1.00000E+00 0.00000E+00 8.00000E-01 4.00000E-01
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2 -2.00000E+00 0.00000E+00 2.00000E+00 0.00000E+00
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Solution found:
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Objective function value at solution: 4.0E-02
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Gradient at solution: -4.0E-01 0.0E+00
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Estimated Lagrange multipliers: blx 0.0E+00 0.0E+00
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Estimated Lagrange multipliers: bux 4.0E-01 0.0E+00
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‎simple_examples/baseresults/outputexample.r‎

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Implementation title: Linux, 64-bit, Intel C/C++ or Intel Fortran
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Precision: double precision
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Product Code: NLL6I27DBL
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Mark: 27.2.0 (self-contained)
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Mark: 27.3.0 (self-contained)
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This is a 64-bit library using 32-bit integers.
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‎simple_examples/data/testdir/testtmp2.d‎

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‎simple_examples/data/tmptest.d‎

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‎simple_examples/source/int32/E04KFJE.java‎

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import com.nag.routines.E04.E04RA;
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import com.nag.routines.E04.E04RH;
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import com.nag.routines.E04.E04RG;
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import com.nag.routines.E04.E04ZM;
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import com.nag.routines.E04.E04KF;
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import com.nag.routines.E04.E04KFU;
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import com.nag.routines.E04.E04RX;
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import com.nag.routines.E04.E04RZ;
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import com.nag.routines.X04.X04AC;
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import java.lang.Math;
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/**
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* E04KF example program text.
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*/
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public class E04KFJE {
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private static final int NMONIT = 9;
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private static final int NVAR = 2;
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public static void main(String[] args) {
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long cpuser = 0;
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int nu = 2 * NVAR;
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/* Header */
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System.out.println(" E04KFJ Example Program Results\n");
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// Define filename for monitoring output
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X04AC x04ac = new X04AC();
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int ifail = 0;
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x04ac.eval(NMONIT, "e04kffe.mon", 1, ifail);
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// Initialize handle
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E04RA e04ra = new E04RA();
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long handle = 0;
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ifail = 0;
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e04ra.eval(handle, NVAR, ifail);
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handle = e04ra.getHANDLE();
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// Define initial guess point
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double[] x = new double[] { -1.5, 1.9 };
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// Define Simple box bounds on X
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E04RH e04rh = new E04RH();
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double[] blx = new double[] { -1.0, -2.0 };
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double[] bux = new double[] { 0.8, 2.0 };
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ifail = 0;
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e04rh.eval(handle, NVAR, blx, bux, ifail);
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// Add nonlinear objective information
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E04RG e04rg = new E04RG();
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int iidx[] = new int[NVAR];
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for (int i = 0; i < NVAR; i++)
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iidx[i] = i + 1;
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ifail = 0;
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e04rg.eval(handle, NVAR, iidx, ifail);
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// Add options
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E04ZM e04zm = new E04ZM();
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ifail = 0;
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e04zm.eval(handle, "FOAS Print Frequency = 5", ifail);
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ifail = 0;
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e04zm.eval(handle, "Print Solution = yes", ifail);
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ifail = 0;
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e04zm.eval(handle, "Print Level = 1", ifail);
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ifail = 0;
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e04zm.eval(handle, "Monitoring File = " + NMONIT, ifail);
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ifail = 0;
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e04zm.eval(handle, "Monitoring Level = 3", ifail);
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// Solve the problem
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E04KF e04kf = new E04KF();
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OBJFUN objfun = new OBJFUN();
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OBJGRD objgrd = new OBJGRD();
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MONIT monit = new MONIT();
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double[] rinfo = new double[100];
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double[] stats = new double[100];
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int[] iuser = new int[0];
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double[] ruser = new double[0];
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ifail = -1;
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e04kf.eval(handle, objfun, objgrd, monit, NVAR, x, rinfo, stats, iuser, ruser, cpuser, ifail);
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ifail = e04kf.getIFAIL();
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// Print objective value at solution
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if ((ifail == 0) || (ifail == 50)) {
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System.out.printf("\n\n Solution found:\n Objective function value at solution: %9.1E\n", rinfo[0]);
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// Retrieve Lagrange multipliers (FDX)
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E04RX e04rx = new E04RX();
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double[] u = new double[nu];
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e04rx.eval(handle, "U", 1, nu, u, ifail);
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if (ifail == 0) {
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System.out.printf(" Gradient at solution: %9.1E %9.1E\n\n", u[0] - u[1], u[2] - u[3]);
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System.out.printf(" Estimated Lagrange multipliers: blx %9.1E %9.1E\n", u[0], u[2]);
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System.out.printf(" Estimated Lagrange multipliers: bux %9.1E %9.1E\n", u[1], u[3]);
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}
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}
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System.out.println();
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// Clean up
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E04RZ e04rz = new E04RZ();
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ifail = 0;
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e04rz.eval(handle, ifail);
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}
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public static class OBJFUN extends E04KF.Abstract_E04KF_OBJFUN {
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public void eval() {
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// Rosenbrock function
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this.FX = Math.pow(1.0 - this.X[0], 2) + 100.0 * Math.pow(this.X[1] - Math.pow(this.X[0], 2), 2);
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}
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}
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public static class OBJGRD extends E04KF.Abstract_E04KF_OBJGRD {
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public void eval() {
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this.FDX[0] = 2.0 * this.X[0] - 400.0 * this.X[0] * (this.X[1] - Math.pow(this.X[0], 2)) - 2.0;
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this.FDX[1] = 200.0 * (this.X[1] - Math.pow(this.X[0], 2));
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}
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}
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public static class MONIT extends E04KF.Abstract_E04KF_MONIT {
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public void eval() {
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E04KFU e04kfu = new E04KFU();
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e04kfu.eval(this.NVAR, this.X, this.INFORM, this.RINFO, this.STATS, this.IUSER, this.RUSER, this.CPUSER);
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this.INFORM = e04kfu.getINFORM();
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}
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}
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}

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