Optimization with Analytic Hessian Computations¶

This GAUSS optimization example demonstrates the use of COMT to estimate the parameters of the Woods Function. This example moves beyond basic optimization and uses a user-defined function to analytically compute both the first derivatives and second derivatives.

The Wood Function is a common function for testing optimization performance. Notably, it converges at { 1, 1, 1, 1 } with a function value of 0.

Key example features¶

The user-defined objective function fct for computing the Woods Function.
The specification of analytic gradients inside fct.
The specification of the analytic Hessian inside fct

Code for optimization¶

new;
library comt;

// Create extra data needed for
// Hessian calculation
H = { 0   0   0   0,
      0 220.2 0  19.8,
      0   0   0   0,
      0  19.8 0 200.2 };

// Starting parameter vector
x_strt = { -3, -1, -3, -1 };

// Objective procedure
proc fct(x, H, ind);
    local h0;
    struct modelResults mm;

    // If first element of indicator vector is non-zero,
    // compute objective function
    if ind[1];
        // Compute objective function
        mm.function = 100*(x[2]-x[1]^2)^2 + (1-x[1])^2 + 90*(x[4]-x[3]^2)^2 +
          (1-x[3])^2 + 10.1*((x[2]-1)^2 + (x[4]-1)^2) + 19.8*(x[2]-1)*(x[4]-1);
    endif;

    if ind[2];
        // Compute gradient
        local a,b;
        a = x[2] - x[1]^2;
        b = x[4] - x[3]^2;
        mm.gradient = zeros(1,4);
        mm.gradient[1] = -2*(200*x[1]*a + 1 - x[1]);
        mm.gradient[2] = 2*(100*a + 10.1*(x[2]-1) + 9.9*(x[4]-1));
        mm.gradient[3] = -2*(180*x[3]*b + 1 - x[3]);
        mm.gradient[4] = 2*(90*b + 10.1*(x[4]-1) + 9.9*(x[2]-1));
    endif;


    if ind[3];
        // Compute Hessian
        mm.hessian = H;
        mm.hessian[1,1] = -2*(200*(x[2]-x[1]^2) - 400*x[1]^2 - 1);
        mm.hessian[1,2] = -400*x[1];
        mm.hessian[2,1] = mm.hessian[1,2];
        mm.hessian[3,3] = -2*(180*(x[4]-x[3]^2) - 360*x[3]^2 - 1);
        mm.hessian[4,3] = -360*x[3];
        mm.hessian[3,4] = mm.hessian[4,3];
     endif;

     // Return modelResults struct
     retp(mm);
endp;

// Declare 'out' to be a comtResults struct
struct comtResults out;

// Minimize objective function
out = comt(&fct, x_strt, H);

// Print results of the optimization
call comtPrt(out);

The code prints results to the Command Window.

Results¶

Gradient comparisons¶

The first notable component of the results is the printed comparison of the analytical and numerical gradients. This is done because the ctl.gradCheck member of the comtControl structure was set to 1.

analytical gradient        numerical gradient
-932.8196153813061073     -932.8196069540683766
-18609.6357937713837600     -18609.6345830073878460

Convergence details¶

The first portion of the results provide details about convergence and performance.

Return code    = 0
Function value = 0.00000
Convergence    : normal convergence

These results indicate that the optimization converged normally, with a return code of 0. Any return Code other than 0 would indicate some issue with the convergence. The exact meaning of the return code can be found in the comt() documentation.

Parameter estimates¶

The next section of the results reports the parameter estimates and the associated gradients.

Parameters  Estimates    Gradient
---------------------------------------------------------------------
x[1,1]         1.0000      0.0000
x[2,1]         1.0000      0.0000
x[3,1]         1.0000      0.0000
x[4,1]         1.0000      0.0000

Computation time¶

The final section of the results reports the number of iterations and computation time.

Number of iterations    2572
Minutes to convergence  0.00793

Optimization with Analytic Gradient Computations

Optimization with Linear Equality Constraints