Optimization

346 Appendix B

% delx -> required for gradient computation

% falpha_prev -> function value at first/previous iteration

% deriv -> gradient vector

% deltag -> difference in gradient vector (over previous

% iteration)

% A -> approximation of the hessian matrix

% search -> search direction

%

clear all

clc

n_of_var = 2;

x = [-3 2];

delx = 0.001;

A = eye(length(x));

epsilon1 = 1e-6;

epsilon2 = 1e-6;

delx = 1e-3;

falpha_prev = func_multivar(x);

fprintf('Initial function value = %7.4f\n ',falpha_prev)

fprintf(' No. x-vector f(x) Deriv \n')

fprintf('__________________________________________\n')

for i = 1:50

if i==1

deriv_prev = grad_vec(x,delx,n_of_var);

search = -deriv_prev;

[alpha,falpha] = golden_funct1(x,search);

if abs(falpha-falpha_prev)<0.001

break;

end

falpha_prev ...