constrained minimization using pattern search, solver-凯发k8网页登录

constrained minimization problem

for this problem, the objective function to minimize is a simple function of a 2-d variable x.

simple_objective(x) = (4 - 2.1*x(1)^2 x(1)^4/3)*x(1)^2 x(1)*x(2) (-4 4*x(2)^2)*x(2)^2;

this function is known as "cam," as described in l.c.w. dixon and g.p. szego [1].

additionally, the problem has nonlinear constraints and bounds.

   x(1)*x(2)   x(1) - x(2)   1.5 <= 0  (nonlinear constraint)
   10 - x(1)*x(2) <= 0                 (nonlinear constraint)
   0 <= x(1) <= 1                      (bound)
   0 <= x(2) <= 13                     (bound)

code the objective function

create a matlab® file named simple_objective.m containing the following code:

type simple_objective

function y = simple_objective(x)
%simple_objective objective function for patternsearch solver
%   凯发官网入口首页 copyright 2004 the mathworks, inc.  
x1 = x(1);
x2 = x(2);
y = (4-2.1.*x1.^2 x1.^4./3).*x1.^2 x1.*x2 (-4 4.*x2.^2).*x2.^2;

solvers such as patternsearch accept a single input x, where x has as many elements as the number of variables in the problem. the objective function computes the scalar value of the objective function and returns it in its single output argument y.

coding the constraint function

create a matlab file named simple_constraint.m containing the following code:

type simple_constraint

function [c, ceq] = simple_constraint(x)
%simple_constraint nonlinear inequality constraints.
%   凯发官网入口首页 copyright 2005-2007 the mathworks, inc.
c = [1.5   x(1)*x(2)   x(1) - x(2); 
     -x(1)*x(2)   10];
% no nonlinear equality constraints:
ceq = [];

the constraint function computes the values of all the inequality and equality constraints and returns the vectors c and ceq, respectively. the value of c represents nonlinear inequality constraints that the solver attempts to make less than or equal to zero. the value of ceq represents nonlinear equality constraints that the solver attempts to make equal to zero. this example has no nonlinear equality constraints, so ceq = []. for details, see .

minimize using patternsearch

specify the objective function as a function handle.

objectivefunction = @simple_objective;

specify the problem bounds.

lb = [0 0];   % lower bounds
ub = [1 13];  % upper bounds

specify the nonlinear constraint function as a function handle.

constraintfunction = @simple_constraint;

specify an initial point for the solver.

x0 = [0.5 0.5];   % starting point

call the solver, requesting the optimal point x and the function value at the optimal point fval.

[x,fval] = patternsearch(objectivefunction,x0,[],[],[],[],lb,ub, ...
    constraintfunction)

optimization finished: mesh size less than options.meshtolerance 
and constraint violation is less than options.constrainttolerance.

x = 1×2
    0.8122   12.3122

fval = 9.1324e 04

add visualization

to observe the solver's progress, specify options that select two plot functions. the plot function psplotbestf plots the best objective function value at every iteration, and the plot function psplotmaxconstr plots the maximum constraint violation at every iteration. set these two plot functions in a cell array. also, display information about the solver's progress in the command window by setting the display option to 'iter'.

options = optimoptions(@patternsearch,'plotfcn',{@psplotbestf,@psplotmaxconstr}, ...
                                      'display','iter');

run the solver, including the options argument.

[x,fval] = patternsearch(objectivefunction,x0,[],[],[],[],lb,ub, ...
    constraintfunction,options)

                                      max
  iter   func-count       f(x)      constraint   meshsize      method
    0         1     0.373958         9.75       0.9086    
    1        18       113581    1.617e-10        0.001   increase penalty
    2       147        92267            0        1e-05   increase penalty
    3       373      91333.2            0        1e-07   increase penalty
    4       638        91324            0        1e-09   increase penalty
optimization finished: mesh size less than options.meshtolerance 
and constraint violation is less than options.constrainttolerance.

x = 1×2
    0.8122   12.3122

fval = 9.1324e 04

nonlinear constraints cause patternsearch to solve many subproblems at each iteration. as shown in both the plots and the iterative display, the solution process has few iterations. however, the func-count column in the iterative display shows many function evaluations per iteration. both the plots and the iterative display show that the initial point is infeasible, and that the objective function is low at the initial point. during the solution process, the objective function value initially increases, then decreases to its final value.

references

[1] dixon, l. c. w., and g .p. szego (eds.). towards global optimisation 2. north-holland: elsevier science ltd., amsterdam, 1978.