source: MML/trunk/machine/SOLEIL/common/toolbox/optimize/testoptimize.m @ 4

%% Constrained optimization with FMINSEARCH

function testoptimize

    
%% Introduction    

%% Usage
 
    %%
    % first, define a test function:
    clc, rosen = @(x) (1-x(1))^2 + 105*(x(2)-x(1)^2)^2;
    
    %%
    % this is the classical Rosenbr\"uck function, which has a global minimum
    % at _f(x)_ = _f_([1, 1]) = 0. The function is relatively hard to optimize,
    % because that minimum is located in a long narrow ``valley'':
    k = 0; range = -5:0.1:5;
    z = zeros(101);    
    for i = range
        m = 0; k = k + 1;        
        for j = range
            m = m + 1;
            z(k, m) = rosen([i, j]);            
        end
    end  
    [y, x] = meshgrid(range, range);
    surf(x, y, z, 'linestyle', 'none'), view(-150, 30), axis tight    
    
    %%
    % Optimizing the fully unconstrained problem with OPTIMIZE indeed finds 
    % the global minimum:
    % warning work only with optimization toolbox !!!
    
    solution = optimize(rosen, [3 3])    
    
    %%
    % Imposing a lower bound on the variables gives 
    [solution, fval] = optimize(rosen, [3 3], [2 2], [])
    
    %%
    % in the figure, this looks like
    zz = z;   zz(x > 2 & y > 2) = inf;  
    ZZ = z;   ZZ(x < 2) = inf;  ZZ(y < 2) = inf;
    figure, hold on
    surf(x, y, zz, 'linestyle', 'none', 'FaceAlpha', 0.2)
    surf(x, y, ZZ, 'linestyle', 'none')
    view(-150, 30), grid on, axis tight      
    plot3(solution(1), solution(2), fval+1e3, 'g.', 'MarkerSize', 20)    
    
    %%
    % Similarly, imposing an upper bound yields
    solution = optimize(rosen, [3 3], [], [0.5 0.5])
    
    zz = z;   zz(x < 0.5 & y < 0.5) = inf;  
    ZZ = z;   ZZ(x > 0.5) = inf;  ZZ(y > 0.5) = inf;
    figure, hold on
    surf(x, y, zz, 'linestyle', 'none', 'FaceAlpha', 0.2)
    surf(x, y, ZZ, 'linestyle', 'none')
    view(150, 30), grid on, axis tight      
    plot3(solution(1), solution(2), fval+1e3, 'g.', 'MarkerSize', 20)    
 
    %%
    % Optimize with _x_(2) fixed at 3. In this case, OPTIMIZE simply
    % removes the variable before FMINSEARCH sees it, essentially 
    % reducing the dimensionality of the problem. This is particularly
    % useful when the number of dimensions _N_ becomes large.
    optimize(rosen, [3 3], [-inf 3], [inf 3])
    
    %%
    % Also general nonlinear constraints can be used. A simple example:
    %
    % nonlinear inequality: 
    %
    % $$\sqrt{x_1^2 + x_2^2} \leq 1$$
    %
    % nonlinear equality  : 
    %
    % $$x_1^2 + x_2^3 = 0.5$$
        
    options = optimset('TolFun', 1e-8, 'TolX', 1e-8);
    [sol, fval, exitflag, output] = optimize(rosen, [3 -3], [], [], [], ...
        [], [], [], @nonlcon, [], options);
    
    %%
    % Note that |nonlcon| is a subfunction, listed below. In a figure, this
    % looks like
    zz = z;   zz(sqrt(x.^2 + y.^2) <= 1) = inf;  
    ZZ = z;   ZZ(sqrt(x.^2 + y.^2) >= 1.2) = inf;
    zZ = z;   zZ(x.^2 + y.^3 >= 0.5 + 0.1) = inf; 
              zZ(x.^2 + y.^3 <= 0.5 - 0.1) = inf;
    figure, hold on
    surf(x, y, zz, 'linestyle', 'none', 'FaceAlpha', 0.2)
    surf(x, y, ZZ, 'linestyle', 'none')
    xX = x(isfinite(zZ)); xX = xX(:);
    yY = y(isfinite(zZ)); xX = xX(:); 
    zZ = zZ(isfinite(zZ)); zZ = zZ(:);
    [xX, inds] = sort(xX); yY = yY(inds); zZ = zZ(inds);
    xyz = [xX, yY, zZ];
    for i = 1:length(xX)-1 % line-command is *somewhat* inconvenient...
        l = line( [xyz(i, 1); xyz(i+1, 1)],[xyz(i, 2); xyz(i+1, 2)], [xyz(i, 3); xyz(i+1, 3)]);
        set(l, 'color', 'r', 'linewidth', 2)
    end
    view(150, 50), grid on, axis tight      
    plot3(sol(1), sol(2), fval+1e3, 'g.', 'MarkerSize', 20) 
   
    %%
    % Note that the output structure contains a field ``constrviolation'':
    output
    
    %%
    % The contents of which shows that all constraints have been satisfied:
    output.constrviolation


end

%%
function [c, ceq] = nonlcon(x)
    c = norm(x) - 1;
    ceq = x(1)^2 + x(2)^3 - 0.5;
end