graphical-opt - Graphical Graphicaloptimization

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raphical optimization Graphical optimization r problems that are cheap to simulate For problems that are cheap to simulate, trusting optimization software to give the ght answer is foolish right answer is foolish. It is better to thoroughly explore manually. In two dimensions, graphical optimization is a good way to go. In higher dimensions, two dimensional cuts can help.
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Example Plot and estimate minimum of the range 1 22 12 1 2 1 2 2 (, ) 2 2 2 f xx x x x xx x  2 20 0 3 x x  In the range x=linspace( 2,0,40); y=linspace(0,3,40); [X,Y]=meshgrid(x,y); Z=2+X Y+2*X.^2+2*X.*Y+Y.^2; cs=contour(X,Y,Z); 3 5 6 7 2.5 3 clabel(cs); xlabel('x_1');ylabel('x_2'); 1 2 4 x 2 1.5 2 3 0.5 1 2 2 4 5 6 7 8 x 1 -2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0
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an do mesh plot instead Can do mesh plot instead rfc YZ); cs=surfc(X,Y,Z); xlabel('x_1'); ylabel('x_2'); zlabel('f(x_1,x_2)'); 4 6 8 x 1 ,x 2 ) 3 0 2 f( -2 -1.5 -1 -0.5 0 0 1 2 x 1 x 2
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Then zoom x=linspace( 1.5, 0.5,40); y=linspace(1,2,40); [X,Y]=meshgrid(x,y); Z=2+X Y+2*X.^2+2*X.*Y+Y.^2; cs=surfc(X,Y,Z); 1.2 1.4 1.6 1.8
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This note was uploaded on 06/06/2011 for the course EAS 4240 taught by Professor Peterifju during the Spring '08 term at University of Florida.

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graphical-opt - Graphical Graphicaloptimization

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