Elias at 22:35, 17 January 2012

2012-01-17T22:35:53Z

← Older revision		Revision as of 18:35, 17 January 2012
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	Open the file for viewing, and execute madQG in Matlab.		Open the file for viewing, and execute madQG in Matlab.

	<~~syntaxhighlight~~ lang="matlab">		<source lang="matlab">
	% madQG are two examples for defining and solving nonlinear		% madQG are two examples for defining and solving nonlinear
	% programming problems using TOMLAB /MAD		% programming problems using TOMLAB /MAD
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	Prob2.ADCons = -1; % Lagrangian function for the nonlinear constraints.		Prob2.ADCons = -1; % Lagrangian function for the nonlinear constraints.
	Result2 = tomRun('conopt', Prob2, 1); % Uses second order information.		Result2 = tomRun('conopt', Prob2, 1); % Uses second order information.
	</~~syntaxhighlight~~>		</source>

Elias: Created page with "{{Part Of Manual|title=the Quickguide Manual|link=Quickguide}} TOMLAB /MAD is a package for general automatic differentiation of Matlab code. Usage is applicable f..."

2011-08-11T10:25:42Z

Created page with "{{Part Of Manual|title=the Quickguide Manual|link=Quickguide}} TOMLAB /MAD is a package for general automatic differentiation of Matlab code. Usage is applicable f..."

New page

{{Part Of Manual|title=the Quickguide Manual|link=[[Quickguide|Quickguide]]}}
TOMLAB /MAD is a package for general automatic differentiation of Matlab code. Usage is applicable for any applications needing derivatives. The package can be used standalone or as part of TOMLAB when floating point precision derivatives are needed.

Following is a simple example of standalone use:

<pre>
>> x = 1;
>> x = fmad(x,1);
>> y = sin(x);
>> y

value =

0.8415

derivatives =

0.5403
</pre>

An example problem with TOMLAB is included in the guide. The following file defines and solves two problems in TOMLAB.

'''File: '''tomlab/quickguide/madQG.m

Open the file for viewing, and execute madQG in Matlab.

<syntaxhighlight lang="matlab">
% madQG are two examples for defining and solving nonlinear
% programming problems using TOMLAB /MAD

Name = 'RBB Problem';
x_0 = [-1.2 1]'; % Starting values for the optimization
x_L = [-10;-10]; % Lower bounds for x.
x_U = [2;2]; % Upper bounds for x.
fLowBnd = 0; % Lower bound on function.

c_L = -1000; % Lower bound on nonlinear constraints.
c_U = 0; % Upper bound on nonlinear constraints.

Prob1 = conAssign('rbbQG_f', [], [], [], x_L, x_U, Name, x_0,...
[], fLowBnd, [], [], [], 'rbbQG_c', [], [], [], c_L, c_U);

Prob2 = conAssign('rbbQG_f', 'rbbQG_g', [], [], x_L, x_U, Name, x_0,...
[], fLowBnd, [], [], [], 'rbbQG_c', 'rbbQG_dc', [], [], c_L, c_U);

Prob1.Warning = 0; % Turning off warnings.
Prob2.Warning = 0; % Turning off warnings.

madinitglobals;
Prob1.ADObj = 1; % Gradient calculated
Prob1.ADCons = 1; % Jacobian calculated
Result1 = tomRun('snopt', Prob1, 1); % Only uses first order information.

madinitglobals;
Prob2.CONOPT.options.LS2PTJ = 0;
Prob2.ADObj = -1; % Hessian calculated
Prob2.ADCons = -1; % Lagrangian function for the nonlinear constraints.
Result2 = tomRun('conopt', Prob2, 1); % Uses second order information.
</syntaxhighlight>

Quickguide MAD Problem - Revision history

Elias at 22:35, 17 January 2012

Elias: Created page with "{{Part Of Manual|title=the Quickguide Manual|link=Quickguide}} TOMLAB /MAD is a package for general automatic differentiation of Matlab code. Usage is applicable f..."