CODEWITHSUNDEEP
matlabmathematical-optimizationgradient-descent
Swami
Swami

Reputation: 81

minFunc package usage

I have been using MATLAB fminunc function to solve my optimization problem. I want to try the minFunc package :

http://www.di.ens.fr/~mschmidt/Software/minFunc.html

When using fminunc, I defined a function funObj.m which gives me the objective value and the gradient at any point 'x'. It also takes in several external inputs say, {a,b,c} which are matrices. So the function prototype looks like :

function [objVal,G] = funObj(x,a,b,c)

I want to use the same setup in the minFunc package. From the examples, I figured this should work :

options.Method='lbfgs';
f = @(x)funObj(x,a,b,c);
x = minFunc(f,x_init,options);

But when I call this way, I get an error as:

Error using funObj
Too many output arguments.

What is the correct way to call minFunc for my case?

**EDIT : Alright, here is a sample function that I want to use with minFunc. Lets say I want to find the minimum of a*(b-x)^2, where a,b are scalar parameters and x being a scalar too. The MATLAB objective function will then look like :

function obj = testFunc(x,a,b)
obj = a*(b-x)^2;

The function call to minimize this using fminunc (in MATLAB ) is simply:

f = @(x)testFunc(x,a,b);
x = fminunc(f,x_init);

This gives me the minimum of x = 10. Now, How do I do the same using minFunc ?

Upvotes: 4

Views: 2562

Answers (2)

Hao Zhu
Hao Zhu

Reputation: 11

Agree with Mark. I think the simplest way to solve it is 

minFunc(@testFunc, x_init, a, b, c)

In MATLAB temporary function can only have one return value. So f = @(x)testFunc(x,a,b); let your method drop gradient part every time. Because minFunc can accept extra paramters, you can pass a, b and c after x_init. I think this would work.

Upvotes: 0

Mark Schmidt
Mark Schmidt

Reputation: 41

"Note that by default minFunc assumes that the gradient is supplied, unless the 'numDiff' option is set to 1 (for forward-differencing) or 2 (for central-differencing)."

The error is because only one argument is returned by the function. You can either return the gradient as a second argument or turn on numerical differencing.

Upvotes: 4

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