From: <HIDDEN>
Newsgroups: comp.soft-sys.matlab
Subject: Re: How to get max value of a function subject some constraints in
Date: Thu, 23 Apr 2009 18:56:01 +0000 (UTC)
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hhwolf76 <> wrote in message <>...
> This is a math problem in my project.
> If theta_N=[a b c d]', which is variable. theta_0=[a0 b0 c0 d0]',which is constant and known. PN is a 4X4 positive definite symetric matrix, which is constant and known. 
> If Y=T^2*(a-a0)+T*(b-b0)+(c-c0)+t*(d-d0). 
> Here, T and t are constant and known value. Y is scalar.
> I need get the max value of |Y|, and corresponding theta_N
> The constaint is 
> (theta_N-theta_0)'*(PN^-1)*(theta_N-theta_0)<=alpha
> alpha is a constant and known value.
> I asked this question in sci.math, someone told me I should use Lagrange multiplier method. But I don't known how to use this method in Matlab because I am not familar with Matlab.
> BTW, the actual problem is even more complicated. In my project, T represents temperature and changes along with the time. t represents time, and the unit is second. At last I need get the max value of the sum of |Y1|+|Y2|+|Y3|+...+|Yn|, when the time is 24 hours, which makes n=24*3600. And I need get corresponding theta_N.

  Define the column vectors x = theta_N-theta_0, and R = [T^2;T;1;t].  Then your problem is equivalent to finding x such that Y^2 = (x'*R)^2 = x'*(R*R')*x is maximized subject to x'*(PN^-1)*x <= alpha.  This is an eigenvector problem.

 [U,S,V] = svd(PN*R*R');
 x = sqrt(alpha/(V(:,1)'*PN^-1*V(:,1)))*V(:,1);

  The reasoning is this.  By the way x is defined it has the property that

 x'*(PN^-1)*x = alpha


 PN*R*R'*V(:,1) = D(1,1)*V(:,1)

where D(1,1) is the largest eigenvalue and V(:,1) the corresponding eigenvector.  Multiplying on the left by V(:,1)'*PN^-1 gives

 V(:,1)'*R*R'*V(:,1) = D(1,1)*V(:,1)'*PN^-1*V(:,1)


 Y^2 = x'*R*R'*x = D(1,1)*x'*PN^-1*x = D(1,1)*alpha

Therefore Y^2 takes its maximum value with this maximum eigenvalue, D(1,1).

  You can then find [a;b;c;d] as an offset of [a0,b0,c0,d0] by x.

  Your informant in sci.math was correct.  This is indeed a problem in Lagrange multipliers and we have selected the largest multiplier by choosing the largest eigenvalue.

Roger Stafford