## Quadratic minimization with norm constraint

Versión 1.3.0.0 (2.84 KB) por
Minimizes convex or non-convex quadratics subject to (in)equality constraint on norm(x)

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This routine minimizes an arbitrary quadratic function subject to a constraint on the l2-norm of the variables. The problem is of a form commonly encountered as a sub-problem in trust region algorithms, but undoubtedly has other applications as well.

USAGE:

[xmin,Jmin] = trustregprob(Q,b,w)
[xmin,Jmin] = trustregprob(Q,b,w,doEquality)

When doEquality=true (the default), the routine solves,

minimize J(x) = x.'*Q*x/2-dot(b,x) such that ||x|| = w

where ||x|| is the l2-norm of x. The variables returned xmin, Jmin are the minimizing x and its objective function value J(x).

When doEquality=false, the routine solves instead subject to ||x|| <= w .

Q is assumed symmetric, but not necessarily positive semi-definite. In other words, the objective function J(x) is potentially non-convex. Since the solution is based on eigen-decomposition, it is appropriate mainly for Q not too large. If multiple solutions exist, only one solution is returned.

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##### Compatibilidad con la versión de MATLAB
Se creó con R2015a
Compatible con cualquier versión
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Inspirado por: Least-square with 2-norm constraint

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Versión Publicado Notas de la versión
1.3.0.0

Improved error checking
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1.2.0.0

Fixed a bug that affected the special case b=zeros(N,1)

1.1.0.0

Improved numerical robustness
Fixed a numerical robustness issue

1.0.0.0

Minor polishes to file description
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Minor edits to help text and description