Convex approximations in stochastic programming by semidefinite programming

doi:10.1007/s10479-011-0986-0

Convex approximations in stochastic programming by semidefinite programming

2011-10-01

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DOI：10.1007/s10479-011-0986-0

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The following question arises in stochastic programming: how can one approximate a noisy convex function with a convex quadratic function that is optimal in some sense. Using several approaches for constructing convex approximations we present some optimization models yielding convex quadratic regressions that are optimal approximations in L (1), L (a) and L (2) norm. Extensive numerical experiments to investigate the behavior of the proposed methods are also performed.

Keyword:

Convex approximation

Stochastic optimization

Successive regression approximations

Semidefinite optimization

Convex approximations in stochastic programming by semidefinite programming

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