### Istvan Deak (Corvinus University, Budapest)

#### Applications of successive regression approximations in stochastic programming

*Wednesday 11 March 2009 at 15.30, JCMB 6206*

##### Abstract

A new method was proposed for solving optimization problems with noisy
functions, the method is called Successive Regression Approximations (SRA). In
optimization practice one often resorts to some kind of approximations. Linear
and quadratic polynomials, orthogonal functions, Taylor series are the most
frequently applied ones. For noisy functions generally derivatives are not
available, so SRA is relying only on the function values.

SRA was tested on several problems, the related computer results were published
in a series of papers. These problems include solving a one-dimensional
equation, probabilistic constrained and two-stage stochastic programming
problems, including large-scale ones, a combined model of Prekopa, quadratic
stochastic programming problems. SRA is suitable for solving linear programming
problems with random technology matrix, too.

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