%R 10.1007/s10589-006-6447-z
%N 1
%J Computational Optimization and Applications
%D 2006
%L eprints492
%X For multiparametric convex nonlinear programming problems we propose a recursive algorithm for approximating, within a given suboptimality tolerance, the value function and an optimizer as functions of the parameters. The approximate solution is expressed as a piecewise affine function over a simplicial partition of a subset of the feasible parameters, and it is organized over a tree structure for efficiency of evaluation. Adaptations of the algorithm to deal with multiparametric semidefinite programming and multiparametric geometric programming are provided and exemplified. The approach is relevant for real-time implementation of several optimization-based feedback control strategies. 
%A Alberto Bemporad
%A Carlo Filippi
%I Springer
%V 35
%T An algorithm for approximate multiparametric convex programming
%P 87-108