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An Active Set Strategy for Solving Optimization Problems with up to 200,000,000 Nonlinear Constraints

Klaus Schittkowski(klaus.schittkowski***at***uni-bayreuth.de)

Abstract: We propose a numerical algorithm for solving smooth nonlinear programming problems with a large number of constraints, but a moderate number of variables. The active set method proceeds from a given bound mw for the maximum number of expected violated constraints, where mw is a user-provided parameter less than the total number of constraints. A quadratic programming subproblem is generated with mw linear constraints, the so-called working set, which are internally exchanged from one iterate to the next. Only for active constraints, i.e., a certain subset of the working set, new gradient values must be computed. The line search takes the active constraints into account. Numerical results for some simple academic test problems show that nonlinear programs with up to 200,000,000 nonlinear constraints can be efficiently solved on a standard PC.

Keywords: SQP; sequential quadratic programming; nonlinear programming; many constraints; active set strategy

Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Report, Department of Computer Science, University of Bayreuth, 95440 Bayreuth, Germany, 7/2008

Download: [PDF]

Entry Submitted: 02/13/2009
Entry Accepted: 02/13/2009
Entry Last Modified: 02/13/2009

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