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KNITRO-Direct: A Hybrid Interior Algorithm for Nonlinear Optimization

Richard Waltz (rwaltz***at***ece.northwestern.edu)
Jose Luis Morales (moales***at***ece.northwestern.edu)
Jorge Nocedal (nocedal***at***ece.northwestern.edu)
Dominique Orban (orban***at***ece.northwestern.edu)

Abstract: A hybrid interior-point method for nonlinear programming is presented. It enjoys the flexibility of switching between a line search based method which computes steps by factoring the primal-dual equations and an iterative method using a conjugate gradient algorithm and globalized by means of trust regions. Steps computed by a direct factorization are always tried first, but if they are deemed to be ineffective, a trust region iteration that guarantees progress toward stationarity is invoked. To demonstrate its effectiveness, the algorithm is implemented in the KNITRO software package and extensively tested on a selection of problems from the CUTEr test

Keywords: nonlinear programming, interior methods

Category 1: Nonlinear Optimization

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: report OTC 2003/10, Optimization Technology Center, Northwestern University

Download: [Compressed Postscript][PDF]

Entry Submitted: 08/13/2003
Entry Accepted: 08/13/2003
Entry Last Modified: 08/14/2003

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