A primal-dual nonlinear rescaling method with dynamic scaling parameter update

Igor Griva (igrivaprinceton.edu)
Roman Polyak (rpolyakgmu.edu)

Abstract: In this paper we developed a general primal-dual nonlinear rescaling method with dynamic scaling parameter update (PDNRD) for convex optimization. We proved the global convergence, established 1.5-Q-superlinear rate of convergence under the standard second order optimality conditions. The PDNRD was numerically implemented and tested on a number of nonlinear problems from COPS and CUTE sets. We present numerical results, which strongly corroborate the theory.

Keywords: Nonlinear rescaling, duality, Lagrangian, primal-dual, multipliers method

Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Technical Report SEOR-11-02, SEOR Department, George Mason University, Fairfax, VA 22030

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Entry Submitted: 07/21/2004
Entry Accepted: 07/21/2004
Entry Last Modified: 07/21/2004

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