Global Convergence Analysis of Algorithms for Finding Feasible Points in Norm-Relaxed MFD

Been-Lon Chen and M. M. Kostreva
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M. M. Kostreva: Clemson University

Journal of Optimization Theory and Applications, 1999, vol. 100, issue 2, No 3, 287-309

Abstract: Abstract This paper introduces two new algorithms for finding initial feasible points from initial infeasible points for the recently developed norm-relaxed method of feasible directions (MFD). Their global convergence is analyzed. The theoretical results show that both methods are globally convergent; one of them guarantees finding a feasible point in a finite number of steps. These two methods are very convenient to implement in the norm-relaxed MFD. Numerical experiments are carried out to demonstrate their performance on some classical test problems and to compare them with the traditional method of phase I problems. The numerical results show that the methods proposed in this paper are more effective than the method of phase I problems in the norm-relaxed MFD. Hence, they can be used for finding initial feasible points for other MFD algorithms and other nonlinear programming methods.

Keywords: Method of feasible directions; direction-finding subproblem; global convergence; feasible points; nonlinear programming (search for similar items in EconPapers)
Date: 1999
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DOI: 10.1023/A:1021778002066

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