A Primal-Dual Algorithm for Minimizing a Non-Convex Function Subject to Bound and Linear Equality Constraints

        A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to simple bounds and linear equality constraints. The method alternates between a classical primal-dual step and a Newton-like step in order to ensure descent on a suitable merit function. Convergence of a well-defined subsequene of iterates is proved from arbitrary starting points. Algorithmic variants are discussed and preliminary numerical results presented.

By: A. R. Conn, Nicholas I. M. Gould (Rutherford Appleton Lab., England) and Ph. L. Toint (Univ. ND de la Paix, Belgium)

Published in: RC20639 in 1996

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