A filter-trust-region method for simple-bound constrained optimization

In this paper we propose a filter-trust-region algorithm for solving nonlinear optimization problems with simple bounds. It extends the technique of Gould et al. [Gould, N.I.M. Sainvitu, C. and Toint, Ph.L., 2005, A filter-trust-region method for unconstrained optimization. SIAM Journal on Optimization, 16(2), 341–357.] designed for unconstrained optimization problems. The two main ingredients of the method are a filter-trust-region algorithm and a gradient-projection method. The algorithm is shown to be globally convergent to at least one first-order critical point. Numerical experiments on a large set of problems are also reported.

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