In this paper, a class of algorithms is developed for bound-constrained optimization. The new scheme uses the gradient-free line search along bent search paths. Unlike traditional algorithms for bound-constrained optimization, our algorithm ensures that the reduced gradient becomes arbitrarily small. It is also proved that all strongly active variables are found and fixed after finitely many iterations. A Matlab implementation of a bound-constrained solver LMBOPT based on the new theory was discussed by the present authors in a companion paper (Math. Program. Comput. 14 (2022), 271–318).
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