Abstract
We propose a relaxed-inertial proximal point type algorithm for solving optimization problems consisting in minimizing strongly quasiconvex functions whose variables lie in finitely dimensional linear subspaces. A relaxed version of the method where the constraint set is only closed and convex is also discussed, and so is the case of a quasiconvex objective function. Numerical experiments illustrate the theoretical results.
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The datasets generated during and/or analysed during the current study are available from S.-M. Grad on reasonable request.
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This research was partially supported by ANID–Chile under project Fondecyt Regular 1220379 (Lara), and by a CIAS Senior Research Fellow Grant of the Corvinus Institute for Advanced Studies (Grad).
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Grad, SM., Lara, F. & Marcavillaca, R.T. Relaxed-inertial proximal point type algorithms for quasiconvex minimization. J Glob Optim 85, 615–635 (2023). https://doi.org/10.1007/s10898-022-01226-z
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DOI: https://doi.org/10.1007/s10898-022-01226-z