Relaxed-inertial proximal point type algorithms for quasiconvex minimization

S.-M. Grad (), F. Lara () and R. T. Marcavillaca ()
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S.-M. Grad: ENSTA Paris, Polytechnic Institute of Paris
F. Lara: Universidad de Tarapacá
R. T. Marcavillaca: Universidad de Tarapacá

Journal of Global Optimization, 2023, vol. 85, issue 3, No 4, 615-635

Abstract: 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.

Keywords: Proximal point algorithms; Relaxed methods; Inertial methods; Generalized convexity; Strong quasiconvexity (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10898-022-01226-z

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