Inertial forward–backward methods for solving vector optimization problems Article Swipe

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Mathematics Vector optimization Conic optimization Banach space Cone (formal languages) Context (archaeology) Convex optimization Mathematical optimization Differentiable function Inertial frame of reference Proper convex function Convex function Regular polygon Hilbert space Optimization problem Subderivative Locally convex topological vector space Algorithm Mathematical analysis Multi-swarm optimization Paleontology Quantum mechanics Biology Geometry Physics

Radu Ioan Boţ , Sorin‐Mihai Grad ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.1080/02331934.2018.1440553 · OA: W2790326975

We propose two forward-backward proximal point type algorithms with inertial/memory effects for determining weakly efficient solutions to a vector optimization problem consisting in vector-minimizing with respect to a given closed convex pointed cone the sum of a proper cone-convex vector function with a cone-convex differentiable one, both mapping from a Hilbert space to a Banach one. Inexact versions of the algorithms, more suitable for implementation, are provided as well, while as a byproduct one can also derive a forward-backward method for solving the mentioned problem. Numerical experiments with the proposed methods are carried out in the context of solving a portfolio optimization problem.

Inertial forward–backward methods for solving vector optimization problems Article Swipe

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