On q-steepest descent method for unconstrained multiobjective optimization problems
Por um escritor misterioso
Last updated 22 dezembro 2024
The <i>q</i>-gradient is the generalization of the gradient based on the <i>q</i>-derivative. The <i>q</i>-version of the steepest descent method for unconstrained multiobjective optimization problems is constructed and recovered to the classical one as <i>q</i> equals 1. In this method, the search process moves step by step from global at the beginning to particularly neighborhood at last. This method does not depend upon a starting point. The proposed algorithm for finding critical points is verified in the numerical examples.
On q-steepest descent method for unconstrained multiobjective optimization problems
Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging
Neural Architecture Search: Insights from 1000 Papers – arXiv Vanity
Full article: Complexity of gradient descent for multiobjective optimization
Nonlinear Conjugate Gradient Methods for Unconstrained Optimization 3030429490, 9783030429492
Single-Objective Optimization Problem - an overview
3. Consider the following unconstrained optimization
Constrained Engineering Optimization Algorithm Based on Elite Selection – topic of research paper in Mathematics. Download scholarly article PDF and read for free on CyberLeninka open science hub.
SciELO - Brasil - A SURVEY ON MULTIOBJECTIVE DESCENT METHODS A SURVEY ON MULTIOBJECTIVE DESCENT METHODS
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