On q-steepest descent method for unconstrained multiobjective optimization problems

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.

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Mathematical optimization - Wikiwand

Neural optimization machine: a neural network approach for optimization and its application in additive manufacturing with physics-guided learning

SciELO - Brasil - A SURVEY ON MULTIOBJECTIVE DESCENT METHODS A SURVEY ON MULTIOBJECTIVE DESCENT METHODS

A hybrid conjugate gradient method for optimization problems

Nonlinear Conjugate Gradient Methods for Unconstrained Optimization 3030429490, 9783030429492

PDF] Newton's Method for Multiobjective Optimization

Classification of optimization Techniques

Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization - ScienceDirect

What Is Optimization Toolbox?