On q-steepest descent method for unconstrained multiobjective optimization problems
Por um escritor misterioso
Last updated 20 junho 2024
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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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Geometry optimization - Schlegel - 2011 - WIREs Computational Molecular Science - Wiley Online Library
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Mathematics, Free Full-Text
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ICLR2022 Statistics
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X Brazilian Workshop on Continuous Optimization IMPA - Instituto de Matemática Pura e Aplicada
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Neural optimization machine: a neural network approach for optimization and its application in additive manufacturing with physics-guided learning
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Fractal Fract, Free Full-Text
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Equality-Constrained Engineering Design Optimization Using a Novel Inexact Quasi-Newton Method
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SciELO - Brasil - A SURVEY ON MULTIOBJECTIVE DESCENT METHODS A SURVEY ON MULTIOBJECTIVE DESCENT METHODS
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Solving Optimization Problems using the Matlab Optimization
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Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization
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