Analytical Benchmark Problems for Multifidelity Optimization Methods

04/16/2022

∙

The paper presents a collection of analytical benchmark problems specifically selected to provide a set of stress tests for the assessment of multifidelity optimization methods. In addition, the paper discusses a comprehensive ensemble of metrics and criteria recommended for the rigorous and meaningful assessment of the performance of multifidelity strategies and algorithms.

READ FULL TEXT

Analytical Benchmark Problems for Multifidelity Optimization Methods

A Survey of Optimization Methods from a Machine Learning Perspective

Fuzzy Inference Systems Optimization

The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches

An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization

A Late Fusion Framework with Multiple Optimization Methods for Media Interestingness

Gradient-free Multi-domain Optimization for Autonomous Systems

A survey of deep learning optimizers-first and second order methods

Analytical Benchmark Problems for Multifidelity Optimization Methods

Related Research

A Survey of Optimization Methods from a Machine Learning Perspective

Fuzzy Inference Systems Optimization

The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches

An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization

A Late Fusion Framework with Multiple Optimization Methods for Media Interestingness

Gradient-free Multi-domain Optimization for Autonomous Systems

A survey of deep learning optimizers-first and second order methods