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Lecture

Convergence Criteria

In courses (2)

MATH-250: Advanced numerical analysis I

Construction and analysis of numerical methods for the solution of problems from linear algebra, integration, approximation, and differentiation. ...

MATH-251(c): Numerical analysis

Le cours présente des méthodes numériques pour la résolution de problèmes mathématiques comme des systèmes d'équations linéaires ou non linéaires, l'approximation de fonctions, l'intégration et la dérivation, et les équations différentielles. ...

Description

This lecture covers different convergence criteria used in optimization algorithms, such as stopping conditions based on the norm of the gradient and the change in the objective function. The instructor explains how these criteria help determine when to stop the optimization process and highlights the importance of paying attention to the criteria when dealing with large values. Various examples are provided to illustrate the application of convergence criteria in practice.

Instructors (3)

Daniel Kressner

Annalisa Buffa

Simone Deparis

Official source

Related concepts (15)

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Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

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