YoVDO

A Fresh Look at Algorithms for Solving Smooth Multiobjective Optimization Problems

Offered By: Erwin Schrödinger International Institute for Mathematics and Physics (ESI) via YouTube

Tags

Algorithms Courses Linear Programming Courses Convex Optimization Courses Constrained Optimization Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a novel approach to solving smooth multiobjective optimization problems in this 24-minute conference talk from the "One World Optimization Seminar in Vienna" workshop. Delve into the construction of practical algorithms based on determining decreasing directions through linear programming problems. Examine the proposed iterative method for unconstrained, sign constrained, and linearly constrained multiobjective optimization problems. Learn how the objective function values sequence decreases with respect to the corresponding nonnegative orthant, and understand how accumulation points of the generated sequence become substationary points or weakly Pareto efficient solutions under convexity assumptions. Discover the advantages of this approach, which involves easily computable decreasing directions in polynomial time, setting it apart from similar algorithms in the literature. Gain insights from the collaborative work of Sorin-Mihai Grad, Tibor Illés, and Petra Renáta Rigó from the Corvinus Center for Operations Research at Corvinus Institute for Advanced Studies, Corvinus University of Budapest.

Syllabus

Sorin-Mihai Grad -A fresh look at algorithms for solving smooth multiobjective optimization problems


Taught by

Erwin Schrödinger International Institute for Mathematics and Physics (ESI)

Related Courses

Linear and Discrete Optimization
École Polytechnique Fédérale de Lausanne via Coursera
Linear and Integer Programming
University of Colorado Boulder via Coursera
Graph Partitioning and Expanders
Stanford University via NovoEd
Discrete Inference and Learning in Artificial Vision
École Centrale Paris via Coursera
Convex Optimization
Stanford University via edX