Deep learning the efficient frontier of convex vector optimization problems
From MaRDI portal
Publication:6618150
DOI10.1007/s10898-024-01408-xMaRDI QIDQ6618150
Birgit Rudloff, Zachary Feinstein
Publication date: 14 October 2024
Published in: Journal of Global Optimization (Search for Journal in Brave)
neural networksmachine learningefficient frontierdeep learningconvex vector optimizationconvex multi-objective optimization
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Survey of multi-objective optimization methods for engineering
- Primal and dual approximation algorithms for convex vector optimization problems
- An approximation algorithm for convex multi-objective programming problems
- An outer approximation algorithm for generating all efficient extreme points in the outcome set of a multiple objective linear programming problem
- Multilayer feedforward networks are universal approximators
- Proximity measures based on KKT points for constrained multi-objective optimization
- An approximation algorithm for multi-objective optimization problems using a box-coverage
- A recursive algorithm for multivariate risk measures and a set-valued Bellman's principle
- Approximation methods in multiobjective programming
- Stochastic Finance
- Vector Optimization
- Time Consistency of the Mean-Risk Problem
- A Benson-type algorithm for bounded convex vector optimization problems with vertex selection
- Option pricing: A simplified approach
- Generating all vertices of a polyhedron is hard
- Outer approximation algorithms for convex vector optimization problems
This page was built for publication: Deep learning the efficient frontier of convex vector optimization problems
