Geometric Duality Results and Approximation Algorithms for Convex Vector Optimization Problems
From MaRDI portal
Publication:5883318
DOI10.1137/21M1458788MaRDI QIDQ5883318
Çağın Ararat, Unnamed Author, Firdevs Ulus
Publication date: 30 March 2023
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.07053
approximation algorithmmultiobjective optimizationscalarizationgeometric dualityhypervolume indicatorconvex vector optimization
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Equivalence between polyhedral projection, multiple objective linear programming and vector linear programming
- Set-valued average value at risk and its computation
- Hypervolume-based multiobjective optimization: theoretical foundations and practical implications
- A dual variant of Benson's ``outer approximation algorithm for multiple objective linear programming
- Primal and dual approximation algorithms for convex vector optimization problems
- A parametric simplex algorithm for linear vector optimization problems
- On duality in multiple objective linear programming
- An approximation algorithm for convex multi-objective programming problems
- Benson type algorithms for linear vector optimization and applications
- An augmented Lagrangian algorithm for multi-objective optimization
- Certainty equivalent and utility indifference pricing for incomplete preferences via convex vector optimization
- Approximately solving multiobjective linear programmes in objective space and an application in radiotherapy treatment planning
- Approximating the nondominated set of an MOLP by approximately solving its dual problem
- An outer approximation algorithm for generating all efficient extreme points in the outcome set of a multiple objective linear programming problem
- Finding all maximal efficient faces in multiobjective linear programming
- On generalized trade-off directions in nonconvex multiobjective optimization
- A set optimization approach to zero-sum matrix games with multi-dimensional payoffs
- The vector linear program solver Bensolve -- notes on theoretical background
- A vector linear programming approach for certain global optimization problems
- Scalarizing vector optimization problems
- A projected gradient method for vector optimization problems
- A norm minimization-based convex vector optimization algorithm
- Inexact projected gradient method for vector optimization
- A recursive algorithm for multivariate risk measures and a set-valued Bellman's principle
- Experiments with classification-based scalarizing functions in interactive multiobjective optimization
- Approximation methods in multiobjective programming
- Geometric Duality for Convex Vector Optimization Problems
- Graph Implementations for Nonsmooth Convex Programs
- Vector Optimization with Infimum and Supremum
- Adaptive Scalarization Methods in Multiobjective Optimization
- Newton's Method for Multiobjective Optimization
- Geometric Duality in Multiple Objective Linear Programming
- First- and Second-Order Epi-Differentiability in Nonlinear Programming
- Second Derivatives of a Convex Function and of Its Legendre–Fenchel Transformate
- A Branch--and--Bound-Based Algorithm for Nonconvex Multiobjective Optimization
- AN ALGORITHM FOR CALCULATING THE SET OF SUPERHEDGING PORTFOLIOS IN MARKETS WITH TRANSACTION COSTS
- Time Consistency of the Mean-Risk Problem
- A Benson-type algorithm for bounded convex vector optimization problems with vertex selection
- Solving Multiobjective Mixed Integer Convex Optimization Problems
- Geometric duality and parametric duality for multiple objective linear programs are equivalent
- A barrier-type method for multiobjective optimization
- Lectures on Convex Sets
- Frontiers of Stochastically Nondominated Portfolios
- Convex Analysis
- Benchmarking optimization software with performance profiles.
- Using multiobjective optimization to map the entropy region
This page was built for publication: Geometric Duality Results and Approximation Algorithms for Convex Vector Optimization Problems
