Pareto Optimization

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Pareto optimization studies how to find and represent solutions when improving one objective can worsen another, such as accuracy versus cost or fairness versus utility. Methods include weighted and Chebyshev scalarization, evolutionary and gradient-based front learning, Bayesian search, and preference-guided navigation; applications range from control and routing to machine learning and resource allocation. The goal is often to give decision-makers a useful set of trade-offs rather than one compromise solution. A central challenge is obtaining broad, reliable coverage of the Pareto front under computational, data, and feasibility constraints, since scalarization can miss non-convex regions and computing the full front may be expensive.

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40 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Computing the Exact Pareto Front in Average-Cost Multi-Objective Markov Decision Processes
Apr 2, 2026Jiping Luo, Nikolaos PappasMarkov Decision Processes Pareto Optimization
Department of Computer and Information Science, Linköping University, Linköping 58183, Sweden.
Approximating Pareto Frontiers in Stochastic Multi-Objective Optimization via Hashing and Randomization
Apr 1, 2026Jinzhao Li, Nan Jiang, Yexiang XueMulti-Objective Optimization Pareto Optimization
Department of Computer Science, Purdue University, 610 Purdue Mall, West Lafayette, 47907, IN, United States. · Department of Computer Science, University of Texas at El Paso, 500 W University Ave, El Paso, 79968, TX, United States.
Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning
Apr 1, 2026Paolo Speziali, Arno De Greef, Mehrdad Asadi +3Travel Planning Pareto Optimization
Vrije Universiteit Brussel Brussels, Belgium · Gemeente Amsterdam Amsterdam, Netherlands
Human-in-the-Loop Pareto Optimization: Trade-off Characterization for Assist-as-Needed Training and Performance Evaluation
Mar 24, 2026Harun Tolasa, Volkan PatogluHuman-in-the-Loop Evaluation Pareto Optimization
the Faculty of Engineering and Natural Sciences at Sabanci University, Istanbul, Turkiye.
Pareto-Optimal Anytime Algorithms via Bayesian Racing
Mar 9, 2026Jonathan Wurth, Helena Stegherr, Neele Kemper +2Algorithm Selection Pareto Optimization
Universität Augsburg, Augsburg, Germany
Post Hoc Extraction of Pareto Fronts for Continuous Control
Mar 3, 2026Raghav Thakar, Gaurav Dixit, Kagan TumerMulti-Objective Reinforcement Learning Pareto Optimization
The Collaborative Robotics and Intelligent Systems (CoRIS) Institute Oregon State University, Corvallis, Oregon, USA
Adaptive Combinatorial Experimental Design: Pareto Optimality for Decision-Making and Inference
Feb 27, 2026Hongrui Xie, Junyu Cao, Kan XuCombinatorial Multi-Armed Bandits Exploration-Exploitation Tradeoff
University of Science and Technology of China · The University of Texas at Austin · Arizona State University
Majoritarian Assignment Rules
Feb 16, 2026Felix Brandt, Haoyuan Chen, Chris Dong +2Fair Resource Allocation Social Choice Theory
Technical University of Munich, Munich, Germany · HPI, University of Potsdam, Potsdam, Germany · ILLC, University of Amsterdam, Amsterdam, The Netherlands
Jacobian Descent for Multi-Objective Optimization
Jun 23, 2024Pierre Quinton, Valérian ReyMulti-Objective Optimization Pareto Optimization
Independent
Python Tool for Visualizing Variability of Pareto Fronts over Multiple Runs
May 15, 2023Shuhei WatanabeMulti-Objective Optimization Pareto Optimization
Department of Computer Science, University of Freiburg, Germany

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Computing the Exact Pareto Front in Average-Cost Multi-Objective Markov Decision Processes

Jiping Luo, Nikolaos Pappas

Department of Computer and Information Science, Linköping University, Linköping 58183, Sweden.

Markov Decision Processes Pareto Optimization

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Many communication and control problems are cast as multi-objective Markov decision processes (MOMDPs). The complete solution to an MOMDP is the Pareto front. Much of the literature approximates this front via scalarization into single-objective MDPs. Recent work has begun to characterize the full front in discounted or simple bi-objective settings by exploiting its geometry. In this work, we characterize the exact front in average-cost MOMDPs. We show that the front is a continuous, piecewise-linear surface lying on the boundary of a convex polytope. Each vertex corresponds to a deterministic policy, and adjacent vertices differ in exactly one state. Each edge is realized as a convex combination of the policies at its endpoints, with the mixing coefficient given in closed form. We apply these results to a remote state estimation problem, where each vertex on the front corresponds to a threshold policy. The exact Pareto front and solutions to certain non-convex MDPs can be obtained without explicitly solving any MDP.