Constrained Optimization

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The field studies how to optimize an objective over a feasible set, from convex programs and combinatorial problems to nonconvex models with complex or conflicting constraints. Classical approaches include projection and Frank–Wolfe methods, interior-point and augmented-Lagrangian algorithms, and evolutionary search; newer work also learns feasible solutions or uses neural projections and solver warm starts. Applications range from robotic trajectory planning and power-grid operation to fair machine learning, resource allocation, and scheduling. A central challenge is balancing scalable computation with reliable feasibility and convergence guarantees, especially for nonconvex or infeasible problems and settings without standard regularity conditions.

Latest papers

337 papers40 papers in the last four weeks, up 29% on the four weeks before. 0.2% of all new papers.

Breaking Exponential Complexity in Games of Ordered Preference: A Tractable Reformulation
Mar 27, 2026Dong Ho Lee, Jingqi Li, Lasse Peters +2Nash Equilibrium Computation Multi-Objective Optimization
Aerospace Engineering and Engineering Mechanics, University of Texas at Austin, Austin, TX 78712-1221 USA · Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX 78712-1221 USA · Department of Mechanical Engineering, UC Berkeley, Berkeley, CA 94720-1740 USA +1
Double Coupling Architecture and Training Method for Optimization Problems of Differential Algebraic Equations with Parameters
Mar 24, 2026Wenqiang Yang, Wenyuan Wu, Yong Feng +1Constrained Optimization Physics-Informed ML
Chongqing Key Laboratory of Automated Reasoning and Cognition, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Liangjiang District, 400714, Chongqing, China. · Chongqing School, University of Chinese Academy of Sciences, 266 Fangzheng Avenue, Liangjiang District, 400714, Chongqing, China.
Spectral-Sphere-Constrained Hyper-Connections
Mar 21, 2026Zhaoyi Liu, Haichuan Zhang, Ang LiHyper-Connections Residual Learning
University of Maryland, College Park · University of Utah
Breaking the $O(\sqrt{T})$ Cumulative Constraint Violation Barrier while Achieving $O(\sqrt{T})$ Static Regret in Constrained Online Convex Optimization
Mar 21, 2026Haricharan Balasundaram, Karthick Krishna Mahendran, Rahul VazeOnline Convex Optimization Constrained Optimization
Fast and Effective Computation of Generalized Symmetric Matrix Factorization
Mar 19, 2026Lei Yang, Han Wan, Min Zhang +1Matrix Factorization Nonconvex Optimization
School of Computer Science and Engineering, and Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University · School of Computer Science and Engineering, Sun Yat-sen University · School of Mathematics and Information Science, Guangzhou University +1
Constrained Hybrid Metaheuristic: A Universal Framework for Continuous Optimisation
Mar 18, 2026Piotr A. Kowalski, Szymon Kucharczyk, Jacek MańdziukMetaheuristic Optimization Black-Box Optimization
Faculty of Physics and Applied Computer Science, AGH University of Krakow, al. A. Mickiewicza 30, 30-059 Cracow, Poland. · Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland. · AGH Doctoral School, AGH University of Krakow, al. A. Mickiewicza 30, 30-059 Cracow, Poland. +2
UniHetCO: A Unified Heterogeneous Representation for Multi-Problem Learning in Unsupervised Neural Combinatorial Optimization
Mar 12, 2026Kien X. Nguyen, Ilya SafroCombinatorial Optimization Neural Combinatorial Optimization
Department of Computer and Information Sciences, University of Delaware, Newark DE 19716, USA
ADMM-based Continuous Trajectory Optimization in Graphs of Convex Sets
Mar 11, 2026Lukas Pries, Jon Arrizabalaga, Zachary Manchester +1Trajectory Optimization Alternating Direction Method of Multipliers
Autonomous Aerial Systems Lab, Dep. of Aerospace and Geodesy, TU Munich, Germany · Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, USA
Bayesian Optimization of Partially Known Systems using Hybrid Models
Mar 11, 2026Eike Cramer, Luis Kutschat, Oliver Stollenwerk +2Bayesian Optimization Constrained Optimization
University College London, Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, London WC1E 7JE, United Kingdom · RWTH Aachen University, Process Systems Engineering (AVT.SVT), Aachen 52074, Germany · University of Wisconsin–Madison, Department of Chemical & Biological Engineering, Madison, WI 53706, USA +2
A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
Mar 10, 2026Yuchen Fang, Jihun Kim, Sen Na +2Constrained Optimization Stochastic Optimization
Department of Mathematics, University of California, Berkeley · Department of IEOR, University of California, Berkeley · School of Industrial and Systems Engineering, Georgia Tech +1
Information Theoretic Bayesian Optimization over the Probability Simplex
Mar 10, 2026Federico Pavesi, Antonio Candelieri, Noémie JaquierBayesian Optimization Constrained Optimization
Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden · Department of Economics, Management and Statistics, University of Milan, Bicocca, Milan, Italy
First-Order Geometry, Spectral Compression, and Structural Compatibility under Bounded Computation
Mar 9, 2026Changkai LiConstrained Optimization Multi-Objective Optimization
Learning efficient representations of complex constraints for scalable optimization
Mar 9, 2026Yilin Wen, Yi Guo, Bo Zhao +4Representation Learning Constrained Optimization
School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing, 102206, China. · Department of Electrical Engineering, Tsinghua University, Haidian District, Beijing, 100084, China. · School of Automation, Beijing Institute of Technology, Haidian District, Beijing, 100081, China. +2
Local Constrained Bayesian Optimization
Mar 9, 2026Jing Jingzhe, Fan Zheyi, Szu Hui Ng +1Surrogate-Assisted Optimization Bayesian Optimization
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China · School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China · Department of Industrial Systems Engineering & Management, National University of Singapore, Singapore.
LIPP: Load-Aware Informative Path Planning with Physical Sampling
Mar 6, 2026Hojune Kim, Guangyao Shi, Gaurav S. SukhatmeInformative Path Planning Constrained Optimization
School of Electrical and Computer Engineering University of Southern California Los Angeles, California · School of Computer Science University of Southern California Los Angeles, California
Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels
Mar 5, 2026Khai Nguyen, Petros Ellinas, Anvita Bhagavathula +1Learning to Optimize Neural Surrogate Modeling
Massachusetts Institute of Technology, Cambridge, MA, USA · Technical University of Denmark, Kongens Lyngby, Denmark
Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
Mar 3, 2026Xindi Gong, Dingcheng Luo, Thomas O'Leary-Roseberry +2PDE Operator Learning Neural Operators
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA · School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia · Centre for Data Science, Queensland University of Technology, Brisbane, Australia +3
cuNRTO: GPU-Accelerated Nonlinear Robust Trajectory Optimization
Mar 3, 2026Jiawei Wang, Arshiya Taj Abdul, Evangelos A. TheodorouSecond-Order Cone Programming Trajectory Optimization
University of California, San Diego · Georgia Institute of Technology, Atlanta
FAST-DIPS: Adjoint-Free Analytic Steps and Hard-Constrained Likelihood Correction for Diffusion-Prior Inverse Problems
Mar 2, 2026Minwoo Kim, Seunghyeok Shin, Hongki LimDiffusion-Based Inverse Problems Inverse Problems
Department of Electrical and Computer Engineering, Inha University Incheon, 22212, South Korea
GPU-friendly and Linearly Convergent First-order Methods for Certifying Optimal $k$ -sparse GLMs
Mar 1, 2026Jiachang Liu, Andrea Lodi, Soroosh ShafieeConstrained Optimization Generalized Linear Models
Center for Data Science for Enterprise and Society, Cornell University, Ithaca, USA · School of Operations Research and Information Engineering, Cornell University, Ithaca, USA · Jacobs Technion-Cornell Institute, Cornell Tech and Technion–IIT, New York, USA
Hippo: High-performance Interior-Point and Projection-based Solver for Generic Constrained Trajectory Optimization
Mar 1, 2026Haizhou Zhao, Ludovic Righetti, Majid KhadivTrajectory Optimization Constrained Optimization
Tandon School of Engineering, New York University, USA · Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany
A distributed semismooth Newton based augmented Lagrangian method for distributed optimization
Feb 27, 2026Qihao Ma, Chengjing Wang, Peipei Tang +2Distributed Optimization Augmented Lagrangian Method
School of Mathematics, Southwest Jiaotong University, No.999, Xian Road, West Park, High-tech Zone, Chengdu 611756, China · School of Computer and Computing Science, Hangzhou City University, Zhejiang Key Laboratory of Big Data Intelligent Computing, Hangzhou, 310015, China · Department of Control Science and Engineering, College of Electronics and Information Engineering, National Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University, Shanghai 201804, China +1
Zeroth-Order Stackelberg Control in Combinatorial Congestion Games
Feb 26, 2026Saeed Masiha, Sepehr Elahi, Negar Kiyavash +1Frank-Wolfe Algorithms Stackelberg Games
EPFL School of Management of Technology · EPFL Department of Computer and Communications Sciences
Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits
Feb 26, 2026Moirangthem Tiken Singh, Amit Kalita, Sapam Jitu SinghAlgorithmic Fairness Fair Resource Allocation
Department of Computer Science and Engineering, DUIET, Dibrugarh University, Assam, India · Department of Computer Science and Engineering, MIT, Manipur University, 795003, India
ConstraintBench: Benchmarking LLM Constraint Reasoning on Direct Optimization
Feb 25, 2026Joseph Tso, Preston Schmittou, Quan Huynh +1Constraint-Guided Reasoning in Language Models Constrained Optimization
Haladir Research Team
Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual
Feb 25, 2026Yining Li, Peizhong Ju, Ness ShroffLLM Safety Alignment RL from Human Feedback
The Ohio State University · University of Kentucky
Unified Complementarity-Based Contact Modeling and Planning for Soft Robots
Feb 24, 2026Milad Azizkhani, Yue ChenSoft Robotics Contact-Rich Robotic Manipulation
Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA · Institute for Robotics and Intelligent Machines (IRIM), Georgia Institute of Technology, Atlanta, GA, USA · Coulter Department of Biomedical Engineering (Georgia Tech and Emory University), Georgia Institute of Technology, Atlanta, GA, USA
Precedence-Constrained Decision Trees and Coverings
Feb 24, 2026Michał Szyfelbein, Dariusz DereniowskiCombinatorial Optimization Constrained Optimization
Gdańsk University of Technology, 80-233 Gdańsk, Poland
Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints
Feb 24, 2026Mohammad S. Alkousa, Fedor S. Stonyakin, Belal A. Alashqar +1Mirror Descent Constrained Optimization
Innopolis University, Innopolis, Universitetskaya Str., 1, 420500, Russia. · Moscow Institute of Physics and Technology, 9 Institutsky lane, Dolgoprudny, 141701, Russia. · V. I. Vernadsky Crimean Federal University, 4 Academician Vernadsky Avenue, Simferopol, 295007, Republic of Crimea, Russia.
Multi-Agent Temporal Logic Planning via Penalty Functions and Block-Coordinate Optimization
Feb 19, 2026Eleftherios E. Vlahakis, Arash Bahari Kordabad, Lars Lindemann +3STL Planning Multi-Agent Planning
the Division of Computer Engineering and Computer Science, University West, 46132, Trollhättan, Sweden · the Max Planck Institute for Software Systems, Kaiserslautern, Germany · the Automatic Control Laboratory, ETH Zürich, 8092, Zürich, Switzerland +2

Mar 27, 2026J/K move · Enter open · S save

Breaking Exponential Complexity in Games of Ordered Preference: A Tractable Reformulation

Dong Ho Lee, Jingqi Li, Lasse Peters, Georgios Bakirtzis+1

Aerospace Engineering and Engineering Mechanics, University of Texas at Austin, Austin, TX 78712-1221 USA · Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX 78712-1221 USA · Department of Mechanical Engineering, UC Berkeley, Berkeley, CA 94720-1740 USA+1

Nash Equilibrium Computation Multi-Objective Optimization Constrained Optimization

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Games of ordered preference (GOOPs) model multi-player equilibrium problems in which each player maintains a distinct hierarchy of strictly prioritized objectives. Existing approaches solve GOOPs by deriving and enforcing the necessary optimality conditions that characterize lexicographically constrained Nash equilibria through a single-level reformulation. However, the number of primal and dual variables in the resulting KKT system grows exponentially with the number of preference levels, leading to severe scalability challenges. We derive a compact reformulation of these necessary conditions that preserves the essential primal stationarity structure across hierarchy levels, yielding a "reduced" KKT system whose size grows polynomially with both the number of players and the number of preference levels. The reduced system constitutes a relaxation of the complete KKT system, yet it remains a valid necessary condition for local GOOP equilibria. For GOOPs with quadratic objectives and linear constraints, we prove that the primal solution sets of the reduced and complete KKT systems coincide. More generally, for GOOPs with arbitrary (but smooth) nonlinear objectives and constraints, the reduced KKT conditions recover all local GOOP equilibria but may admit spurious non-equilibrium solutions. We introduce a second-order sufficient condition to certify when a candidate point corresponds to a local GOOP equilibrium. We also develop a primal-dual interior-point method for computing a local GOOP equilibrium with local quadratic convergence. The resulting framework enables scalable and efficient computation of GOOP equilibria beyond the tractable range of existing exponentially complex formulations.