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.

Flow-Corrected Shape Optimization: Taming Manifold Drift in High-Dimensional 3D Models
Aug 7, 2026Emilien Seiler, Nicolas Talabot, Yingxuan You +2Constrained Generative Modeling 3D Shape Generation
CVLab, EPFL
Muon on the Stiefel Manifold Admits an Exact Closed-Form Update
Aug 6, 2026Mikhail Solonko, Molozhavenko Alexander, Maxim RakhubaStiefel Manifold Optimization Riemannian Optimization
HSE University, Moscow, Russia.
Differentiating Through Dual Prices: End-to-End Policy Learning Under Capacity Constraints
Aug 5, 2026Mohammadsaeed Haghi, Mahdi Salmani, Nima Kelidari +2Constrained Optimization Differentiable Optimization
University of Southern California
CARGO-VL: Counterfactual Arbitration with Risk-Constrained Group Optimization for Vision-Language Models
Aug 5, 2026De Jiang, Zhengyang Zhang, Kehong Yuan +1VLM Robustness Counterfactual Evaluation of VLMs
LaPrune: Controllable Differentiable Sparsity at Million Scale
Aug 4, 2026Jakub Antczak, Joanna Wojciechowicz, Łukasz Struski +1Constrained Optimization
Wrocław University of Science and Technology, Wrocław, Poland · Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland
Exploiting Separability in Multi-Scale Grey-Box Bayesian Optimization
Aug 4, 2026Joshua E. Hammond, Tyler A. Soderstrom, Brian A. Korgel +1Bayesian Optimization Bilevel Optimization
McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E. Dean Keaton St. Stop C0400, Austin, Texas 78712, USA · ExxonMobil Technology and Engineering, 22777 Springwoods Village Pkwy, Spring, Texas 77389, USA · Energy Institute, The University of Texas at Austin, 2304 Whitis Ave. Stop C2400, Austin, Texas 78712, USA +1
MixedComplementarityProblems.jl: A Fast, Batched, Open-Source Interior Point Solver for Mixed Complementarity Problems
Aug 2, 2026David Fridovich-KeilConstrained Optimization
Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin, TX, USA
Parameterized Fair Resource Allocation under Diversity Constraints
Jul 29, 2026Keke Huang, Yik Yu Ng, Laks V. S. Lakshmanan +1Fair Resource Allocation Group Fairness
Huazhong University of Science and Technology Wuhan, Hubei, China · The University of British Columbia Vancouver, BC, Canada · McGill University Montreal, Quebec, Canada +1
Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling
Jul 28, 2026Itai Zilberstein, Pranav Rajbhandari, Steve Chien +1Distributed Optimization Potential Games
Department of Computer Science, Carnegie Mellon University · Jet Propulsion Laboratory, California Institute of Technology · Additional affiliations: Strategy Robot, Inc., Strategic Machine, Inc., Optimized Markets, Inc.
Optimization with Dynamic Constraint Learning (DCL)
Jul 28, 2026Ezgi Oztekin, Figen Oztoprak, S. Ilker BirbilSurrogate-Assisted Optimization Black-Box Optimization
Department of Mathematics, Gebze Technical University · Department of Industrial Engineering, Gebze Technical University · Amsterdam Business School, University of Amsterdam
Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization
Jul 28, 2026Wentao ZhangOnline Convex Optimization Constrained Optimization
Tsinghua Shenzhen International Graduate School · Tsinghua University
Balancing multiscale similarity and cartographic constraints: A similarity-driven optimization framework for line generalization
Jul 28, 2026Pengbo Li, Haowen Yan, Xiaomin Lu +1Spatial Generalization Constrained Optimization
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China · National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China · Key Laboratory of Science and Technology in Surveying & Mapping, Lanzhou 730070, China
Beyond Single-Episode Optimization: Sliding-Window Aware Generative Auto-Bidding for Long-Term Advertising Effectiveness
Jul 28, 2026Binglin Wu, Chuan Yue, Yingyi Zhang +4Automated Bidding Online Advertising
Dalian University of Technology · Alibaba International Digital Commerce Group · Dalian University of Technology & City University of Hong Kong
Smooth Learning with Hard Constraints via Legendre-Regularized Policies
Jul 27, 2026Zikun Lin, Rui Chen, Yijie WangDecision-Focused Learning Constrained Optimization
Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China. · School of Data Science, The Chinese University of Hong Kong, Shenzhen, Guangdong, China. · School of Economics and Management, Tongji University, Shanghai, China.
A Multi-stage Constrained Optimization Framework for Data-driven Problems
Jul 26, 2026Ye ShiVariational Autoencoders Constrained Generative Modeling
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
Constraint-Bound Agnostic Bayesian Optimization: One Model for All Thresholds
Jul 26, 2026Jin Wang, Xi Lin, Handing WangBayesian Optimization Constrained Optimization
School of Artificial Intelligence, Xidian University, China · School of Mathematics and Statistics, Xi’an Jiaotong University, China
FMOPF: Latent Flow Matching with Constraint-Aware Interaction Priors for AC Optimal Power Flow
Jul 24, 2026Zhilin HuangConstrained Generative Modeling Flow Matching
China Southern Power Dispatching and Control Center, China Southern Power Grid
Online Optimization of Difference-of-Convex Compositions with Smooth Mappings
Jul 21, 2026Jingwei Ji, Jong-Shi Pang, Renyuan XuNonconvex Optimization Constrained Optimization
Management Science and Engineering, Stanford University · The Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California
Stronger Memory-Query Tradeoffs for Convex Optimization: The Limitations of Subquadratic Memory
Jul 21, 2026Michael Menart, Aleksandar Nikolov, Ohad ShamirQuery Complexity Oracle Complexity
Department of Computer Science, University of Toronto, Vector Institute · Department of Computer Science, University of Toronto · Weizmann Institute of Science and University of Toronto +1
An Iterative Geometric Approach to Optimizing Separating Hyperplanes
Jul 19, 2026Akos HajnalSupport Vector Machines Constrained Optimization
Laboratory of Parallel and Distributed Systems Institute for Computer Science and Control (SZTAKI) · John von Neumann Faculty of Informatics Obuda University Budapest, Hungary
Constrained Path Reasoning: Measuring When Committed Stages Earn Their Cost
Jul 19, 2026Honglin LiLLM Inference Efficiency Inference-Time Search
ShanghaiTech University
Certified-Gap Dual-Price Policies for Real-Time Truckload Bid Acceptance with Relocating, Clock-Constrained Resources
Jul 18, 2026Aswin ChandrasekaranReal-Time Scheduling Intelligent Transportation Systems
Bubba AI
Hybrid Augmented Lagrangian Method for General Constrained Optimization via Evolutionary Algorithms
Jul 18, 2026Lampros Printzios, Konstantinos ChatzilygeroudisEvolutionary Optimization Augmented Lagrangian Method
Laboratory of Automation & Robotics (LAR), Department of Electrical & Computer Engineering, University of Patras, University Campus, Patras, GR-26504, Achaia, Greece
Agentic Calibration of Grey-Box Simulation Models: An LLM-Driven Alternative
Jul 17, 2026David Gómez-Guillén, Mireia Diaz, Josep Lluis Arcos +1Parameter Estimation Black-Box Optimization
Unit of Infections and Cancer – Information and Interventions (UNIC-I&I), Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL, L’Hospitalet de Llobregat, Spain · Autonomous University of Barcelona (UAB), Spain · Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Ministerio de Ciencia, Innovación y Universidades, Madrid, Spain +1
Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems
Jul 15, 2026Dhruv Shivkant, Saket Mohanty, Utkarsh WadhwaModel Compression Constrained Optimization
Indian Institute of Science · EXL
Safe Overtaking for Autonomous Racing Using Hierarchical Optimization and Learning-Based Control
Jul 15, 2026Hassan Jardali, Kai Yin, Lantao LiuNonlinear MPC Control Barrier Functions
Luddy School of Informatics, Computing, and Engineering, Indiana University · Machine Learning Science, Expedia Group, Austin, TX 78758 USA
Globalized Constrained Stein Variational Inference for Diverse Feasible Robot Motion Planning
Jul 14, 2026Jiayun Li, Georgia ChalvatzakiConstrained Motion Planning Constrained Optimization
PEARL Lab, Dept. of Computer Science, TU Darmstadt. · Hessian.AI, Darmstadt, Germany. · Robotics Institute Germany (RIG).
A new dual-population constrained multi-objective evolutionary optimization algorithm with repair constraint handling for structural optimization
Jul 14, 2026Fardad Homafar, Jasmin JelovicaEvolutionary Optimization Multi-Objective Evolutionary Optimization
Department of Mechanical Engineering, The University of British Columbia, 6250 Applied Science Ln, Vancouver, V6T 1Z4, Canada · Department of Civil Engineering, The University of British Columbia, 6250 Applied Science Ln, Vancouver, V6T 1Z4, Canada
Incremental Transformer for Surrogate-Based Inverse Design of Geopolymer Mixtures
Jul 12, 2026Giansalvo Cirrincione, Filippo GrassiaSurrogate Modeling Materials Property Prediction
Laboratory of Innovative Technologies (LTI, EA 3899) University of Picardie Jules Verne, Amiens, 80025, Hauts-de-France, France
Lower Bound on the Cumulative Constrained Violation for the OGD+Projection algorithm for Constrained Online Convex Optimization (COCO)
Jul 12, 2026Haricharan Balasundaram, Karthick Krishna Mahendran, Rahul VazeOnline Convex Optimization Constrained Optimization

Aug 7, 2026J/K move · Enter open · S save

Flow-Corrected Shape Optimization: Taming Manifold Drift in High-Dimensional 3D Models

Emilien Seiler, Nicolas Talabot, Yingxuan You, Federico Stella+1

CVLab, EPFL

Constrained Generative Modeling 3D Shape Generation Constrained Optimization

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Optimizing 3D shapes within the latent spaces of deep generative models is fundamental to computer assisted engineering, yet remains prone to a critical failure mode we term manifold drift: the tendency of gradient-based optimization to move latent vectors away from the manifold of valid shapes. This problem is exacerbated in state-of-the-art 3D shape generative models that operate in increasingly high-dimensional latent spaces where valid shapes occupy a vanishingly small fraction of the full space. Existing mitigation strategies, including latent regularization and flow-matching approaches, either sacrifice expressiveness, demand a difficult trade-off between objective guidance and generative fidelity that remains prone to manifold drift, or are computationally infeasible to scale to modern, large-capacity 3D shape models. We introduce a novel optimizer-corrector framework that alternates between gradient steps for objective minimization and guided flow matching to drive the latent state back to the valid shape manifold. By decoupling objective minimization from flow-based correction, optimizing freely and correcting strictly, this alternating design avoids inherent trade-offs, preserving geometric validity without sacrificing expressiveness while remaining computationally feasible on modern 3D shape models. We demonstrate its effectiveness across generative priors of varying complexity, from simple vector latent spaces to large-scale architectures across a variety of downstream optimization tasks, including aerodynamic drag reduction and object compliance optimization.