Global Optimization

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Global optimization seeks the best solution over an entire search space, including nonconvex landscapes with many local minima, costly evaluations, noise, or constraints. Methods range from branch-and-bound and convex relaxations that provide certificates to Bayesian optimization and hierarchical partitioning for black-box functions, as well as evolutionary, annealing, and learned search strategies. Applications include control, vision and pose estimation, clustering, engineering design, and combinatorial problems, evaluated by solution quality, feasibility, computational cost, and recovery of multiple optima. A central tension is that rigorous guarantees can be costly in high dimensions, while scalable heuristics may find strong candidates without proving global optimality; initialization, reachable regions, and benchmark realism also affect such claims.

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36 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

WCC: A Learning-Based Framework for Deciding When to Apply Cooperative Coevolution in Large-Scale Optimization
Oct 27, 2026Wenjie Qiu, Hepei Li, Jianfeng Wang +2Global Optimization Evolutionary Optimization
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China · Taiyuan University of Technology, Taiyuan 030024, China · Nankai University, Tianjin 300350, China, and also with Hanyang University ERICA, Ansan 15588, South Korea
Convergence Rates of Gaussian Process Thompson Sampling for Efficient Global Optimization
Oct 14, 2026Haowei Wang, Yilin ZhengThompson Sampling Global Optimization
National University of Singapore Singapore
Global Optimality of Pose Graph Optimization
Oct 10, 2026Shuan Ding, Ruishuang Chen, Mengfan Cao +1Pose Graph Optimization Robotic Optimization
Guangdong Provincial Key Laboratory of FullyActuated System Control Theory and Technology, and the School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, China · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
A practical DIRECT-type algorithm for medium-scale black-box global optimization
Sep 9, 2026Linas Stripinis, Remigijus PaulavičiusBlack-Box Optimization Global Optimization
Vilnius University Institute of Data Science and Digital Technologies, Akademijos 4, Vilnius, Lithuania, LT-08663
Fast PAC Global Optimization via Restarted Langevin: Exploration, Exploitation, and Degenerate Cooling
Sep 5, 2026Ioannis Kontoyiannis, Sean MeynGlobal Optimization Langevin Dynamics
Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WB, UK · Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA
AlgoWorlds: Benchmarking Tool Use for Global Optimization in Algorithmic Worlds
Aug 29, 2026Zixiang Xu, Jiaan Wang, Fandong MengAI Agent Benchmarks Global Optimization
Weixin AI, Tencent · University of Southern California
Coronavirus Optimization Algorithm: A Success-History Adaptive Evolutionary Framework with Archive-Assisted Search and Stagnation Recovery for Global Optimization
Aug 24, 2026Hari Mohan PandeyEvolutionary Optimization Global Optimization
Department of Computing & Engineering Bournemouth University UK
Reachable Global Optimization in AI Systems: How Global Is Global?
Aug 20, 2026Wesley ShuGlobal Optimization Reachability Analysis
The Institute of Energetic Paradigm
Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks
Aug 13, 2026Wojciech Zarzecki, Jarosław ArabasEvolutionary Optimization Benchmark Design
Warsaw University of Technology, Institute of Computer Science
Data-Native Global Optimization for Big Data K-means Clustering
Jul 17, 2026Ravil Mussabayev, Rustam Mussabayev, Zukhra Yerdaliyeva +1Clustering Unsupervised Clustering
AI Research Lab, Satbayev University, Almaty, 050000, Kazakhstan · Laboratory for Analysis and Modeling of Information Processes, Institute of Information and Computational Technologies, Almaty, 050010, Kazakhstan
How Many Initial Points Does Bayesian Optimization Need?
Jul 5, 2026Mujin Cheon, James Odgers, Dong-Yeun Koh +1Bayesian Optimization Global Optimization
Korea Advanced Institute of Science & Technology (KAIST) · HayanMind Inc. · Technical University of Nuremberg (UTN) +3
DE-2LS: Differential Evolution with Late-Stage local-search for Unconstrained Single-Objective Numerical Optimization
Jun 26, 2026Dikshit ChauhanDifferential Evolution Evolutionary Optimization
Department of Electrical and Computer Engineering, National University of Singapore
Random Walk on Bézier Curves for Global Optimization
Jun 25, 2026Jinpeng Wang, Xingguo Xu, Yujing Sun +3Stochastic Optimization Evolutionary Optimization
aSchool of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China · bSchool of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China · cSchool of Mathematics, Hefei University of Technology, Hefei, 230601, China +3
\chisao{}: A GPU-Native Parallel Optimizer for Multimodal Black-Box Functions via Convergence-Anticonvergence Oscillation
Jun 24, 2026Ira WolfsonGPU Acceleration Black-Box Optimization
Department of Electronics and Electrical Engineering Braude College of Engineering, Karmiel, Israel
GLENS: Global Search via Learning from Solver Iterates with Diffusion Models
May 29, 2026Anjian Li, Bartolomeo Stellato, Ryne BeesonLearning to Optimize Diffusion Sampling
Department of Electrical and Computer Engineering, Princeton University, Princeton, 08544, NJ, USA. · Department of Operations Research and Financial Engineering, Princeton University, Princeton, 08544, NJ, USA. · Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, 08544, NJ, USA.
Probabilistic Smoothing with Ratio-Monotone Transforms for Global Optimization
May 26, 2026Kukyoung Jang, Taehyun Cho, Junrui Zhang +2Black-Box Optimization Global Optimization
Department of Statistics, Seoul, Korea University · Department of Electrical and Computer Engineering, Seoul, Seoul National University · Shandong University at Weihai, Weihai, China.
From Mean-Field Limits to Semiclassical Concentration: Global Convergence of the Canonical Evolutionary Strategy
May 19, 2026Matías Neto, Nicolás Garay, Luis Martí +1Dynamical Systems Evolutionary Optimization
Inria Chile Research Center, Av Apoquindo 2827, Las Condes, RM, Chile · Departamento de Ingeniería Matemática, Universidad de Chile, Santiago, Chile
Proximal basin hopping: global optimization with guarantees
May 18, 2026Guillaume Lauga, Cesare Molinari, Samuel VaiterProximal Optimization Global Optimization
LJAD · LJAD Université Côte d’Azur France · MaLGA +3
Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
May 15, 2026Zeji Yi, Chaoyi Pan, Guanya Shi +1Optimization Convergence Analysis Black-Box Optimization
Carnegie Mellon University
Stochastic global optimization of continuous functions via random walks on Grassmannians
May 13, 2026Kartik Gupta, Stephen D. Miller, Pradeep Ravikumar +1Stochastic Optimization Subspace Optimization
RDEx-CASK: Cauchy Mutation, Archive, and Stagnation Kick for RDEx-CSOP
May 10, 2026Dikshant, Dikshit Chauhan, Chen Hao +3Differential Evolution Evolutionary Optimization
Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
May 8, 2026Hao Chen, Chendi Qian, Christopher Morris +2Max-Cut Semidefinite Programming
1Davidson School of Chemical Engineering, Purdue University · Faculty of Computer Science, RWTH Aachen University · 3Jacobs Technion-Cornell Institute, Cornell Tech
Bandits attack function optimization
May 5, 2026Philippe Preux, Rémi Munos, Michal ValkoBlack-Box Optimization Global Optimization
Université de Lille, LIFL (UMR 8022 CNRS), and INRIA, France · INRIA, and LIFL, Lille, France
Black-box optimization of noisy functions with unknown smoothness
May 4, 2026Jean-Bastien Grill, Michal Valko, Rémi MunosBlack-Box Optimization Zeroth-Order Optimization
SequeL team, INRIA Lille - Nord Europe, France · Google DeepMind, UK∗
Stochastic simultaneous optimistic optimization
Apr 27, 2026Michal Valko, Alexandra Carpentier, Rémi MunosStochastic Optimization Multi-Armed Bandits
INRIA Lille - Nord Europe, SequeL team, 40 avenue Halley 59650, Villeneuve d’Ascq, France · Statistical Laboratory, CMS, Wilberforce Road, CB3 0WB, University of Cambridge, United Kingdom
An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions
Apr 21, 2026Wei-Ting Tang, Akshay Kudva, Calvin Tsay +1Bayesian Optimization Global Optimization
Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, 53706, Wisconsin, USA. · Department of Chemical and Biomolecular Engineering, The Ohio State University, 151 W. Woodruf Avenue, Columbus, 43210, Ohio, USA. · Department of Computing, Imperial College London, Exhibition Rd, South Kensington, SW7 2AZ, London, United Kingdom.
Classical and Quantum Speedups for Non-Convex Optimization via Energy Conserving Descent
Apr 14, 2026Yihang Sun, Huaijin Wang, Patrick Hayden +1Nonconvex Optimization Global Optimization
Department of Management Science and Engineering, Stanford University. · Leinweber Institute for Theoretical Physics, Stanford University. · Leinweber Institute for Theoretical Physics, Stanford University and Google DeepMind.
Globally Optimal Pose from Orthographic Silhouettes
Apr 10, 2026Agniva Sengupta, Dilara Kuş, Jianning Li +1Object Pose Estimation Global Optimization
Freie Universität Berlin · Zuse Institute Berlin
One-Shot Localisation of the Global Minimum of a Noisy One-Dimensional Function: A Controlled Comparison of an Iterative Neural Minimizer, Set Transformers and Classical Estimators
Apr 4, 2026Qusay Muzaffar, David Levin, Michael WermanBlack-Box Optimization Zeroth-Order Optimization
Department of Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel · Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel
A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization
Mar 30, 2026Wenjie Qiu, Zixin Wang, Hongyu Fang +2Global Optimization Evolutionary Optimization
South China University of Technology, Guangzhou, Guangdong, China

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WCC: A Learning-Based Framework for Deciding When to Apply Cooperative Coevolution in Large-Scale Optimization

Wenjie Qiu, Hepei Li, Jianfeng Wang, Jun Zhang+1

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China · Taiyuan University of Technology, Taiyuan 030024, China · Nankai University, Tianjin 300350, China, and also with Hanyang University ERICA, Ansan 15588, South Korea

Global Optimization Evolutionary Optimization Algorithm Selection Learning to Optimize

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Cooperative Coevolution (CC) and Non-Decomposition Algorithms (NDAs) are two primary approaches for large-scale global optimization (LSGO). Although both have achieved success, they exhibit markedly different performance across problem types and each faces approach-specific limitations, leaving the question of "when to use CC" unresolved. We address this gap with WCC, a novel learning-based framework that dynamically decides when to deploy CC in LSGO. WCC employs a streamlined design for efficient adaptive learning. We formulate the decision as a Markov decision process, designing a targeted feature set, a robust reward, and a tailored network architecture to provide the agent with rich information about the problem state and optimization progress. Our auto-regressive policy enables WCC to perform fine-grained, dynamic switching between CC and NDAs while conditionally allocating the computational budget without expert specifications. WCC shows strong competitiveness and superior generalization across problems, optimization horizons, and baseline optimizers. It realizes a "train-once and reuse" paradigm under a low budget, significantly curbing LSGO cost. Additionally, in-depth analyses yield insights not captured by existing theory, offering practical guidance on when and how to effectively deploy CC.