Global Optimization

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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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
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 VaiterGlobal Optimization Nonconvex 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 Black-Box 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 +2Semidefinite Programming Graph Neural Networks
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.
One-Shot Localisation of the Global Minimum of a Noisy One-Dimensional Function: An Iterative Neural Minimizer Compared with 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, Israel · Department of Applied Mathematics, Tel Aviv University, Israel
ImprovEvolve: Basin-Hopping Meets LLM-Guided Evolutionary Search
Feb 10, 2026Alexey Kravatskiy, Valentin Khrulkov, Ivan OseledetsAutomated Algorithm Discovery Evolutionary Optimization
1MIRIAI, Russia · 2FusionBrain Lab, Russia · 3AXXX, Russia +1
Favorability of Loss Landscape with Weight Decay Requires Both Large Overparametrization and Initialization
May 28, 2025Etienne Boursier, Matthew Bowditch, Matthias Englert +1Weight Decay Neural Network Initialization
INRIA LMO, Université Paris-Saclay Orsay, France · Mathematics Institute University of Warwick Coventry, UK · Department of Computer Science University of Warwick Coventry, UK

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A practical DIRECT-type algorithm for medium-scale black-box global optimization

Linas Stripinis, Remigijus Paulavičius

Vilnius University Institute of Data Science and Digital Technologies, Akademijos 4, Vilnius, Lithuania, LT-08663

Black-Box Optimization Global Optimization

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The DIRECT algorithm is a deterministic global optimization method known for its versatility and balanced exploration-exploitation strategy. However, DIRECT-type algorithms are primarily effective for low-dimensional problems and often exhibit slow convergence as dimensionality increases, limiting their applicability to more complex optimization tasks. To address this limitation, this paper introduces X-DTC-GL, a novel DIRECT-type algorithm that incorporates dynamic partitioning and hybridization techniques. The dynamic partitioning approach adaptively refines the search space based on local one-dimensional surrogate models, enabling rapid subdivision of promising hyper-rectangles. The hybridization strategy selectively employs a hill-climbing method to exploit promising regions identified by the surrogate models. Extensive experiments on four diverse benchmark suites demonstrate that X-DTC-GL significantly outperforms existing DIRECT-type baselines, achieving improvements of ~12% in solvability and ~27% in solution quality. Performance-profile analyses indicate the fastest convergence on up to ~40% of instances, the best runtime performance on ~17% of problems, and competitive overall execution times. By improving performance within the partition-based framework, these advances strengthen the algorithm's competitiveness in state-of-the-art black-box optimization.

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