Convex Optimization

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Convex Optimization

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Convex Sets

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Stochastic Optimization

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Stochastic Smooth Convex Optimization

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Nonconvex

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S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty
Jun 1, 2026Fabio Pavirani, Bert Claessens, Pierre Pinson +1Demand Response Power Systems
Beyond $\ell_2$ -norm and $\ell_\infty$ -norm: A Curvature-Inspired $\ell_p$ -Norm Scheme for Deep Neural Networks
Jun 1, 2026Jianhao Xu, Zhuang YangNeural Network Optimization Stochastic Gradient Descent
Convex Distance Operator Transport: A Convex and Geometry-Preserving Formulation
Jun 1, 2026Junhyoung Chung, Euijong Song, Won Hwa Kim +1Optimal Transport Convex Sets
Stochastic convergence of parallel asynchronous adaptive first-order methods
Jun 1, 2026Serge Gratton, Philippe L. TointStochastic Smooth Convex Optimization Zeroth-Order Optimization
Near-Optimal Machine Unlearning Utility for Smooth Strongly Convex Losses
Jun 1, 2026Matthew Regehr, Gautam Kamath, Andrew LowyMachine Unlearning Several Approximate Unlearning Strategies
Information-Theoretic Lower Bounds for Bit-Constrained Stochastic Optimization via a Reduction to Compressed Gaussian Mean Estimation
May 30, 2026Munsik KimInformation-Theoretic Limits Sharp Joint-Kl Oracle Theory
In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
May 30, 2026Zijian LiuStochastic Smooth Convex Optimization Stochastic Gradient Descent
A Unified Framework for Gradient Aggregation in Multi-Objective Optimization
May 28, 2026Zeou Hu, Kelvin Ho, Yaoliang YuMulti-Objective Optimization Convex Sets
MoSSP: A Momentum-Based Single-Loop Stochastic Penalty Method for Nonconvex Constrained DC-Regularized Optimization
May 28, 2026Luxuan Li, Chunfeng Cui, Xiao WangConvex Optimization Penalties
Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization
May 27, 2026Sara Gjorgjieva, Eva Tuba, Tome EftimovStochastic Optimization Learning-Augmented Algorithms
Decentralized Parameter-Free Online Learning with Compressed Gossip
May 27, 2026Tomas Ortega, Hamid JafarkhaniLearning-Augmented Algorithms Near-Optimal Regret Guarantees
Convergence of Spectral Descent for Non-smooth Optimization
May 26, 2026Yixuan Yang, Yuqing He, Song LiMuon Optimizer Convex Optimization
Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback
May 25, 2026Anas Barakat, Andreas Kontogiannis, Vasilis Pollatos +2O(T^Β)$ Simultaneous Regret Convex Optimization
Affinity Graph Connectivity in Convex Clustering
May 23, 2026Sam Rosen, Jason XuExponential Random Graph Models Convex Sets
Zeroth-Order Nonconvex Nonsmooth Optimization with Heavy-Tailed Noise
May 23, 2026Zhuanghua Liu, Luo LuoStochastic Smooth Convex Optimization Convex Optimization
Convex Optimization for Alignment and Preference Learning on a Single GPU
May 22, 2026Miria Feng, Mert PilanciPreference Alignment Direct Preference Optimization
Signal Temporal Logic Motion Planning via Graphs of Convex Sets
May 22, 2026Yu Chen, Ancheng Hou, Mingyang Feng +2Signal Temporal Logic Motion Planners
Convex Low-resource Accent-Robust Language Detection in Speech Recognition
May 22, 2026Miria Feng, William Tan, Mert PilanciDiscrete Speech Representations Convex Sets
Tokenisation via Convex Relaxations
May 21, 2026Jan Tempus, Philip Whittington, Craig W. Schmidt +2Tokenization Token-Level Uncertainty
Regret-Based $(ε,δ)$ -optimal Stopping Criteria for Bayesian Optimization
May 21, 2026Haowei Wang, Jingyi Wang, Qiyu WeiBayesian Optimization Optimal Stopping
Real-Time Auto-Optimization in Unknown Environments via Structure-Exploiting Dual Control for Exploration and Exploitation
May 21, 2026Shiying Dong, Haoyang Yang, Qiwei Liu +1Optimal Control Llm-Based Optimization Modeling
Bandit Convex Optimization with Gradient Prediction Adaptivity
May 21, 2026Shuche Wang, Adarsh Barik, Vincent Y. F. TanConvex Optimization Bandits
A Geometric Approach to Constrained Online Learning
May 20, 2026Dhruv Sarkar, Abhishek SinhaConvex Optimization O(T^Β)$ Simultaneous Regret
Robust Subspace-Constrained Quadratic Models for Low-Dimensional Structure Learning
May 19, 2026Zheng Zhai, Xiaohui LiLow-Dimensional Structure Least Squares
From Mean-Field Limits to Semiclassical Concentration: Global Convergence of the Canonical Evolutionary Strategy
May 19, 2026Matías Neto, Nicolás Garay, Luis Martí +1Evolutionary Search Convergence Analysis
D-Convexity: A Unified Differentiable Convex Shape Prior via Quasi-Concavity for Data-driven Image Segmentation
May 19, 2026Shengzhe Chen, Hao YanRobust Segmentation Convex Sets
Stochastic Penalty-Barrier Methods for Constrained Machine Learning
May 18, 2026Adam Bosák, Andrii Kliachkin, Jana Lepšová +2Quadratic Slack Penalty Stochastic Smooth Convex Optimization
Scalable Bi-causal Optimal Transport via KL Relaxation and Policy Gradients
May 17, 2026Haoyang Cao, Jesse Hoekstra, Renyuan Xu +2Optimal Transport Stochastic Optimization
Accelerated Gradient Descent for Faster Convergence with Minimal Overhead
May 15, 2026Manuel Graca, L. Miguel Silveira, Arlindo Oliveira +1Mini-Batch Stochastic Gradient Descent Gradient Descent
Practical Validity Conditions for Byzantine-Tolerant Federated Learning
May 15, 2026Mélanie Cambus, Darya Melnyk, Tijana Milentijević +1Byzantine Attacks Federated Learning
Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
May 15, 2026Dmitry KovalevStochastic Smooth Convex Optimization Lipschitz Constant
Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation
May 14, 2026Zhankun Luo, Antesh Upadhyay, M. Berk Sahin +3Variance Reduction Stochastic Optimization
Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe
May 14, 2026El Mahdi ChaytiStochastic Smooth Convex Optimization Zeroth-Order Optimization
Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
May 14, 2026Antesh Upadhyay, Arda Fazla, Abolfazl HashemiStochastic Smooth Convex Optimization Convex Optimization
Motion Planning for Autonomous Vehicles using Optimization over Graphs of Convex Sets
May 13, 2026Matheus Wagner, Antônio Augusto FröhlichTrajectory Optimization Autonomous Driving
Stochastic global optimization of continuous functions via random walks on Grassmannians
May 13, 2026Kartik Gupta, Stephen D. Miller, Pradeep Ravikumar +1Stochastic Optimization Low-Dimensional Structure
Min-Max Optimization Requires Exponentially Many Queries
May 13, 2026Martino Bernasconi, Matteo Castiglioni, Andrea Celli +1Convex Optimization Nonconvex
Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization
May 13, 2026Yaxin Yu, Long Chen, Minfu FengStochastic Smooth Convex Optimization Adam
Online Learning-to-Defer with Varying Experts
May 12, 2026Dang Hoang Duy, Yannis Montreuil, Maxime Meyer +3Learning-Augmented Algorithms Experts
Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
May 12, 2026Konstantinos Oikonomidis, Jan Quan, Kimon Antonakopoulos +3Spectral Preconditioning Convex Optimization
Parameterized Complexity of Stationarity Testing for Piecewise-Affine Functions and Shallow CNN Losses
May 11, 2026Yuhan YeStationarity Convex Optimization
Function-Space ADMM for Decentralized Federated Learning: A Control Theoretic Perspective
May 10, 2026Akihito Taya, Yuuki Nishiyama, Kaoru SezakiFederated Learning Distributional Learning
SHIELD: Scalable Optimal Control with Certification using Duality and Convexity
May 9, 2026Hansung Kim, Siddharth H. Nair, Francesco BorrelliModel Predictive Control Optimal Control
A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization
May 9, 2026Zhiyao Zhang, Myeung Suk Oh, Zhen Qin +3Bilevel Optimization Multi-Objective Optimization
Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
May 9, 2026Zhirayr Tovmasyan, Artavazd Maranjyan, Peter RichtárikVariance Reduction Stochastic Gradient Descent
Tight Generalization Bounds for Noiseless Inverse Optimization
May 9, 2026Pouria Fatemi, Hoomaan Maskan, Suvrit Sra +1Stochastic Optimization Generalization Bounds
Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle
May 9, 2026Peter Richtárik, Kaja Gruntkowska, Hanmin LiConstrained Optimization Convex Sets
Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples
May 8, 2026Anastasis KratsiosLipschitz Constant Kernel Hilbert Spaces
A Unified Lyapunov-IQC Framework for Uniform Stability of Smooth Quadratic First-Order Accelerated Optimizers
May 8, 2026Don Li, Dacian DaescuLyapunov Function Convex Sets
Globally Optimal Training of Spiking Neural Networks via Parameter Reconstruction
May 8, 2026Himanshu Udupi, Xiaocong Yang, ChengXiang ZhaiSpiking Neural Networks Time-Step-Wise Learnable Surrogate Gradient Function
Exploring the non-convexity in machine learning using quantum-inspired optimization
May 8, 2026Kandula Eswara Sai Kumar, Parth Dhananjay Danve, Abhishek Chopra +1Quantum Approximate Optimization Algorithm Nonconvex
Distributional simplicity bias and effective convexity in Energy Based Models
May 8, 2026Aurélien Decelle, Alfonso de Jesús Navas Gómez, Beatriz SeoaneFree-Energy Landscape Two-Dimensional Ising Model
SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
May 8, 2026Hippolyte Labarrière, Cesare Molinari, Silvia Villa +1Variational Inference Mini-Batch Stochastic Gradient Descent
Convex Optimization with Nested Evolving Feasible Sets
May 8, 2026Karthick Krishna M., Haricharan Balasundaram, Rahul VazeConvex Optimization O(T^Β)$ Simultaneous Regret
Sample Complexity of Stochastic Optimization with Integer Variables
May 8, 2026Hongyu Cheng, Yinghao Zheng, Marco Molinaro +1Stochastic Optimization Mixed-Integer Programming
When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
May 7, 2026Yi Wang, Chandrajit BajajConvex Optimization Descent
Distributionally-Robust Learning to Optimize
May 7, 2026Vinit Ranjan, Jisun Park, Bartolomeo StellatoRobust Optimization Distributional Learning
When Does $\ell_2$ -Boosting Overfit Benignly? High-Dimensional Risk Asymptotics and the $\ell_1$ Implicit Bias
May 7, 2026Ye Su, Jian Li, Yong LiuOverfitting Empirical Risk Minimization
Power-Efficiency and Scalability Analysis of Magnetically-Actuated Satellite Swarms via Convex Optimization
May 7, 2026Yuta Takahashi, Seang Shim, Hiraku Sakamoto +1Unmanned Aerial Vehicle Swarms Satellite
Quadratic Objective Perturbation: Curvature-Based Differential Privacy
May 7, 2026Daniel Cortild, Coralia CartisDifferential Privacy Empirical Risk Minimization

Jun 1, 2026 · cs.AIJ/K move · Enter open · S save

S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty

Fabio Pavirani, Bert Claessens, Pierre Pinson, Chris Develder

Effective scheduling in the energy sector is essential to ensure the reliable operation of electrical grids and their connected assets by, for instance, optimizing the dispatch of generation units and storage systems. An effective planning strategy must (a) accommodate advanced and potentially non-linear system models -- exploiting the increasing data availability of modern grids, and (b) explicitly handle uncertainties arising, for instance, from the integration of renewable energy sources. While existing approaches can address either non-linearity (e.g., Monte Carlo Tree Search) or uncertainty (e.g., stochastic mathematical optimization), there is a lack of planning techniques capable of addressing both challenges simultaneously. To bridge this gap, we propose a Stochastic Scenario-Structured Tree Search (S3TS) algorithm that explicitly represents uncertainty through scenario trees while enabling the integration of advanced non-linear models. We evaluate S3TS on a simulated demand response signal publication problem, largely mimicking the imbalance settlement mechanism in Belgium. The results demonstrate near-optimal performance in linear, analytically tractable settings, with costs within 14% of the mathematically optimal solution conditioned to the scenario trees. In highly non-linear scenarios, S3TS significantly outperforms baseline methods, achieving cost reductions of up to 51% and 5.4% compared to a myopic algorithm and deterministic MCTS, respectively.

Demand Response Power Systems Optimal Scheduling Monte Carlo Tree Search Stochastic Optimization Tree Search Stochastic Planning Methods

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