Alexander Gasnikov

Stochastic Decentralized Optimization of Non-Smooth Convex and Convex-Concave Problems over Time-Varying Networks AAAI 2026 An Optimal Algorithm for Strongly Convex Min-Min Optimization UAI 2025 Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed ICML 2025 Decentralized Optimization with Coupled Constraints ICLR 2025 On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled Saddle-Point Optimization: Lower Bounds and Optimal Algorithms ICML 2025 OPTAMI: Global Superlinear Convergence of High-order Methods ICLR 2025 Synthetic Proofs with Tool-Integrated Reasoning: Contrastive Alignment for LLM Mathematics with Lean EMNLP 2025 Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values NeurIPS 2024 Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization NeurIPS 2024 Advancing the Lower Bounds: an Accelerated, Stochastic, Second-order Method with Optimal Adaptation to Inexactness ICLR 2024 Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems AISTATS 2024 Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations NeurIPS 2024 High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise ICML 2024 Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks NeurIPS 2024 Optimal Flow Matching: Learning Straight Trajectories in Just One Step NeurIPS 2024 Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases AISTATS 2024 Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance NeurIPS 2023 Algorithm for Constrained Markov Decision Process with Linear Convergence AISTATS 2023 First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities NeurIPS 2023 High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance ICML 2023 Is Consensus Acceleration Possible in Decentralized Optimization over Slowly Time-Varying Networks? ICML 2023 Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities NeurIPS 2023 Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling NeurIPS 2022 Acceleration in Distributed Optimization under Similarity AISTATS 2022 A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate NeurIPS 2022 Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise NeurIPS 2022 Decentralized Local Stochastic Extra-Gradient for Variational Inequalities NeurIPS 2022 Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees NeurIPS 2022 Optimal Algorithms for Decentralized Stochastic Variational Inequalities NeurIPS 2022 Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity NeurIPS 2022 Primal-Dual Stochastic Mirror Descent for MDPs AISTATS 2022 The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization NeurIPS 2022 The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization NeurIPS 2022 The power of first-order smooth optimization for black-box non-smooth problems ICML 2022 ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks ICML 2021 Distributed Saddle-Point Problems Under Data Similarity NeurIPS 2021 Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks NeurIPS 2021 Newton Method over Networks is Fast up to the Statistical Precision ICML 2021 On a Combination of Alternating Minimization and Nesterov’s Momentum ICML 2021 Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping NeurIPS 2020 Near Optimal Methods for Minimizing Convex Functions with Lipschitz p-th Derivatives COLT 2019 On the Complexity of Approximating Wasserstein Barycenters ICML 2019 Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization COLT 2019 Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn’s Algorithm ICML 2018 Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters NeurIPS 2018 Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods NeurIPS 2016