Optimization and Control

Papers filed under math.OC on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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601 to 660 of 1,592

Error Feedback Fixes SignSGD and other Gradient Compression Schemes
Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich +1
cs.LGmath.OCstat.MLarXiv:1901.09847v22019
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator
Cong Fang, Chris Junchi Li, Zhouchen Lin +1
math.OCcs.LGstat.MLarXiv:1807.01695v22018
Distributionally Robust Stochastic Optimization with Wasserstein Distance
Rui Gao, Anton J. Kleywegt
math.OCarXiv:1604.02199v32016
Convex Optimization: Algorithms and Complexity
Sébastien Bubeck
math.OCcs.CCcs.LGarXiv:1405.4980v22014
Algorithms for the Split Variational Inequality Problem
Yair Censor, Aviv Gibali, Simeon Reich
math.OCmath.FAarXiv:1009.3780v22010
Nechvile-Transformed Spacecraft Dynamics and Propellant Computation in the 3-Body Problem
Michael J. Dixon, Isaac M. Ross
math.OCeess.SYmath.NAarXiv:2608.29271v12026
Barycentric Weak Inner-Product Gromov-Wasserstein
Youssef Mroueh
math.OCstat.MLarXiv:2608.25145v12026
On the Principles Behind Neural Network Optimizers
Yushun Zhang
cs.LGmath.OCarXiv:2608.16760v12026
The Multiple Timescales of Gradient Descent on the Edge of Stability: A Perturbative Derivation of the Central Flow
Raphaël Berthier
cs.LGmath.OCstat.MLarXiv:2609.01034v12026
Tight Complexity Bounds for Optimizing Composite Objectives
Blake Woodworth, Nathan Srebro
math.OCcs.LGstat.MLarXiv:1605.08003v32016
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks
Ziwei Ji, Matus Telgarsky
cs.LGmath.OCstat.MLarXiv:1909.12292v42019
Finite-Sample Metric Non-Collapse for Geometrically Supervised Latent World Models in Control
Alain Bensoussan, Minh-Nhat Phung, Minh-Binh Tran
math.OCcs.LGarXiv:2608.07265v22026
Neural Networks Provably Learn Spectral Representations for Group Composition
Jianliang He, Leda Wang, Fengzhuo Zhang +2
cs.LGmath.OCmath.RTarXiv:2606.02993v22026
On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking
Jianliang He, Leda Wang, Siyu Chen +1
cs.LGmath.OCstat.MLarXiv:2602.16849v12026
Momentum Improves Normalized SGD
Ashok Cutkosky, Harsh Mehta
cs.LGmath.OCstat.MLarXiv:2002.03305v22020
Optimal sizing of renewable energy storage: A comparative study of hydrogen and battery system considering degradation and seasonal storage
Son Tay Le, Tuan Ngoc Nguyen, Dac-Khuong Bui +1
math.OCarXiv:2211.07833v12022
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism
Paria Rashidinejad, Banghua Zhu, Cong Ma +2
cs.LGcs.AImath.OCarXiv:2103.12021v22021
From noisy data to feedback controllers: non-conservative design via a matrix S-lemma
Henk J. van Waarde, M. Kanat Camlibel, Mehran Mesbahi
math.OCarXiv:2006.00870v22020
Real-time City-scale Ridesharing via Linear Assignment Problems
Andrea Simonetto, Julien Monteil, Claudio Gambella
math.OCarXiv:1902.10676v12019
Frequency Stability of Synchronous Machines and Grid-Forming Power Converters
Ali Tayyebi, Dominic Groß, Adolfo Anta +2
eess.SYmath.OCarXiv:2003.04715v12020
Willems' Fundamental Lemma for State-space Systems and its Extension to Multiple Datasets
Henk J. van Waarde, Claudio De Persis, M. Kanat Camlibel +1
math.OCmath.DSarXiv:2002.01023v22020
Naive Exploration is Optimal for Online LQR
Max Simchowitz, Dylan J. Foster
cs.LGmath.OCstat.MLarXiv:2001.09576v42020
Distributionally Robust Optimization: A Review
Hamed Rahimian, Sanjay Mehrotra
math.OCcs.LGstat.MLarXiv:1908.05659v12019
Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning
Zhouchen Lin, Risheng Liu, Huan Li
math.NAcs.LGmath.OCarXiv:1310.5035v22013
An Improved Analysis of Training Over-parameterized Deep Neural Networks
Difan Zou, Quanquan Gu
cs.LGmath.OCstat.MLarXiv:1906.04688v12019
Why gradient clipping accelerates training: A theoretical justification for adaptivity
Jingzhao Zhang, Tianxing He, Suvrit Sra +1
math.OCcs.LGarXiv:1905.11881v22019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
Yuan Cao, Quanquan Gu
cs.LGmath.OCstat.MLarXiv:1905.13210v32019
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
Chao Ning, Fengqi You
cs.LGmath.OCstat.MLarXiv:1904.01934v12019
A Two-Stage Approach for Combined Heat and Power Economic Emission Dispatch: Combining Multi-Objective Optimization with Integrated Decision Making
Yang Li, Jinlong Wang, Dongbo Zhao +2
math.OCarXiv:1808.05704v12018
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Katya Scheinberg, Shiqian Ma, Donald Goldfarb
cs.LGmath.OCstat.MLarXiv:1011.0097v12010
Placement and Implementation of Grid-Forming and Grid-Following Virtual Inertia and Fast Frequency Response
Bala Kameshwar Poolla, Dominic Groß, Florian Dörfler
math.OCarXiv:1807.01942v42018
Scaling provable adversarial defenses
Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen +1
cs.LGcs.AImath.OCarXiv:1805.12514v22018
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification
Max Simchowitz, Horia Mania, Stephen Tu +2
cs.LGmath.OCstat.MLarXiv:1802.08334v42018
Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton
math.OCmath.DSphysics.data-anarXiv:1711.05501v22017
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Pan Xu, Jinghui Chen, Difan Zou +1
stat.MLcs.LGmath.OCarXiv:1707.06618v32017
DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization
Amir Ali Ahmadi, Anirudha Majumdar
math.OCcs.DSeess.SYarXiv:1706.02586v32017
Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms
Line Roald, Göran Andersson
math.OCarXiv:1706.03241v22017
Structured sparsity-inducing norms through submodular functions
Francis Bach
cs.LGmath.OCstat.MLarXiv:1008.4220v32010
A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates
Zhi Li, Wei Shi, Ming Yan
math.OCcs.DCcs.LGarXiv:1704.07807v22017
Independent Reinforcement Learning in Discounted Markov Games
Asrin Efe Yorulmaz, Ugur Aydin, Tamer Basar
cs.GTcs.AIcs.LGarXiv:2609.00504v12026
An Overview of Sustainable Green 5G Networks
Qingqing Wu, Geoffrey Ye Li, Wen Chen +2
cs.ITcs.NImath.OCarXiv:1609.09773v22016
Learning in games with continuous action sets and unknown payoff functions
Panayotis Mertikopoulos, Zhengyuan Zhou
math.OCcs.GTcs.LGarXiv:1608.07310v22016
Distributed Optimization Under Adversarial Nodes
Shreyas Sundaram, Bahman Gharesifard
eess.SYcs.DCmath.OCarXiv:1606.08939v12016
Stochastic Optimization for Large-scale Optimal Transport
Genevay Aude, Marco Cuturi, Gabriel Peyré +1
math.OCcs.LGmath.NAarXiv:1605.08527v12016
A Geometric Analysis of Phase Retrieval
Ju Sun, Qing Qu, John Wright
cs.ITmath.OCstat.MLarXiv:1602.06664v32016
Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process
Shiqiang Wang, Rahul Urgaonkar, Murtaza Zafer +3
cs.DCcs.NImath.OCarXiv:1506.05261v22015
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow
Stephen Tu, Ross Boczar, Max Simchowitz +2
math.OCarXiv:1507.03566v22015
Dynamic Service Placement for Mobile Micro-Clouds with Predicted Future Costs
Shiqiang Wang, Rahul Urgaonkar, Ting He +3
cs.DCcs.NImath.OCarXiv:1503.02735v22015
Online Optimization : Competing with Dynamic Comparators
Ali Jadbabaie, Alexander Rakhlin, Shahin Shahrampour +1
cs.LGmath.OCstat.MLarXiv:1501.06225v12015
Convex Optimization for Big Data
Volkan Cevher, Stephen Becker, Mark Schmidt
math.OCcs.LGstat.MLarXiv:1411.0972v12014
Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty
Dimitar Ho
eess.SYcs.LGmath.OCarXiv:2608.13651v12026
The Loss Does Not See the Basis, but Adam Does
Devender Singh
cs.LGmath.OCstat.MLarXiv:2608.05136v12026
Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers
Tim Tsz-Kit Lau, Weijie Su
math.OCcs.AIcs.LGarXiv:2605.18106v42026
Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling
Yongchang Hao, Lili Mou
cs.LGcs.AImath.OCarXiv:2604.04987v12026
Adam Improves Muon: Adaptive Moment Estimation with Orthogonalized Momentum
Minxin Zhang, Yuxuan Liu, Hayden Schaeffer
cs.LGmath.OCarXiv:2602.17080v22026
Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization
Xiao Wang, Shiqian Ma, Donald Goldfarb +1
math.OCcs.LGmath.NAarXiv:1607.01231v42016
Robust subspace clustering
Mahdi Soltanolkotabi, Ehsan Elhamifar, Emmanuel J. Candès
cs.LGcs.ITmath.OCarXiv:1301.2603v32013
Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization
Shai Shalev-Shwartz, Tong Zhang
stat.MLcs.LGmath.OCarXiv:1209.1873v22012
Robust Energy Management for Microgrids With High-Penetration Renewables
Yu Zhang, Nikolaos Gatsis, Georgios B. Giannakis
math.OCeess.SYarXiv:1207.4831v32012
Personalized Federated Learning: A Meta-Learning Approach
Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar
cs.LGmath.OCstat.MLarXiv:2002.07948v42020

Optimization and Control

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator

Distributionally Robust Stochastic Optimization with Wasserstein Distance

Convex Optimization: Algorithms and Complexity

Algorithms for the Split Variational Inequality Problem

Nechvile-Transformed Spacecraft Dynamics and Propellant Computation in the 3-Body Problem

Barycentric Weak Inner-Product Gromov-Wasserstein

On the Principles Behind Neural Network Optimizers

The Multiple Timescales of Gradient Descent on the Edge of Stability: A Perturbative Derivation of the Central Flow

Tight Complexity Bounds for Optimizing Composite Objectives

Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks

Finite-Sample Metric Non-Collapse for Geometrically Supervised Latent World Models in Control

Neural Networks Provably Learn Spectral Representations for Group Composition

On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking

Momentum Improves Normalized SGD

Optimal sizing of renewable energy storage: A comparative study of hydrogen and battery system considering degradation and seasonal storage

Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism

From noisy data to feedback controllers: non-conservative design via a matrix S-lemma

Real-time City-scale Ridesharing via Linear Assignment Problems

Frequency Stability of Synchronous Machines and Grid-Forming Power Converters

Willems' Fundamental Lemma for State-space Systems and its Extension to Multiple Datasets

Naive Exploration is Optimal for Online LQR

Distributionally Robust Optimization: A Review

Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning

An Improved Analysis of Training Over-parameterized Deep Neural Networks

Why gradient clipping accelerates training: A theoretical justification for adaptivity

Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming

A Two-Stage Approach for Combined Heat and Power Economic Emission Dispatch: Combining Multi-Objective Optimization with Integrated Decision Making

Sparse Inverse Covariance Selection via Alternating Linearization Methods

Placement and Implementation of Grid-Forming and Grid-Following Virtual Inertia and Fast Frequency Response

Scaling provable adversarial defenses

Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification

Sparse identification of nonlinear dynamics for model predictive control in the low-data limit

Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization

Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms

Structured sparsity-inducing norms through submodular functions

A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates

Independent Reinforcement Learning in Discounted Markov Games

An Overview of Sustainable Green 5G Networks

Learning in games with continuous action sets and unknown payoff functions

Distributed Optimization Under Adversarial Nodes

Stochastic Optimization for Large-scale Optimal Transport

A Geometric Analysis of Phase Retrieval

Dynamic Service Migration in Mobile Edge Computing Based on Markov Decision Process

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow

Dynamic Service Placement for Mobile Micro-Clouds with Predicted Future Costs

Online Optimization : Competing with Dynamic Comparators

Convex Optimization for Big Data

Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty

The Loss Does Not See the Basis, but Adam Does

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers

Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling

Adam Improves Muon: Adaptive Moment Estimation with Orthogonalized Momentum

Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization

Robust subspace clustering

Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization

Robust Energy Management for Microgrids With High-Penetration Renewables

Personalized Federated Learning: A Meta-Learning Approach