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We study large-scale, nonsmooth, nonconconvex optimization problems. In particular, we focus on nonconvex problems with \emph{composite} objectives. This class of problems includes the extensively studied convex, composite objective problems as a special case. To tackle composite nonconvex problems, we introduce a powerful new framework based on asymptotically \emph{nonvanishing} errors, avoiding the common convenient assumption of eventually vanishing errors. Within our framework we derive both batch and incremental nonconvex proximal splitting algorithms. To our knowledge, our framework is first to develop and analyze incremental \emph{nonconvex} proximal-splitting algorithms, even if we disregard the ability to handle nonvanishing errors. We illustrate our theoretical framework by showing how it applies to difficult large-scale, nonsmooth, and nonconvex problems.
Author Information
Suvrit Sra (MIT)
Suvrit Sra is a faculty member within the EECS department at MIT, where he is also a core faculty member of IDSS, LIDS, MIT-ML Group, as well as the statistics and data science center. His research spans topics in optimization, matrix theory, differential geometry, and probability theory, which he connects with machine learning --- a key focus of his research is on the theme "Optimization for Machine Learning” (http://opt-ml.org)
More from the Same Authors
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2022 Poster: CCCP is Frank-Wolfe in disguise »
Alp Yurtsever · Suvrit Sra -
2022 Poster: Efficient Sampling on Riemannian Manifolds via Langevin MCMC »
Xiang Cheng · Jingzhao Zhang · Suvrit Sra -
2021 Poster: Can contrastive learning avoid shortcut solutions? »
Joshua Robinson · Li Sun · Ke Yu · Kayhan Batmanghelich · Stefanie Jegelka · Suvrit Sra -
2021 Poster: Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates »
Alp Yurtsever · Alex Gu · Suvrit Sra -
2020 : Invited speaker: SGD without replacement: optimal rate analysis and more, Suvrit Sra »
Suvrit Sra -
2020 Poster: SGD with shuffling: optimal rates without component convexity and large epoch requirements »
Kwangjun Ahn · Chulhee Yun · Suvrit Sra -
2020 Spotlight: SGD with shuffling: optimal rates without component convexity and large epoch requirements »
Kwangjun Ahn · Chulhee Yun · Suvrit Sra -
2020 Poster: Why are Adaptive Methods Good for Attention Models? »
Jingzhao Zhang · Sai Praneeth Karimireddy · Andreas Veit · Seungyeon Kim · Sashank Reddi · Sanjiv Kumar · Suvrit Sra -
2020 Poster: Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes »
Yi Tian · Jian Qian · Suvrit Sra -
2020 Spotlight: Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes »
Yi Tian · Jian Qian · Suvrit Sra -
2019 Poster: Flexible Modeling of Diversity with Strongly Log-Concave Distributions »
Joshua Robinson · Suvrit Sra · Stefanie Jegelka -
2019 Poster: Are deep ResNets provably better than linear predictors? »
Chulhee Yun · Suvrit Sra · Ali Jadbabaie -
2019 Poster: Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity »
Chulhee Yun · Suvrit Sra · Ali Jadbabaie -
2019 Spotlight: Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity »
Chulhee Yun · Suvrit Sra · Ali Jadbabaie -
2018 Poster: Direct Runge-Kutta Discretization Achieves Acceleration »
Jingzhao Zhang · Aryan Mokhtari · Suvrit Sra · Ali Jadbabaie -
2018 Spotlight: Direct Runge-Kutta Discretization Achieves Acceleration »
Jingzhao Zhang · Aryan Mokhtari · Suvrit Sra · Ali Jadbabaie -
2018 Poster: Exponentiated Strongly Rayleigh Distributions »
Zelda Mariet · Suvrit Sra · Stefanie Jegelka -
2018 Tutorial: Negative Dependence, Stable Polynomials, and All That »
Suvrit Sra · Stefanie Jegelka -
2017 Workshop: OPT 2017: Optimization for Machine Learning »
Suvrit Sra · Sashank J. Reddi · Alekh Agarwal · Benjamin Recht -
2017 Poster: Elementary Symmetric Polynomials for Optimal Experimental Design »
Zelda Mariet · Suvrit Sra -
2017 Poster: Polynomial time algorithms for dual volume sampling »
Chengtao Li · Stefanie Jegelka · Suvrit Sra -
2016 Workshop: OPT 2016: Optimization for Machine Learning »
Suvrit Sra · Francis Bach · Sashank J. Reddi · Niao He -
2016 : Taming non-convexity via geometry »
Suvrit Sra -
2016 Poster: Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling »
Chengtao Li · Suvrit Sra · Stefanie Jegelka -
2016 Poster: Kronecker Determinantal Point Processes »
Zelda Mariet · Suvrit Sra -
2016 Poster: Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization »
Sashank J. Reddi · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2016 Poster: Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds »
Hongyi Zhang · Sashank J. Reddi · Suvrit Sra -
2016 Tutorial: Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity »
Suvrit Sra · Francis Bach -
2015 Workshop: Optimization for Machine Learning (OPT2015) »
Suvrit Sra · Alekh Agarwal · Leon Bottou · Sashank J. Reddi -
2015 Poster: Matrix Manifold Optimization for Gaussian Mixtures »
Reshad Hosseini · Suvrit Sra -
2015 Poster: On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants »
Sashank J. Reddi · Ahmed Hefny · Suvrit Sra · Barnabas Poczos · Alexander Smola -
2014 Workshop: OPT2014: Optimization for Machine Learning »
Zaid Harchaoui · Suvrit Sra · Alekh Agarwal · Martin Jaggi · Miro Dudik · Aaditya Ramdas · Jean Lasserre · Yoshua Bengio · Amir Beck -
2014 Poster: Efficient Structured Matrix Rank Minimization »
Adams Wei Yu · Wanli Ma · Yaoliang Yu · Jaime Carbonell · Suvrit Sra -
2013 Workshop: OPT2013: Optimization for Machine Learning »
Suvrit Sra · Alekh Agarwal -
2013 Poster: Geometric optimisation on positive definite matrices for elliptically contoured distributions »
Suvrit Sra · Reshad Hosseini -
2013 Poster: Reflection methods for user-friendly submodular optimization »
Stefanie Jegelka · Francis Bach · Suvrit Sra -
2012 Workshop: Optimization for Machine Learning »
Suvrit Sra · Alekh Agarwal -
2012 Poster: A new metric on the manifold of kernel matrices with application to matrix geometric means »
Suvrit Sra -
2011 Workshop: Optimization for Machine Learning »
Suvrit Sra · Stephen Wright · Sebastian Nowozin -
2010 Workshop: Numerical Mathematics Challenges in Machine Learning »
Matthias Seeger · Suvrit Sra -
2010 Workshop: Optimization for Machine Learning »
Suvrit Sra · Sebastian Nowozin · Stephen Wright -
2009 Workshop: Optimization for Machine Learning »
Sebastian Nowozin · Suvrit Sra · S.V.N Vishwanthan · Stephen Wright -
2008 Workshop: Optimization for Machine Learning »
Suvrit Sra · Sebastian Nowozin · Vishwanathan S V N