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03/30/2023
Sublinear Convergence Rates of Extragradient-Type Methods: A Survey on Classical and Recent Developments
The extragradient (EG), introduced by G. M. Korpelevich in 1976, is a we...
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02/08/2023
Extragradient-Type Methods with 𝒪(1/k) Convergence Rates for Co-Hypomonotone Inclusions
In this paper, we develop two “Nesterov's accelerated” variants of the w...
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01/08/2023
Accelerated Randomized Block-Coordinate Algorithms for Co-coercive Equations and Applications
In this paper, we develop an accelerated randomized block-coordinate alg...
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12/19/2022
Gradient Descent-Type Methods: Background and Simple Unified Convergence Analysis
In this book chapter, we briefly describe the main components that const...
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12/01/2022
Data Integration Via Analysis of Subspaces (DIVAS)
Modern data collection in many data paradigms, including bioinformatics,...
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10/15/2021
Halpern-Type Accelerated and Splitting Algorithms For Monotone Inclusions
In this paper, we develop a new type of accelerated algorithms to solve ...
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03/05/2021
Federated Learning with Randomized Douglas-Rachford Splitting Methods
In this paper, we develop two new algorithms, called, FedDR and asyncFed...
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11/24/2020
Shuffling Gradient-Based Methods with Momentum
We combine two advanced ideas widely used in optimization for machine le...
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11/20/2020
Convergence Analysis of Homotopy-SGD for non-convex optimization
First-order stochastic methods for solving large-scale non-convex optimi...
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10/27/2020
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where...
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08/20/2020
An Optimal Hybrid Variance-Reduced Algorithm for Stochastic Composite Nonconvex Optimization
In this note we propose a new variant of the hybrid variance-reduced pro...
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07/17/2020
Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise
The feasibility of federated learning is highly constrained by the serve...
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06/27/2020
Hybrid Variance-Reduced SGD Algorithms For Nonconvex-Concave Minimax Problems
We develop a novel variance-reduced algorithm to solve a stochastic nonc...
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03/03/2020
Randomized Primal-Dual Algorithms for Composite Convex Minimization with Faster Convergence Rates
We develop two novel randomized primal-dual algorithms to solve nonsmoot...
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03/01/2020
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
We propose a novel hybrid stochastic policy gradient estimator by combin...
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02/19/2020
A Unified Convergence Analysis for Shuffling-Type Gradient Methods
In this paper, we provide a unified convergence analysis for a class of ...
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02/17/2020
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
We develop two new stochastic Gauss-Newton algorithms for solving a clas...
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02/17/2020
A Newton Frank-Wolfe Method for Constrained Self-Concordant Minimization
We demonstrate how to scalably solve a class of constrained self-concord...
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07/26/2019
Using positive spanning sets to achieve stationarity with the Boosted DC Algorithm
The Difference of Convex function Algorithm (DCA) is widely used for min...
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07/08/2019
A Hybrid Stochastic Optimization Framework for Stochastic Composite Nonconvex Optimization
In this paper, we introduce a new approach to develop stochastic optimiz...
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05/15/2019
Hybrid Stochastic Gradient Descent Algorithms for Stochastic Nonconvex Optimization
We introduce a hybrid stochastic estimator to design stochastic gradient...
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02/15/2019
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
In this paper, we propose a new stochastic algorithmic framework to solv...
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01/11/2018
Non-stationary Douglas-Rachford and alternating direction method of multipliers: adaptive stepsizes and convergence
We revisit the classical Douglas-Rachford (DR) method for finding a zero...
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11/09/2017
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
We propose a new randomized coordinate descent method for a convex optim...
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03/14/2017
Generalized Self-Concordant Functions: A Recipe for Newton-Type Methods
We study the smooth structure of convex functions by generalizing a powe...
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06/10/2016
Extended Gauss-Newton and Gauss-Newton-ADMM Algorithms for Low-Rank Matrix Optimization
We develop a generic Gauss-Newton (GN) framework for solving a class of ...
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03/21/2016
Convex block-sparse linear regression with expanders -- provably
Sparse matrices are favorable objects in machine learning and optimizati...
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03/05/2016
A single-phase, proximal path-following framework
We propose a new proximal, path-following framework for a class of const...
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09/01/2015
Adaptive Smoothing Algorithms for Nonsmooth Composite Convex Minimization
We propose an adaptive smoothing algorithm based on Nesterov's smoothing...
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02/04/2015
Composite convex minimization involving self-concordant-like cost functions
The self-concordant-like property of a smooth convex function is a new a...
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06/20/2014
A Primal-Dual Algorithmic Framework for Constrained Convex Minimization
We present a primal-dual algorithmic framework to obtain approximate sol...
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05/13/2014
Scalable sparse covariance estimation via self-concordance
We consider the class of convex minimization problems, composed of a sel...
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08/13/2013
Composite Self-Concordant Minimization
We propose a variable metric framework for minimizing the sum of a self-...
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01/08/2013