Publications related to On the convergence of stochastic primal-dual hybrid gradient

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

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A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

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In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...

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Localizing Unsynchronized Sensors With Unknown Sources

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We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the source ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Stochastic homogenization of degenerate integral functionals with linear growth

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We study the limit behaviour of sequences of non-convex, vectorial, random integral functionals, defined on W1,1, whose integrands are ergodic and satisfy degenerate linear growth conditions. The latter involve suitable random, scale-dependent weight-funct ...

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On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Volkan Cevher, Ahmet Alacaoglu

In this paper, we analyze the recently proposed stochastic primal-dual hybrid gradient (SPDHG) algorithm and provide new theoretical results. In particular, we prove almost sure convergence of the iterates to a solution with convexity and linear convergenc ...

SIAM PUBLICATIONS2022

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A first-order primal-dual method with adaptivity to local smoothness

Volkan Cevher, Maria-Luiza Vladarean

We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

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Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

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Stochastic gradient descent (SGD) and randomized coordinate descent (RCD) are two of the workhorses for training modern automated decision systems. Intriguingly, convergence properties of these methods are not well-established as we move away from the spec ...

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Mathematical Foundations of Robust and Distributionally Robust Optimization

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Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertainty set or are governed by any probability distribution from within an ambigu ...

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Integrating advanced discrete choice models in mixed integer linear optimization

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In many transportation systems, a mismatch between the associated design and planning decisions and the demand is typically encountered. A tailored system is not only appealing to operators, which could have a better knowledge of their operational costs, b ...

PERGAMON-ELSEVIER SCIENCE LTD2021

On the convergence of stochastic primal-dual hybrid gradient

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Localizing Unsynchronized Sensors With Unknown Sources

Stochastic homogenization of degenerate integral functionals with linear growth

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Stochastic distributed learning with gradient quantization and double-variance reduction

A first-order primal-dual method with adaptivity to local smoothness

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Mathematical Foundations of Robust and Distributionally Robust Optimization

Integrating advanced discrete choice models in mixed integer linear optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A first-order primal-dual method with adaptivity to local smoothness

Stochastic homogenization of degenerate integral functionals with linear growth

Mathematical Foundations of Robust and Distributionally Robust Optimization

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Localizing Unsynchronized Sensors With Unknown Sources

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond

Integrating advanced discrete choice models in mixed integer linear optimization

Stochastic distributed learning with gradient quantization and double-variance reduction