Publications related to Scalable sparse covariance estimation via self-concordance

Perturbed Utility Stochastic Traffic Assignment

This paper develops a fast algorithm for computing the equilibrium assignment with the perturbed utility route choice (PURC) model. Without compromise, this allows the significant advantages of the PURC model to be used in large-scale applications. We form ...

Informs2024

Universal and adaptive methods for robust stochastic optimization

Ali Kavis

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

EPFL2023

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Giancarlo Ferrari Trecate, Maryam Kamgarpour, Yuning Jiang, Baiwei Guo

We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optim ...

2023

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

Ahmet Alacaoglu

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 ...

EPFL2021

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 ...

2021

Convex optimization in sums of Banach spaces

Michaël Unser, Shayan Aziznejad

We characterize the solution of a broad class of convex optimization problems that address the reconstruction of a function from a finite number of linear measurements. The underlying hypothesis is that the solution is decomposable as a finite sum of compo ...

2021

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Shaul Nadav Hallak

This paper introduces a method for computing points satisfying the second-order necessary optimality conditions for nonconvex minimization problems subject to a closed and convex constraint set. The method comprises two independent steps corresponding to t ...

SPRINGER/PLENUM PUBLISHERS2020

Stochastic Frank-Wolfe for Composite Convex Minimization

Volkan Cevher, Alp Yurtsever

A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints. The majority of classical SDP solvers are designed fo ...

2019

Proximity Operators of Discrete Information Divergences

Mireille El Gheche, Giovanni Chierchia

While phi-divergences have been extensively studied in convex analysis, their use in optimization problems often remains challenging. In this regard, one of the main shortcomings of existing methods is that the minimization of phi-divergences is usually pe ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2018

Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

Jean-Baptiste Francis Marie Juliette Cordonnier

The interest for distributed stochastic optimization has raised to train complex Machine Learning models with more data on distributed systems. Increasing the computation power speeds up the training but it faces a communication bottleneck between workers ...

2018

Scalable sparse covariance estimation via self-concordance

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Perturbed Utility Stochastic Traffic Assignment

Universal and adaptive methods for robust stochastic optimization

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

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

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

Convex optimization in sums of Banach spaces

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach

Stochastic Frank-Wolfe for Composite Convex Minimization

Proximity Operators of Discrete Information Divergences

Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

Perturbed Utility Stochastic Traffic Assignment

Convex optimization in sums of Banach spaces

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

Convex Optimization using Sparsified Stochastic Gradient Descent with Memory

Universal and adaptive methods for robust stochastic optimization

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

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Stochastic Frank-Wolfe for Composite Convex Minimization

Proximity Operators of Discrete Information Divergences

Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach