Publications by Armin Eftekhari

Computing Second-Order Points Under Equality Constraints: Revisiting Fletcher's Augmented Lagrangian

We address the problem of minimizing a smooth function under smooth equality constraints. Under regularity assumptions on these constraints, we propose a notion of approximate first- and second-order critical point which relies on the geometric formalism o ...

Springer/Plenum Publishers2024

Sparse non-negative super-resolution - simplified and stabilised

Hemant Tyagi, Armin Eftekhari

We consider the problem of non-negative super-resolution, which concerns reconstructing a non-negative signal x = Sigma(k )(i=1)a(i)delta(ti) from m samples of its convolution with a window function phi(s - t), of the form y(s(j)) = Sigma(k)(i=1) a(i) phi( ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021

Subquadratic Overparameterization for Shallow Neural Networks

Armin Eftekhari, Volkan Cevher, Chaehwan Song, Thomas Michaelsen Pethick

Overparameterization refers to the important phenomenon where the width of a neural network is chosen such that learning algorithms can provably attain zero loss in nonconvex training. The existing theory establishes such global convergence using various i ...

2021

Scalable Learning-Based Sampling Optimization For Compressive Dynamic MRI

Ruud van Heeswijk, Armin Eftekhari, Volkan Cevher, Baran Gözcü, Thomas Sanchez

Compressed sensing applied to magnetic resonance imaging (MRI) allows to reduce the scanning time by enabling images to be reconstructed from highly undersampled data. In this paper, we tackle the problem of designing a sampling mask for an arbitrary recon ...

IEEE2020

Double-Loop Unadjusted Langevin Algorithm

Armin Eftekhari, Volkan Cevher, Paul Thierry Yves Rolland, Ali Kavis

A well-known first-order method for sampling from log-concave probability distributions is the Unadjusted Langevin Algorithm (ULA). This work proposes a new annealing step-size schedule for ULA, which allows to prove new convergence guarantees for sampling ...

2020

Principal Component Analysis By Optimization Of Symmetric Functions Has No Spurious Local Optima

Armin Eftekhari

Principal component analysis (PCA) finds the best linear representation of data and is an indispensable tool in many learning and inference tasks. Classically, principal components of a dataset are interpreted as the directions that preserve most of its "e ...

SIAM PUBLICATIONS2020

MOSES: A Streaming Algorithm for Linear Dimensionality Reduction

Armin Eftekhari

This paper introduces Memory-limited Online Subspace Estimation Scheme (MOSES) for both estimating the principal components of streaming data and reducing its dimension. More specifically, in various applications such as sensor networks, the data vectors a ...

IEEE COMPUTER SOC2020

Streaming Principal Component Analysis From Incomplete Data

Armin Eftekhari

Linear subspace models are pervasive in computational sciences and particularly used for large datasets which are often incomplete due to privacy issues or sampling constraints. Therefore, a critical problem is developing an efficient algorithm for detecti ...

Microtome Publishing2019

An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints

Armin Eftekhari, Volkan Cevher, Ahmet Alacaoglu, Mehmet Fatih Sahin, Fabian Ricardo Latorre Gomez

We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex problems with nonlinear constraints. We characterize the total computational complexity of our method subject to a verifiable geometric condition, which is closely related to t ...

2019

Sparse Inverse Problems Over Measures: Equivalence Of The Conditional Gradient And Exchange Methods

Armin Eftekhari

We study an optimization program over nonnegative Borel measures that encourages sparsity in its solution. Efficient solvers for this program are in increasing demand, as it arises when learning from data generated by a "continuum-of-subspaces" model, a re ...

SIAM PUBLICATIONS2019

Fast and Provable ADMM for Learning with Generative Priors

Armin Eftekhari, Volkan Cevher, Fabian Ricardo Latorre Gomez

In this work, we propose a (linearized) Alternating Direction Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject to a nonconvex constraint. We focus on the special case where such constraint arises from the specification that a ...

2019