This paper surveys the recent attempts at leveraging machine learning to solve constrained
optimization problems. It focuses on surveying the work on integrating combinatorial solvers …

Structured pruning is a commonly used technique in deploying deep neural networks
(DNNs) onto resource-constrained devices. However, the existing pruning methods are …

The goal of this article is to promote the use of fixed point strategies in data science by
showing that they provide a simplifying and unifying framework to model, analyze, and solve …

We give safe screening rules to eliminate variables from regression with $\ell_0 $
regularization or cardinality constraint. These rules are based on guarantees that a feature …

We consider the problem of estimating a vector from its noisy measurements using a prior
specified only through a denoising function. Recent work on plug-and-play priors (PnP) and …

Convex sparsity-inducing regularizations are ubiquitous in high-dimensional machine
learning, but solving the resulting optimization problems can be slow. To accelerate solvers …

Sparse coding is typically solved by iterative optimization techniques, such as the Iterative
Shrinkage-Thresholding Algorithm (ISTA). Unfolding and learning weights of ISTA using …

Parallel optimization has become popular for largescale learning in the past decades.
However, existing methods suffer from huge computational cost, memory usage, and …

Sparsity regularized loss minimization problems play an important role in various fields
including machine learning, data mining, and modern statistics. Proximal gradient descent …

It is promising to solve linear inverse problems by unfolding iterative algorithms (eg, iterative
shrinkage thresholding algorithm (ISTA)) as deep neural networks (DNNs) with learnable …