A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

Alternating minimization represents a widely applicable and empirically successful
approach for finding low-rank matrices that best fit the given data. For example, for the …

Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with
many important applications in machine learning and statistics. In this paper we propose a …

We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex
objective for the rank minimization problem and a closely related family of semidefinite …

In this paper, we address compressed sensing of a low-rank matrix posing the inverse
problem as an approximation problem with a specified target rank of the solution. A simple …

Minimizing the rank of a matrix subject to constraints is a challenging is a challenging
problem that arises in many control applications including controller design, realization …

Influenza viruses have been responsible for large losses of lives around the world and
continue to present a great public health challenge. Antigenic characterization based on …

Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in
many applications in machine learning, control theory, and discrete geometry. This class of …

One-bit quantization with time-varying sampling thresholds (also known as random
dithering) has recently found significant utilization potential in statistical signal processing …

This paper investigates an iterative approach to solve the general rank-constrained
optimization problems (RCOPs) defined to optimize a convex objective function subject to a …