390 citations · 3.4k across the 56 of their papers we have counts for
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Optimization Methods for Designing Sequences with Low Autocorrelation Sidelobes
Junxiao Song, Prabhu Babu, Daniel P. Palomar
Unimodular sequences with low autocorrelations are desired in many applications, especially in the area of radar and code-division multiple access (CDMA). In this paper, we propose…
A Parallel Stochastic Approximation Method for Nonconvex Multi-Agent Optimization Problems
Yang Yang, Gesualdo Scutari, Daniel P. Palomar +1
Consider the problem of minimizing the expected value of a (possibly nonconvex) cost function parameterized by a random (vector) variable, when the expectation cannot be computed a…
Sparse Generalized Eigenvalue Problem via Smooth Optimization
Junxiao Song, Prabhu Babu, Daniel P. Palomar
In this paper, we consider an -norm penalized formulation of the generalized eigenvalue problem (GEP), aimed at extracting the leading sparse generalized eigenvector of a…
Convex separable problems with linear and box constraints in signal processing and communications
Antonio A. D'Amico, Luca Sanguinetti, Daniel P. Palomar
In this work, we focus on separable convex optimization problems with box constraints and a set of triangular linear constraints. The solution is given in closed-form as a function…
Regularized Tyler's Scatter Estimator: Existence, Uniqueness, and Algorithms
Ying Sun, Prabhu Babu, Daniel P. Palomar
This paper considers the regularized Tyler's scatter estimator for elliptical distributions, which has received considerable attention recently. Various types of shrinkage Tyler's…
Convex separable problems with linear and box constraints
Antonio A. D'Amico, Luca Sanguinetti, Daniel P. Palomar
In this work, we focus on separable convex optimization problems with linear and box constraints and compute the solution in closed-form as a function of some Lagrange multipliers…