activity
20202022
most citedNesterov Acceleration for Equality-Constrained Convex Optimization via Continuously Differentiable Penalty Functions

14 citations · 15 across the 5 of their papers we have counts for

collaborators

7 papers

math.OC2022

Accelerated Algorithms for a Class of Optimization Problems with Constraints

Anjali Parashar, Priyank Srivastava, Anuradha M. Annaswamy

This paper presents a framework to solve constrained optimization problems in an accelerated manner based on High-Order Tuners (HT). Our approach is based on reformulating the orig…

eess.SY2022

Learning Invariant Stabilizing Controllers for Frequency Regulation under Variable Inertia

Priyank Srivastava, Patricia Hidalgo-Gonzalez, Jorge Cortes

Declines in cost and concerns about the environmental impact of traditional generation have boosted the penetration of renewables and non-conventional distributed energy resources…

math.OC202114 cited

Nesterov Acceleration for Equality-Constrained Convex Optimization via Continuously Differentiable Penalty Functions

Priyank Srivastava, Jorge Cortes

We propose a framework to use Nesterov's accelerated method for constrained convex optimization problems. Our approach consists of first reformulating the original problem as an un…

math.OC2021

Solving Linear Equations with Separable Problem Data over Directed Networks

Priyank Srivastava, Jorge Cortes

This paper deals with linear algebraic equations where the global coefficient matrix and constant vector are given respectively, by the summation of the coefficient matrices and co…

eess.SY2020

Enabling DER Participation in Frequency Regulation Markets

Priyank Srivastava, Chin-Yao Chang, Jorge Cortes

Distributed energy resources (DERs) are playing an increasing role in ancillary services for the bulk grid, particularly in frequency regulation. In this paper, we propose a framew…

math.OC20201 cited

Network Optimization via Smooth Exact Penalty Functions Enabled by Distributed Gradient Computation

Priyank Srivastava, Jorge Cortes

This paper proposes a distributed algorithm for a network of agents to solve an optimization problem with separable objective function and locally coupled constraints. Our strategy…