2 citations · 2 across the 5 of their papers we have counts for
5 papers
Distributed Randomized Block Stochastic Gradient Tracking Method
Farzad Yousefian, Jayesh Yevale, Harshal D. Kaushik
We consider distributed optimization over networks where each agent is associated with a smooth and strongly convex local objective function. We assume that the agents only have ac…
An Incremental Gradient Method for Optimization Problems with Variational Inequality Constraints
Harshal D. Kaushik, Sepideh Samadi, Farzad Yousefian
We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associ…
A Method with Convergence Rates for Optimization Problems with Variational Inequality Constraints
Harshal D. Kaushik, Farzad Yousefian
We consider a class of optimization problems with Cartesian variational inequality (CVI) constraints, where the objective function is convex and the CVI is associated with a monoto…
An Incremental Gradient Method for Large-scale Distributed Nonlinearly Constrained Optimization
Harshal D. Kaushik, Farzad Yousefian
Motivated by applications arising from sensor networks and machine learning, we consider the problem of minimizing a finite sum of nondifferentiable convex functions where each com…
A Randomized Block Coordinate Iterative Regularized Gradient Method for High-dimensional Ill-posed Convex Optimization
Harshal Kaushik, Farzad Yousefian
Motivated by high-dimensional nonlinear optimization problems as well as ill-posed optimization problems arising in image processing, we consider a bilevel optimization model where…