111 citations · 338 across the 50 of their papers we have counts for
31 papers · 1 filter
Decentralized Stochastic Constrained Optimization via Prox-Linearization
Shivangi Dubey Sharma, Basil M. Idrees, Lavish Arora +1
This paper studies consensus-based decentralized stochastic optimization for minimizing possibly non-convex expected objectives with convex non-smooth regularizers and nonlinear fu…
Hinge-Proximal Stochastic Gradient Methods for Convex Optimization with Functional Constraints
Vaibhav Rajoriya, Prateek Priyaranjan Pradhan, Ketan Rajawat
This paper considers stochastic convex optimization problems with smooth functional constraints arising in constrained estimation and robust signal recovery. We operate in the high…
Stochastic Sequential Quadratic Programming for Optimization with Functional Constraints
Panchajanya Sanyal, Srujan Teja Thomdapu, Ketan Rajawat
Stochastic convex optimization problems with nonlinear functional constraints are ubiquitous in signal processing applications including constrained least-squares, set-membership a…
Analysis of Decentralized Stochastic Successive Convex Approximation for composite non-convex problems
Basil M. Idrees, Shivangi Dubey Sharma, Ketan Rajawat
This work considers the decentralized successive convex approximation (SCA) method for minimizing stochastic non-convex objectives subject to convex constraints, along with possibl…
Constrained Stochastic Recursive Momentum Successive Convex Approximation
Basil M. Idrees, Lavish Arora, Ketan Rajawat
We consider stochastic optimization problems with non-convex functional constraints, such as those arising in trajectory generation, sparse approximation, and robust classification…
Sharpened Lazy Incremental Quasi-Newton Method
Aakash Lahoti, Spandan Senapati, Ketan Rajawat +1
The problem of minimizing the sum of functions in dimensions is ubiquitous in machine learning and statistics. In many applications where the number of observations is…