2 citations · 3 across the 7 of their papers we have counts for
7 papers
Mirror-Free Proximal Methods
Abhijeet Vyas, Brian Bullins
We present a \emph{mirror-free} mirror prox (MFMP) algorithm, which extends the classic approach of Nemirovski (2004) to allow for proximal-like updates without the explicit need f…
Beyond First-Order Methods for -Structured Non-Monotone Variational Inequalities
Abhijeet Vyas, Brian Bullins
We propose novel high-order algorithms for a class of -structured non-monotone variational inequalities. In particular, work by Diakonikolas et al. (2021), which introduced…
Online Min-Max Optimization: From Individual Regrets to Cumulative Saddle Points
Abhijeet Vyas, Brian Bullins
We propose and study an online version of min-max optimization based on cumulative saddle points under a variety of performance measures beyond convex-concave settings. After first…
Programmable Quantum-Like bits from Signed Regular Graphs
Ethan Dickey, Abhijeet Vyas, Sabre Kais
Extending upon observations of the emergence of quantum-like (QL) states from classical complex synchronized networks, this work adds mathematical rigor to the analysis of single Q…
Beyond first-order methods for non-convex non-concave min-max optimization
Abhijeet Vyas, Brian Bullins
We propose a study of structured non-convex non-concave min-max problems which goes beyond standard first-order approaches. Inspired by the tight understanding established in recen…
Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML
Hrishikesh Viswanath, Md Ashiqur Rahman, Abhijeet Vyas +5
Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering, and mathematical problems involving func…
Competitive Gradient Optimization
Abhijeet Vyas, Kamyar Azizzadenesheli
We study the problem of convergence to a stationary point in zero-sum games. We propose competitive gradient optimization (CGO ), a gradient-based method that incorporates the inte…