Deeksha Adil

collaborators

3 papers

math.OC2026

Convex optimization with $p$ -norm oracles

Deeksha Adil, Brian Bullins, Arun Jambulapati +1

In recent years, there have been significant advances in efficiently solving $ℓ_{s}$ -regression using linear system solvers and $ℓ_{2}$ -regression [Adil-Kyng-Peng-Sachdeva, J. AC…

math.OC2025

Balancing Gradient and Hessian Queries in Non-Convex Optimization

Deeksha Adil, Brian Bullins, Aaron Sidford +1

We develop optimization methods which offer new trade-offs between the number of gradient and Hessian computations needed to compute the critical point of a non-convex function. We…

cs.DS2025

Acceleration Meets Inverse Maintenance: Faster $ℓ_{\infty}$ -Regression

Deeksha Adil, Shunhua Jiang, Rasmus Kyng

We propose a randomized multiplicative weight update (MWU) algorithm for $ℓ_{\infty}$ regression that runs in $O (n^{2 + 1/22.5} poly (1/ I^µ))$ time whe…

Convex optimization with p-norm oracles

Balancing Gradient and Hessian Queries in Non-Convex Optimization

Acceleration Meets Inverse Maintenance: Faster ℓ∞​-Regression

Convex optimization with $p$ -norm oracles

Acceleration Meets Inverse Maintenance: Faster $ℓ_{\infty}$ -Regression