Deeksha Adil

most citedFaster p-norm minimizing flows, via smoothed q-norm problems

1 citations · 1 across the 1 of their papers we have counts for

collaborators

3 papers

cs.DS2021

Almost-linear-time Weighted $ℓ_{p}$ -norm Solvers in Slightly Dense Graphs via Sparsification

Deeksha Adil, Brian Bullins, Rasmus Kyng +1

We give almost-linear-time algorithms for constructing sparsifiers with $n p o l y (lo g n)$ edges that approximately preserve weighted $(ℓ_{2}^{2} + ℓ_{p}^{p})$ flow or voltage obj…

cs.DS2019★ 1 cited

Faster p-norm minimizing flows, via smoothed q-norm problems

Deeksha Adil, Sushant Sachdeva

We present faster high-accuracy algorithms for computing $ℓ_{p}$ -norm minimizing flows. On a graph with $m$ edges, our algorithm can compute a $(1 + 1/ poly (m))$ -approximate u…

cs.DS2019

Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression

Deeksha Adil, Richard Peng, Sushant Sachdeva

Linear regression in $ℓ_{p}$ -norm is a canonical optimization problem that arises in several applications, including sparse recovery, semi-supervised learning, and signal processi…

Almost-linear-time Weighted ℓp​-norm Solvers in Slightly Dense Graphs via Sparsification

Faster p-norm minimizing flows, via smoothed q-norm problems

Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression

Almost-linear-time Weighted $ℓ_{p}$ -norm Solvers in Slightly Dense Graphs via Sparsification