Deeksha Adil

activity

20192025

most citedFast, Provably convergent IRLS Algorithm for p-norm Linear Regression

14 citations · 18 across the 6 of their papers we have counts for

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Showing 2019Show all

3 papers · 1 filter

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★ 14 cited

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…

cs.DS2019

Iterative Refinement for $ℓ_{p}$ -norm Regression

Deeksha Adil, Rasmus Kyng, Richard Peng +1

We give improved algorithms for the $ℓ_{p}$ -regression problem, $min_{x} ∥ x ∥_{p}$ such that $A x = b,$ for all $p \in (1, 2) \cup (2, \infty) .$ Our algorithms obtain a high accur…

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

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

Iterative Refinement for ℓp​-norm Regression

Iterative Refinement for $ℓ_{p}$ -norm Regression