activity
20192025
most citedMaximizing the Minimum Eigenvalue in Constant Dimension

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.DM2025

An Algorithmic Upper Bound for Permanents via a Permanental Schur Inequality

Aditi Laddha, Madhusudhan Reddy Pittu

Computing the permanent of a non-negative matrix is a computationally challenging, \#P-complete problem with wide-ranging applications. We introduce a novel permanental analogue of…

cs.DS20241 cited

Maximizing the Minimum Eigenvalue in Constant Dimension

Adam Brown, Aditi Laddha, Mohit Singh

In an instance of the minimum eigenvalue problem, we are given a collection of vectors , and the goal is to pick a subset $B\subseteq [n…

cs.DS2024

Approximation Algorithms for the Weighted Nash Social Welfare via Convex and Non-Convex Programs

Adam Brown, Aditi Laddha, Madhusudhan Reddy Pittu +1

In an instance of the weighted Nash Social Welfare problem, we are given a set of indivisible items, , and agents, , where each agent $i \in \math…

cs.DS2022

Efficient Determinant Maximization for All Matroids

Adam Brown, Aditi Laddha, Madhusudhan Pittu +1

Determinant maximization provides an elegant generalization of problems in many areas, including convex geometry, statistics, machine learning, fair allocation of goods, and networ…

cs.DS2022

A Unified Approach to Discrepancy Minimization

Nikhil Bansal, Aditi Laddha, Santosh S. Vempala

We study a unified approach and algorithm for constructive discrepancy minimization based on a stochastic process. By varying the parameters of the process, one can recover various…

cs.DS2019

Strong Self-Concordance and Sampling

Aditi Laddha, Yin Tat Lee, Santosh Vempala

Motivated by the Dikin walk, we develop aspects of an interior-point theory for sampling in high dimension. Specifically, we introduce a symmetric parameter and the notion of stron…