Arun Jambulapati

activity

20242026

collaborators

7 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…

cs.DS2025

Radial Isotropic Position via an Implicit Newton's Method

Arun Jambulapati, Jonathan Li, Kevin Tian

Placing a dataset $A = {a_{i}}_{i \in [n]} \subset R^{d}$ in radial isotropic position, i.e., finding an invertible $R \in R^{d \times d}$ such th…

math.OC2024

Extracting Dual Solutions via Primal Optimizers

Yair Carmon, Arun Jambulapati, Liam O'Carroll +1

We provide a general method to convert a "primal" black-box algorithm for solving regularized convex-concave minimax optimization problems into an algorithm for solving the associa…

cs.DS2024

Eulerian Graph Sparsification by Effective Resistance Decomposition

Arun Jambulapati, Sushant Sachdeva, Aaron Sidford +2

We provide an algorithm that, given an $n$ -vertex $m$ -edge Eulerian graph with polynomially bounded weights, computes an $O ˘ (n lo g^{2} n \cdot ε^{- 2})$ -edge $\vare…

cs.LG2024

Testing Calibration in Nearly-Linear Time

Lunjia Hu, Arun Jambulapati, Kevin Tian +1

In the recent literature on machine learning and decision making, calibration has emerged as a desirable and widely-studied statistical property of the outputs of binary prediction…

math.OC2024

Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization

Arun Jambulapati, Aaron Sidford, Kevin Tian

We develop a new parallel algorithm for minimizing Lipschitz, convex functions with a stochastic subgradient oracle. The total number of queries made and the query depth, i.e., the…