4 citations · 17 across the 5 of their papers we have counts for
6 papers
Faster Randomized Interior Point Methods for Tall/Wide Linear Programs
Agniva Chowdhury, Gregory Dexter, Palma London +2
Linear programming (LP) is an extremely useful tool which has been successfully applied to solve various problems in a wide range of areas, including operations research, engineeri…
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming
Gregory Dexter, Agniva Chowdhury, Haim Avron +1
Interior point methods (IPMs) are a common approach for solving linear programs (LPs) with strong theoretical guarantees and solid empirical performance. The time complexity of the…
Approximation Algorithms for Sparse Principal Component Analysis
Agniva Chowdhury, Petros Drineas, David P. Woodruff +1
Principal component analysis (PCA) is a widely used dimension reduction technique in machine learning and multivariate statistics. To improve the interpretability of PCA, various a…
Speeding up Linear Programming using Randomized Linear Algebra
Agniva Chowdhury, Palma London, Haim Avron +1
Linear programming (LP) is an extremely useful tool and has been successfully applied to solve various problems in a wide range of areas, including operations research, engineering…
Randomized Iterative Algorithms for Fisher Discriminant Analysis
Agniva Chowdhury, Jiasen Yang, Petros Drineas
Fisher discriminant analysis (FDA) is a widely used method for classification and dimensionality reduction. When the number of predictor variables greatly exceeds the number of obs…
Structural Conditions for Projection-Cost Preservation via Randomized Matrix Multiplication
Agniva Chowdhury, Jiasen Yang, Petros Drineas
Projection-cost preservation is a low-rank approximation guarantee which ensures that the cost of any rank- projection can be preserved using a smaller sketch of the original da…