2 citations · 3 across the 6 of their papers we have counts for
7 papers
GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra
Pratik Rathore, Zachary Frangella, Parth Nobel +2
This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA). Despite substantial…
Disciplined Nonlinear Programming
Daniel Cederberg, William Zhang, Parth Nobel +1
We introduce disciplined nonlinear programming (DNLP), a syntax for specifying nonlinear programming problems. DNLP is inspired by disciplined convex programming (DCP) and allows s…
Differentiating Through a Quadratic Cone Program
Quill Healey, Parth Nobel, Stephen Boyd
Quadratic cone programs are rapidly becoming the standard canonical form for convex optimization problems. In this paper we address the question of differentiating the solution map…
CuClarabel: GPU Acceleration for a Conic Optimization Solver
Yuwen Chen, Danny Tse, Parth Nobel +2
We present the GPU implementation of the general-purpose interior-point solver Clarabel for convex optimization problems with conic constraints. We introduce a mixed parallel compu…
Fast Path Planning Through Large Collections of Safe Boxes
Tobia Marcucci, Parth Nobel, Russ Tedrake +1
We present a fast algorithm for the design of smooth paths (or trajectories) that are constrained to lie in a collection of axis-aligned boxes. We consider the case where the numbe…
Tractable Evaluation of Stein's Unbiased Risk Estimate with Convex Regularizers
Parth Nobel, Emmanuel Candès, Stephen Boyd
Stein's unbiased risk estimate (SURE) gives an unbiased estimate of the risk of any estimator of the mean of a Gaussian random vector. We focus here on the case when the e…
Computing Tighter Bounds on the -Queens Constant via Newton's Method
Parth Nobel, Akshay Agrawal, Stephen Boyd
In recent work Simkin shows that bounds on an exponent occurring in the famous -queens problem can be evaluated by solving convex optimization problems, allowing him to find bou…