2 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…