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researcher

Stephen P. Boyd

Stanford

98 papers hereh-index 127143.4k citations690 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author3
  • middle author19
  • last author76

Across the 98 of 98 papers where every author was matched, so the position is known.

fields
  • math.OC54
  • cs.LG12
  • stat.ML7
  • cs.DC2
  • cs.SI2
  • eess.SY2
affiliations
  • Stanford
Homepage
same name
  • Stephen P. Boyd — 5 papers, h 2
  • Stephen P. Boyd — 3 papers, h 1
  • Stephen P. Boyd — 3 papers, h 3
  • Stephen P. Boyd — 1 paper, h 1
  • Stephen P. Boyd — 1 paper, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20072023
most citedOSQP: An Operator Splitting Solver for Quadratic Programs

1.2k citations · 3.4k across the 72 of their papers we have counts for

collaborators
Showing 2017 · math.OCShow all

4 papers · 2 filters

math.OC2017★ 1.2k cited

OSQP: An Operator Splitting Solver for Quadratic Programs

Bartolomeo Stellato, Goran Banjac, Paul Goulart +2

We present a general-purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requ…

math.OC2017

A Rewriting System for Convex Optimization Problems

Akshay Agrawal, Robin Verschueren, Steven Diamond +1

We describe a modular rewriting system for translating optimization problems written in a domain-specific language to forms compatible with low-level solver interfaces. Translation…

math.OC2017★ 3 cited

On the convergence of mirror descent beyond stochastic convex programming

Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos +2

In this paper, we examine the convergence of mirror descent in a class of stochastic optimization problems that are not necessarily convex (or even quasi-convex), and which we call…

math.OC2017

General Heuristics for Nonconvex Quadratically Constrained Quadratic Programming

Jaehyun Park, Stephen Boyd

We introduce the Suggest-and-Improve framework for general nonconvex quadratically constrained quadratic programs (QCQPs). Using this framework, we generalize a number of known met…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.