Scalable Rapidly Deployable Convex Optimization for Data Analytics

Abstract

Over the period of the contract we have developed the full stack for wide use of convex optimization, in machine learning and many other areas. We have developed a new open-source domain specific language (DSL) for convex optimization, in Python, Julia, and recently, in R. Each of these has been published, and each are widely used and cited. We have developed a novel open-source solver, SCS, that is bundled with the DSLs, and solves any combination of linear programs, SOCPs, SDPs, exponential cone programs, and power cone programs. CVXPY supports basic methods for distributed optimization, on multiple heterogenous platforms. We have also done basic research in various application areas, using CVXPY, to demonstrate its usefulness. See attached report for publication information.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2018
Accession Number
AD1054000

Entities

People

  • Stephen Boyd

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Computer Vision
  • Contracts
  • Convex Programming
  • Data Analysis
  • Data Science
  • Dynamic Programming
  • Information Science
  • Language
  • Linear Programming
  • Machine Learning
  • Mathematical Programming
  • Operations Research
  • Optimization
  • Systems Engineering

Readers

  • Distributed Systems and Data Platform Development
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms