Code generation for embedded convex optimization

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Abstract/Contents

Abstract
Convex optimization is widely used, in many fields, but is nearly always constrained to problems solved in a few minutes or seconds, and even then, nearly always with a human in the loop. The advent of parser-solvers has made convex optimization simpler and more accessible, and greatly increased the number of people using convex optimization. Most current applications, however, are for the design of systems or analysis of data. It is possible to use convex optimization for real-time or embedded applications, where the optimization solver is a part of a larger system. Here, the optimization algorithm must find solutions much faster than a generic solver, and often has a hard, real-time deadline. Use in embedded applications additionally means that the solver cannot fail, and must be robust even in the presence of relatively poor quality data. For ease of embedding, the solver should be simple, and have minimal dependencies on external libraries. Convex optimization has been successfully applied in such settings in the past. However, they have usually necessitated a custom, hand-written solver. This requires signficant time and expertise, and has been a major factor preventing the adoption of convex optimization in embedded applications. This work describes the implementation and use of a prototype code generator for convex optimization, CVXGEN, that creates high-speed solvers automatically. Using the principles of disciplined convex programming, CVXGEN allows the user to describe an optimization problem in a convenient, high-level language, then receive code for compilation into an extremely fast, robust, embeddable solver.

Description

Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2011
Issuance monographic
Language English

Creators/Contributors

Associated with Mattingley, Jacob Elliot
Associated with Stanford University, Department of Electrical Engineering
Primary advisor Boyd, Stephen P
Thesis advisor Boyd, Stephen P
Thesis advisor Saunders, Michael A
Thesis advisor Ye, Yinyu
Advisor Saunders, Michael A
Advisor Ye, Yinyu

Subjects

Genre Theses

Bibliographic information

Statement of responsibility Jacob Elliot Mattingley.
Note Submitted to the Department of Electrical Engineering.
Thesis Thesis (Ph.D.)--Stanford University, 2011.
Location electronic resource

Access conditions

Copyright
© 2011 by Jacob Elliot Mattingley

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