Skip to main content
archive
Search Submit Donate Log in
Press Enter to search · Advanced search

Mathematics > Optimization and Control

arXiv:0802.1766 (math)
[Submitted on 13 Feb 2008]

Title:Structured Semidefinite Representation of Some Convex Sets

Authors:J. William Helton, Jiawang Nie
View a PDF of the paper titled Structured Semidefinite Representation of Some Convex Sets, by J. William Helton and Jiawang Nie
View PDF HTML (experimental)
Abstract: Linear matrix Inequalities (LMIs) have had a major impact on control but formulating a problem as an LMI is an art. Recently there is the beginnings of a theory of which problems are in fact expressible as LMIs. For optimization purposes it can also be useful to have "lifts" which are expressible as LMIs. We show here that this is a much less restrictive condition and give methods for actually constructing lifts and their LMI representation.
Comments: 6 pages
Subjects: Optimization and Control (math.OC)
Cite as: arXiv:0802.1766 [math.OC]
  (or arXiv:0802.1766v1 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.0802.1766
arXiv-issued DOI via DataCite

Submission history

From: Jiawang Nie [view email]
[v1] Wed, 13 Feb 2008 06:57:10 UTC (11 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Structured Semidefinite Representation of Some Convex Sets, by J. William Helton and Jiawang Nie
  • View PDF
  • HTML (experimental)
  • TeX Source
view license

Current browse context:

math.OC
< prev   |   next >
new | recent | 2008-02
Change to browse by:
math

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
Loading...

BibTeX formatted citation

Data provided by:

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
We gratefully acknowledge support from our major funders, member institutions, , and all contributors.
About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)
Major funding support from
Simons Foundation Simons Foundation International Schmidt Sciences