Probabilistic algorithms for extreme point identification

DOI10.1080/09720502.2007.10700478zbMath1218.90111OpenAlexW2317013597MaRDI QIDQ3508860

Richard J. Caron, Shafiu Jibrin, Arnon Boneh

Publication date: 1 July 2008

Published in: Journal of Interdisciplinary Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/09720502.2007.10700478

zbMATH Keywords

convex hullprobabilistic methodredundancylinear inequalitiessolid angles

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Linear programming (90C05) Convex sets in (n) dimensions (including convex hypersurfaces) (52A20) Randomized algorithms (68W20)

Related Items

Semidefinite diagonal directions Monte Carlo algorithms for detecting necessary linear matrix inequality constraints

Uses Software

• SeDuMi
• Qhull

Cites Work

• Unnamed Item
• Redundancy in mathematical programming. A state-of-the-art survey
• On the best case performance of hit and run methods for detecting necessary constraints
• Hit-and-run algorithms for the identification of nonredundant linear inequalities
• The coupon-collector problem revisited — a survey of engineering problems and computational methods
• Lectures on Polytopes
• Using SeDuMi 1.02, A Matlab toolbox for optimization over symmetric cones
• Semidefinite Programming