Projection methods for conic feasibility problems: applications to polynomial sum-of-squares decompositions

DOI10.1080/10556780903191165zbMath1228.90071OpenAlexW2071410137MaRDI QIDQ3093033

Jérôme Malick, Didier Henrion

Publication date: 12 October 2011

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556780903191165

zbMATH Keywords

convex conessemidefinite programmingpositive polynomialsleast-squares problemsprojection algorithms

Mathematics Subject Classification ID

Semidefinite programming (90C22)

Related Items (8)

Decomposition Methods for Sparse Matrix Nearness Problems ⋮ Finding largest small polygons with gloptipoly ⋮ A fixed-point method for approximate projection onto the positive semidefinite cone ⋮ Error bounds, facial residual functions and applications to the exponential cone ⋮ An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization ⋮ Computation of Sum of Squares Polynomials from Data Points ⋮ An extended projective formula and its application to semidefinite optimization ⋮ Projection Methods in Conic Optimization

Uses Software

• Matlab
• SeDuMi
• GloptiPoly
• Benchmarks for Optimization Software
• SDLS
• PLCP
• LMIRank
• HIFOO
• HANSO

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