Numerical approaches for collaborative data processing

DOI10.1007/s11081-006-0350-4zbMath1171.65400OpenAlexW2102117606MaRDI QIDQ1027146

Ryan Feeley, Michael Frenklach, Pete Seiler, Andrew K. Packard

Publication date: 30 June 2009

Published in: Optimization and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11081-006-0350-4

zbMATH Keywords

predictionsemidefinite programmingnumerical examplesmodel validationdata processingpolynomial optimizationssums-of-squares polynomials

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Quadratic programming (90C20) Theory of data (68P99)

Related Items (4)

Comparison of Statistical and Deterministic Frameworks of Uncertainty Quantification ⋮ Consistency Analysis for Massively Inconsistent Datasets in Bound-to-Bound Data Collaboration ⋮ Representing Model Discrepancy in Bound-to-Bound Data Collaboration ⋮ Efficient numerical methods for the optimisation of large kinetic reaction mechanisms

Uses Software

• Sostools
• SeDuMi
• GRI-Mech 3.0
• SeDuMi Interface

Cites Work

• Extremal psd forms with few terms
• An algorithm for sums of squares of real polynomials
• Semidefinite programming relaxations for semialgebraic problems
• Symmetry groups, semidefinite programs, and sums of squares
• Global Optimization with Polynomials and the Problem of Moments
• Linear Matrix Inequalities in System and Control Theory
• Semidefinite Programming
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