Matrix Support Functionals for Inverse Problems, Regularization, and Learning

DOI10.1137/140984178zbMath1327.46075OpenAlexW878886853MaRDI QIDQ5258946

James V. Burke, Tim Hoheisel

Publication date: 24 June 2015

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/140984178

zbMATH Keywords

quadratic programmingnormal conesubdifferentialvalue functionsupport functionnuclear normmatrix-fractional function

Mathematics Subject Classification ID

Quadratic programming (90C20) Optimality conditions and duality in mathematical programming (90C46) Nonsmooth analysis (49J52) Set-valued and variational analysis (49J53) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10)

Related Items (4)

Convex Geometry of the Generalized Matrix-Fractional Function ⋮ A note on the K-epigraph ⋮ Variational Properties of Matrix Functions via the Generalized Matrix-Fractional Function ⋮ A Study of Convex Convex-Composite Functions via Infimal Convolution with Applications

Cites Work

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• Definiteness and semidefiniteness of quadratic forms revisited
• Equality relating Euclidean distance cone to positive semidefinite cone
• Convex multi-task feature learning
• Convex analysis and nonlinear optimization. Theory and examples
• Über das Vorkommen definiter und semidefiniter Formen in Scharen quadratischer Formen
• Lectures on Modern Convex Optimization
• Geometric Methods and Applications
• A Minimax Theorem with Applications to Machine Learning, Signal Processing, and Finance
• Matrix Analysis
• Variational Analysis
• ON A PARTITIONED INVERSION FORMULA HAVING USEFUL APPLICATIONS IN ECONOMETRICS

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