Smooth convex approximation to the maximum eigenvalue function

DOI10.1007/s10898-004-8271-2zbMath1066.90081OpenAlexW2143711883MaRDI QIDQ1777442

Xin Chen, Liqun Qi, Hou-Duo Qi, Kok Lay Teo

Publication date: 17 May 2005

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-004-8271-2

zbMATH Keywords

convex optimizationeigenvalue function

Mathematics Subject Classification ID

Convex programming (90C25) Eigenvalues, singular values, and eigenvectors (15A18)

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Cites Work

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• Well-posed optimization problems
• On the convergence of the exponential multiplier method for convex programming
• An aggregate function method for nonlinear programming
• Tikhonov regularization methods for variational inequality problems
• Non-interior continuation methods for solving semidefinite complementarity problems
• A regularization Newton method for solving nonlinear complementarity problems
• A non-interior continuation method for generalized linear complementarity problems
• Regularization of P₀-Functions in Box Variational Inequality Problems
• Structural and Stability Properties of P ₀ Nonlinear Complementarity Problems
• Twice Differentiable Spectral Functions
• Matrix Analysis
• Beyond Monotonicity in Regularization Methods for Nonlinear Complementarity Problems
• A Regularized Smoothing Newton Method for Box Constrained Variational Inequality Problems with P0-Functions
• A Smoothing Newton Method for Extended Vertical Linear Complementarity Problems
• The $\U$-Lagrangian of the Maximum Eigenvalue Function
• Derivatives of Spectral Functions
• Second Derivatives for Optimizing Eigenvalues of Symmetric Matrices
• On Eigenvalue Optimization
• Penalty and Barrier Methods: A Unified Framework
• A second-order bundle method to minimize the maximum eigenvalue function.