A convex quadratic semi-definite programming approach to the partial additive constant problem in multidimensional scaling

DOI10.1080/00949655.2011.579970zbMath1293.90050OpenAlexW2006764697MaRDI QIDQ5300818

Nai-Hua Xiu, Hou-Duo Qi

Publication date: 28 June 2013

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2011.579970

zbMATH Keywords

Lagrange multiplierFarkas' lemmamultidimensional scalingpositive semi-definite programmingpartial additive constant

Mathematics Subject Classification ID

Semidefinite programming (90C22) Convex programming (90C25) Applications of mathematical programming (90C90)

Related Items

Conditional quadratic semidefinite programming: examples and methods ⋮ Regularized multidimensional scaling with radial basis functions ⋮ Discriminant analysis of regularized multidimensional scaling

Cites Work

• Unnamed Item
• Unnamed Item
• Computing the nearest correlation matrix--a problem from finance
• The additive constant problem in multidimensional scaling
• The analytical solution of the additive constant problem
• An inexact primal-dual path following algorithm for convex quadratic SDP
• Non-interior continuation methods for solving semidefinite complementarity problems
• A general nonmetric technique for finding the smallest coordinate space for a configuration of points
• Some boundary conditions for a monotone analysis of symmetric matrices
• A new solution to the additive constant problem in metric multidimensional scaling
• Remarks to Maurice Frechet's article ``Sur la definition axiomatique d'une classe d'espaces vectoriels distancies applicables vectoriellement sur l'espace de Hilbert
• An augmented Lagrangian dual approach for the H-weighted nearest correlation matrix problem
• Local Duality of Nonlinear Semidefinite Programming
• A Quadratically Convergent Newton Method for Computing the Nearest Correlation Matrix
• Pricing and hedging basket options to prespecified levels of acceptability
• A Dual Approach to Semidefinite Least-Squares Problems
• The Strong Second-Order Sufficient Condition and Constraint Nondegeneracy in Nonlinear Semidefinite Programming and Their Implications
• Least-Squares Covariance Matrix Adjustment