Conditional quadratic semidefinite programming: examples and methods
From MaRDI portal
Publication:489109
DOI10.1007/s40305-014-0048-9zbMath1338.90296OpenAlexW2041814638MaRDI QIDQ489109
Publication date: 27 January 2015
Published in: Journal of the Operations Research Society of China (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s40305-014-0048-9
Related Items
Parameter-related projection-based iterative algorithm for a kind of generalized positive semidefinite least squares problem ⋮ An Ordinal Weighted EDM Model for Nonmetric Multidimensional Scaling ⋮ Feasibility and a fast algorithm for Euclidean distance matrix optimization with ordinal constraints ⋮ Half thresholding eigenvalue algorithm for semidefinite matrix completion ⋮ Perturbation analysis of the Euclidean distance matrix optimization problem and its numerical implications ⋮ A Convex Matrix Optimization for the Additive Constant Problem in Multidimensional Scaling with Application to Locally Linear Embedding
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Interpolation of scattered data: distance matrices and conditionally positive definite functions
- Definiteness and semidefiniteness of quadratic forms revisited
- Conditionally positive functions and p-norm distance matrices
- Equality relating Euclidean distance cone to positive semidefinite cone
- An inexact primal-dual path following algorithm for convex quadratic SDP
- The best Euclidean fit to a given distance matrix in prescribed dimensions
- Approximation by matrices positive semidefinite on a subspace
- A cyclic projection algorithm via duality
- The embedding problem for predistance matrices
- Solving Euclidean distance matrix completion problems via semidefinite progrmming
- Hadamard inverses, square roots and products of almost semidefinite matrices
- Conditionally definite matrices
- Distance matrix completion by numerical optimization
- The positive definite completion problem relative to a subspace
- A nonsmooth version of Newton's method
- Modern multidimensional scaling. Theory and applications.
- Multidimensional scaling. I: Theory and method
- An augmented Lagrangian dual approach for the H-weighted nearest correlation matrix problem
- A Newton-CG Augmented Lagrangian Method for Semidefinite Programming
- An Alternating Projection Algorithm for Computing the Nearest Euclidean Distance Matrix
- A Quadratically Convergent Newton Method for Computing the Nearest Correlation Matrix
- Molecular Embedding via a Second Order Dissimilarity Parameterized Approach
- Calibrating Least Squares Semidefinite Programming with Equality and Inequality Constraints
- Constraint Nondegeneracy, Strong Regularity, and Nonsingularity in Semidefinite Programming
- Optimization and nonsmooth analysis
- Numerical Optimization
- Analysis of Nonsmooth Symmetric-Matrix-Valued Functions with Applications to Semidefinite Complementarity Problems
- A TWO-REGIME, STOCHASTIC-VOLATILITY EXTENSION OF THE LIBOR MARKET MODEL
- An Inexact Accelerated Proximal Gradient Method for Large Scale Linearly Constrained Convex SDP
- Constrained Best Euclidean Distance Embedding on a Sphere: A Matrix Optimization Approach
- A Semismooth Newton Method for the Nearest Euclidean Distance Matrix Problem
- A convex quadratic semi-definite programming approach to the partial additive constant problem in multidimensional scaling
- Semismooth Matrix-Valued Functions
- Methods of conjugate gradients for solving linear systems
- Numerical optimization. Theoretical and practical aspects. Transl. from the French
