Spectral Projected Gradient Method for the Positive Semi-definite Procrustes Problem
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Description:
This code implements an algorithm to solve the PSD Procrustes problem:
given rectangular matrices A and B, find the symmetric positive
semidefinite matrix X that minimizes the Frobenius norm of XA-B, i.e.
min 0.5||XA-B||_F^{2} s.t. X\in S_{+}(n),
where S_{+}(n) denote the set form by the all symmetric and
positive semi-definite matrices of size n-by-n with real entries.
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Reference:
Harry F. Oviedo Leon
"Un Método de Gradiente Projectado Espectral para el Problema
Procrustes Semidefinido Positivo". (ResearchGate)
In english: "Spectral Projected Gradient Method for the Positive
Semi-definite Procrustes Problem".
Author: Harry F. Oviedo Leon
Date: 01-Oct-2017
See help
>> help demo_PSDP:
% Example 1:
>> demo_PSDP()
% Example 2:
>> demo_PSDP(1000,1000,1,0)
Cite As
Harry Oviedo (2024). Spectral Projected Gradient Method for the Positive Semi-definite Procrustes Problem (https://www.mathworks.com/matlabcentral/fileexchange/64597-spectral-projected-gradient-method-for-the-positive-semi-definite-procrustes-problem), MATLAB Central File Exchange. Retrieved .
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Version | Published | Release Notes | |
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1.0.0.0 |