LearnMelt: Learning to Minimize Dissemination on Large Graphs
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Updated
May 22, 2018 - MATLAB
LearnMelt: Learning to Minimize Dissemination on Large Graphs
Hessian spectral analysis with tensorflow1.x
Python code that solves the eigensystem associated with Hermitan matrices. Demonstrated by finding the the first few eigenvalues and the corresponding eigenvectors of the aharmonic oscillator Hamiltonian.
Algorithms of computational math: Legendre polynomial; Gauss method; Cholesky method; Method of Squares; Newton interpolation; Inverse Gauss method; Interpolation by cubic splines; Gauss Zeidel method; Jacobi method; Eigenvalues; Euler method; Left/Right rectangle method; Simpson method; The method of dividing a segment in half; Newton's method;
This program is implemented as a project for EE 242 course, and it implements Normalized Power Iteration with Deflation algorithmm to calculate most dominant eigenvalue, its eigenvector and the second most dominant eigenvalue.
Singular generalized eigenvalue problems.
Employed Principal Component Analysis to represent input image of faces as a linear Combination of amorphous facial structures called "Ghost Faces" or "Eigenfaces" thereby reducing dimentionality of the data and improving performance
Computational Linear Algebra course covering topics like iterative methods, matrix decompositions, and applications. It includes theoretical concepts, practical exercises, and code. Advanced methods like QR factorization, spectral theorem, and iterative solvers for linear systems.
Linear Algebra for Machine Learning and Data Science
ForEig - A Fortran library for eigenvalue and eigenvector calculations.
Face Recognition using method of Eigenfaces
(DEPRECATED) HOWTO: use LAPACK on a Swift app to compute the eigenvalues & eigenvectors on macOS/iOS/Linux
Repository for the HU-Berlin course Numerical Introductory Seminar
Eigenvalues and eigenfunction of Laplace-Beltrami operator on a Möbius strip.
Calculate bounds on the Perron (dominant) eigenvalue of nonnegative matrices
Visualizing high dimensional data with PCA and LDA.
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