Explore MATLAB source code curated for "稀疏矩阵" with clean implementations, documentation, and examples.
A MATLAB-based compressed sensing video encoding program implementing the latest DCVS (Distributed Compressed Video Sensing) theoretical algorithm. Utilizes BWHT (Binary Walsh-Hadamard Transform) for sparse matrix representation and GPSR (Gradient Projection for Sparse Reconstruction) algorithm for signal recovery.
Implementation Background: LDPC (Low-Density Parity Check) codes are specialized linear block codes characterized by sparse parity-check matrices where the number of "1"s is significantly fewer than "0"s. Key technical requirements include row/column weight constraints and minimal overlap between rows/columns. This MATLAB implementation involves constructing sparse parity matrices and features multiple decoding approaches including message-passing algorithms with both MATLAB-native and optimized C-based implementations.
MATLAB code for complex network Barabási-Albert model that returns the adjacency matrix of the network graph. The implementation utilizes sparse matrix storage to significantly optimize memory usage and computational efficiency. The algorithm follows preferential attachment principles to generate scale-free networks.
A comparative analysis of sparse and dense matrix implementations for solving linear equations, examining CPU time efficiency and memory usage differences through a practical demonstration program with code-level insights.
This implementation computes eigenvalues and eigenvectors of large sparse matrices, optimized for MATLAB 6.1 and newer environments using efficient iterative algorithms like Arnoldi or Lanczos methods.
Addressing High-Amplitude Impulsive Noise Contamination with Matrix Decomposition and Optimization Algorithms
Implementation of the Lanczos Method for Efficiently Calculating the Largest Few Singular Values of Matrices
Performance comparison between sparse and dense matrix computations for solving linear systems