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Session 7
Numerical Computation
Sparse Matrix Computations and Storage Schemes
4 hours
Duration
8
Materials
6
Objectives
Session Overview
Advanced techniques for sparse matrix operations including storage formats, direct and iterative solvers, graph algorithms, and parallel sparse computations.
Learning Objectives
By the end of this session, you should be able to:
- Master various sparse matrix storage formats (CSR, CSC, COO, etc.)
- Implement efficient sparse matrix-vector multiplication algorithms
- Understand sparse direct solvers and fill-in minimization strategies
- Apply graph algorithms to sparse matrix reordering and partitioning
- Design sparse iterative solvers with effective preconditioning
- Implement parallel sparse matrix computations
Course Materials
Download materials for offline study and reference
Sparse Matrix Theory and Applications (65 pages)
Available material
Storage Format Implementations and Comparisons
Available material
Sparse Direct Solver Algorithms
Available material
Graph-based Matrix Reordering Methods
Available material
Parallel Sparse Computation Techniques
Available material
Large-Scale Sparse System Applications
Available material
Performance Analysis and Optimization
Available material
Complete Sparse Matrix Software Library
Available material