Part 2: Scientific Computing with NumPy and SciPy

Chapter 6: Linear Algebra in NumPy and SciPy

Foundational~150 min

Learning Objectives

  • Compute eigenvalue and singular value decompositions efficiently with LAPACK backends
  • Construct and operate on sparse matrices using scipy.sparse for large-scale problems
  • Implement efficient Kronecker product operations without forming the full matrix
  • Compute matrix functions (exponential, logarithm) and least-squares solutions

Sections

Prerequisites & Notation
nextSpecial
Basic Operations
Eigenvalue and Singular Value Decomposition
Sparse Matrices with scipy.sparse
Kronecker Products — Efficient Implementation
Matrix Functions and Special Matrices
Least Squares and Pseudoinverse
Chapter Summary
Special
Exercises
References & Further Reading
Special

Prerequisites

ch05

💬 Discussion

Loading discussions...