# The Computational Solutions from Introduction to Linear Algebra: Third Edition

## by Gilbert Strang.

Matlab Code For Various Problems:
Chapter 1 (Introduction to Vectors):
Chapter 2 (Solving Linear Systems):
Chapter 3 (Vector Spaces and Subspaces):
• There were no computational problems for this chapter
Chapter 4 (Orthogonality):
Chapter 5 (Determinants):
• There were no computational problems for this chapter
Chapter 6 (Eigenvalues and Eigenvectors):
Chapter 7 (Linear Transformations):
Chapter 8 (Applications):
• There were no computational problems for this chapter
Chapter 9 (Numerical Linear Algebra):
• prob_9_1_2.m (ill-conditioning of the Hilbert matrices)
• prob_9_1_3.m (ill-conditioning of the Hilbert matrices continued)
• prob_9_1_4.m (ill-conditioning of the Hilbert matrices continued again)
• prob_9_1_10.m (the possible accuracy errors possible by performing LU without pivoting)
• slu.m (Requires Strang's textbook LU factorization (without row exchanges))
• slv.m (Requires Strang's textbook LU backsolve (without row exchanges))
• prob_9_2_9.m (eigenvalue calculations for this problem)
• prob_9_2_11.m (eigenvalue calculations for this problem)
• prob_9_3_10.m (the Gauss-Seidel method on the -1, 2, -1 matrix)
• prob_9_3_12.m (the SOR method on the -1, 2, -1 matrix)
• prob_9_3_14.m (the inverse power method with a Markov matrix)
• prob_9_3_17.m (the inverse power method again)
• prob_9_3_22.m (a demonstration of the Lanczos algorithm on the -1, 2, -1 matrix)
• The problems above may require the core numeric algorithms below
• gen_der2_mat.m (a simple function to construct the -1, 2, -1 matrix easily)
• jacobi.m (An implementation of the Jacobi iterative method to solve Ax=b)
• gseidel.m (An implementation of the Gauss-Seidel iterative method to solve Ax=b)
• sor.m (An implementation of the SOR iterative method to solve Ax=b)
• lanczos.m (An implementation of the Lanczos algorithm)
Chapter 10 (Complex Vectors and Matrices):
• There were no computational problems for this chapter

John Weatherwax