Appendix H — Mapping to Strang's Introduction to Linear Algebra

This appendix is for instructors (and self-studiers) who are switching between this book and Gilbert Strang's Introduction to Linear Algebra (Wellesley–Cambridge Press), the closest spiritual sibling to this text and the source of the "four fundamental subspaces" framing we adopt in Part III. The two books share a worldview — linear algebra as something you see and do, with the column picture of $A\mathbf{x}$ front and center — so the mapping is unusually clean.

A caution about precision. Strang's book has gone through several editions (the 5th and 6th are the most common in classrooms), and section numbers move between editions. For that reason this table maps to Strang's chapters and named topics, not to exact section numbers; where even the chapter-level fit is loose, it is flagged [verify]. Use the topic names, not the numerals, to find the corresponding material in whichever edition you hold. Strang's chapter numbering used below follows the widely circulated structure (Ch. 1 Vectors → Ch. 10 Applications); confirm against your copy.

H.1 Chapter-by-chapter correspondence

This book (chapter · topic) Strang chapter · topic Notes
1. What Is Linear Algebra? Ch. 1 (intro to vectors); preface/overview Our transformation-first framing has no direct Strang section; closest is his opening on linear combinations.
2. Vectors 1.1–1.2 Vectors and linear combinations; dot products Direct fit.
3. Systems of Linear Equations Ch. 2 (Solving $A\mathbf{x}=\mathbf{b}$); the geometry of equations Strang's "row picture vs. column picture" is our Ch. 3 geometry.
4. Gaussian Elimination & Row Reduction 2.2–2.4 Elimination, elimination matrices Direct fit.
5. Vector Spaces 3.1 Spaces of vectors Strang folds spaces and subspaces together; we split them.
6. Subspaces, Span, Linear Independence 3.2–3.5 Nullspace, independence, basis, dimension Strang introduces $N(A)$ here; we defer the column/null story to Ch. 13. [verify] exact split
7. Matrices as Functions Ch. 8 (Linear Transformations), esp. 8.1–8.2 Ordering differs: Strang treats transformations late (Ch. 8); we make them the gateway to Part II.
8. Matrix Operations 2.4 Matrix multiplication; transpose in 2.7/4.x Multiplication-as-composition is our emphasis; Strang derives the rule similarly.
9. The Inverse Matrix 2.5 Inverse matrices Direct fit.
10. LU & PLU Decomposition 2.6 Elimination = factorization ($A=LU$); 2.7 permutations Direct fit.
11. The Determinant Ch. 5 (Determinants) — 5.1–5.3 Strang places determinants after the four subspaces; we place them in Part II. [verify] ordering
12. Application: Computer Graphics Ch. 8 transformations; applications chapter (Ch. 10) Homogeneous coordinates are light in Strang; richer here. [verify]
13. Column Space & Null Space 3.1–3.2 Column space and nullspace Core Strang material; identical framing.
14. Row Space, Left Null Space, Rank–Nullity 3.3–3.5 The four subspaces; rank The defining Strang topic — his "big picture" of the four subspaces is our Part III.
15. Dimension, Basis, Coordinates 3.4–3.5 Basis and dimension Direct fit.
16. Change of Basis 8.2–8.3 The matrix of a transformation; basis change Similarity preview matches Strang's transformation-matrix sections.
17. Application: Linear Regression 4.3 Least squares; Ch. 10 applications Least-squares-as-projection is shared; we frame it via $C(A)$.
18. Dot Products, Norms, Angles 1.2 + Ch. 4 intro (orthogonality) Direct fit; Cauchy–Schwarz appears in both.
19. Orthogonal Projection 4.2 Projections; 4.3 least squares Direct fit.
20. Gram–Schmidt & QR 4.4 Orthonormal bases, Gram–Schmidt, $A=QR$ Direct fit.
21. Orthogonal Matrices & Rotations 4.4 + Ch. 8; unitary in Ch. 9 (complex) Direct fit; quantum/unitary preview is ours.
22. Application: Fourier Series Ch. 8/10 applications; Fourier in applications chapter Strang treats Fourier as orthogonal-basis application; same spirit. [verify] location
23. Eigenvalues & Eigenvectors 6.1 Introduction to eigenvalues Direct fit; invariant-direction framing shared.
24. The Characteristic Polynomial 6.1–6.2 $\det(A-\lambda I)=0$; multiplicities Direct fit.
25. Diagonalization 6.2 Diagonalizing a matrix; powers $A^k$ Direct fit ($A=PDP^{-1}$ in Strang's notation $A=S\Lambda S^{-1}$).
26. Complex Eigenvalues Ch. 9 (Complex vectors and matrices); 6.x Strang's complex chapter; rotations-in-disguise framing is ours. [verify]
27. The Spectral Theorem 6.4 Symmetric matrices; spectral theorem Direct fit.
28. Positive Definite & Quadratic Forms 6.5 Positive definite matrices Direct fit; energy/covariance framing shared.
29. Application: PageRank Ch. 10 (Markov matrices, applications); 6.x Strang covers Markov matrices and has discussed PageRank; we make it the climax. [verify]
30. Singular Value Decomposition Ch. 7 (The SVD) — 7.1–7.2 Direct fit; rotate–stretch–rotate geometry shared.
31. SVD Applications 7.2 Bases and matrices in the SVD; low-rank; applications Eckart–Young / image compression; direct fit.
32. Principal Component Analysis 7.3 Principal component analysis (PCA) Strang has a dedicated PCA section; direct fit.
33. Application: Machine Learning Ch. 7 (deep learning section in recent eds.); Ch. 10 Recent Strang editions add a learning-from-data chapter; mapping is edition-dependent. [verify]
34. Inner Product Spaces Ch. 4 + Ch. 9; abstract inner products Strang stays largely concrete; our abstraction goes further. [verify]
35. Linear Transformations & Abstract Vector Spaces Ch. 8 (Linear Transformations) Direct fit, generalized; kernel/image = nullspace/column space.
36. Jordan Normal Form Ch. 8 appendix / 6.x (Jordan form) Strang treats Jordan form briefly; we devote a full chapter. [verify]
37. Matrix Exponential & Systems of ODEs 6.3 Systems of differential equations; $e^{At}$ Direct fit.
38. Numerical Linear Algebra Ch. 9 (numerical); 9.2 condition number, iterative methods Direct fit.
39. Capstone Ch. 10 (Applications) Integrative; no single Strang section.
40. Where Linear Algebra Goes Next Epilogue / tensors in deep-learning sections Forward-looking; no direct Strang section. [verify]

H.2 The two big ordering differences

If you are migrating a Strang-based syllabus to this book (or vice versa), two structural differences matter more than any individual row above:

  1. Transformations come early here, late in Strang. We introduce "a matrix is a function" in Chapter 7 — the gateway to the entire middle of the book — whereas Strang develops the four subspaces first and treats linear transformations formally in his Chapter 8. An instructor who likes Strang's order can read our Chapter 7 as a preview and our Chapter 35 as the formal payoff; an instructor who likes our order can pull Strang's Chapter 8 forward.

  2. Determinants come early here, late in Strang. We place the determinant in Part II (Chapter 11) as the area/volume-scaling factor, tightly coupled to the transformation picture. Strang famously places determinants in his Chapter 5, after the four fundamental subspaces, on the view that they are less central than students assume. (This is the opposite of Axler, who defers determinants to the very end — see Appendix I.) Both choices are defensible; ours is driven by the geometry.

H.3 What the two books share most strongly

The four-fundamental-subspaces framework of our Part III is, more than anything else, the inheritance from Strang: the picture of $C(A)$, $N(A)$, $C(A^{\mathsf{T}})$, and $N(A^{\mathsf{T}})$ as the organizing skeleton of the whole subject — and the locked notation we use for them (Appendix F) — is his. For that material the two books are nearly interchangeable, down to the symbols. Strang's MIT OpenCourseWare lectures (18.06) follow his book closely and pair well with either text; his "big picture of linear algebra" diagram is the visual our Chapters 13–14 build toward.