Applied Linear Algebra: interactive lab demos

MATH 31246 · CCNY · Boyd & Vandenberghe, Introduction to Applied Linear Algebra (VMLS)

Each lab notebook practices the linear algebra and the Python. The applications live here: move the sliders, watch what the matrices do, and answer the "Try this" questions. Every demo shows the few lines of Python that compute the same thing, so you can try them in the notebook.

Pick a lab, then a tab. Links of the form page.html#tab open a tab directly; the notebooks link to them.

LAB 1 · VMLS §1.1–1.4, §3.1

Vectors

LAB 2 · VMLS §1.5

Python, functions and NumPy

LAB 3 · VMLS §3.1–3.4, §4.1–4.4

Norms, distance and k-means

LAB 4 · VMLS §5.1, §8.3

Linear systems and linear combinations

LAB 5 · VMLS §5.2–5.4

Bases, orthonormal vectors, Gram–Schmidt

LAB 6 · VMLS §6.1–6.5

Matrices, norms and complexity

LAB 7 · VMLS §7.1

Geometric transformations

LAB 8 · VMLS §7.2–7.4

Selectors, incidence matrices, convolution

LAB 9 · VMLS §2.1–2.3, §8.1–8.2

Affine models and subspaces

LAB 10 · VMLS §10.1–10.3

LU factorization

LAB 11 · VMLS §10.4, §11.1–11.5

QR, inverses and solving systems

LAB 12 · VMLS §9.1–9.4

Dynamical systems and determinants

LAB 13 · Supplement; VMLS §3.3

Eigenvectors and PCA

LAB 14 · VMLS §12.1–12.3, §13.1

Least squares and the SVD