Linear Algebra
Courses
Vectors, matrices, eigenproblems — the language of nearly all numerics.
Content for this course is still being written. For now, explore the skill map below — every node links to its full page.
Skill map
Each node is a skill; an arrow means "learn this first." Deep-dive links go to the full pages.
Matrix Storage…
Row- vs column-major: the layout under the math.
content coming soon
deep dive ↓Row-Major and Column-Major Storage
Gaussian Elimination…
Solve Ax=b by row reduction with pivoting.
content coming soon
deep dive ↓Gaussian Elimination
Power Iteration…
Find the dominant eigenpair by repeated multiplication.
content coming soon
deep dive ↓Power Iteration
Condition Number…
How sensitive is the solution? Stability and precision.
content coming soon
deep dive ↓Condition Number and Numerical Stability
QR Factorization…
Stable factorization for least-squares and eigenvalues.
content coming soon
deep dive ↓Householder QR Factorization
Conjugate Gradient…
Iterative solver for symmetric positive-definite systems.
content coming soon
deep dive ↓Conjugate Gradient
Krylov / Arnoldi…
Orthonormal bases of Krylov subspaces for large problems.
content coming soon
deep dive ↓Arnoldi Iteration
Lanczos Iteration…
Symmetric Arnoldi: tridiagonal compression of an operator.
content coming soon
deep dive ↓Lanczos Iteration