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Linear Algebra

A Visual and Application-Driven Approach for High School Students

Sanjay Dagam  ·  2026

215 pages 9 Chapters Solutions Manual included Prerequisite: AP Calculus High School / Early College
215
Pages · Main Text
213
Pages · Solutions
9
Chapters
Difficulty levels

Why I wrote this textbook

Linear algebra was the subject that changed how I see mathematics. When I first encountered it, I was struck by how a set of abstract ideas — vectors, matrices, transformations — turned out to be the hidden language behind so much of the modern world. Google's PageRank algorithm, which ranks billions of web pages, is built on eigenvectors. The neural networks behind AI and machine learning represent knowledge as matrices and learn by adjusting them. Image compression, facial recognition, GPS navigation, and the way streaming services decide what to recommend — all of it runs on linear algebra.

Discovering those connections didn't just make the mathematics more interesting. It formalized something I had been feeling for a while: that the most powerful mathematics isn't the kind you do in isolation — it's the kind that reaches into the real world and changes what's possible. That realization is what set me on the path toward applied mathematics, and linear algebra was where it began.

This textbook is written for high school students who are curious, who have finished calculus, and who are ready to discover what mathematics can actually do. If it gives even one reader the same sense of possibility I felt when I first understood what linear algebra was for, it will have been worth writing.

Built for students who want to understand, not just compute.

This textbook takes a visual, application-driven approach to linear algebra—designed specifically for high school students who've completed AP Calculus and are ready to explore one of the most powerful branches of mathematics.

Every chapter connects abstract ideas to real-world applications: from Google's PageRank algorithm to Pixar's animation pipeline, from quantum mechanics to machine learning.

📘
Definition & theorem boxes
Key results highlighted with color-coded callouts for quick reference and review.
✏️
Three difficulty levels
Basic, Intermediate, and Challenge problems in every chapter with full solutions.
🌐
Real-world applications
Each chapter closes with a section connecting theory to engineering, CS, and science.
🔬
Proofs included
Mathematical reasoning presented clearly — not skipped — to build genuine understanding.

Better together — Khan Academy & this textbook

This textbook is designed to stand on its own — but it pairs exceptionally well with Sal Khan's linear algebra video lectures. The two resources complement each other in a specific way: Khan Academy excels at building intuition through clear, visual explanations. This textbook adds formal structure, rigorous proofs, and graded problem sets that turn that intuition into lasting mathematical understanding.

A natural approach: watch the Khan Academy video for a concept first, let the visual explanation build your intuition, then open the corresponding chapter here to see the formal definitions, work through the theorems, and test yourself with the problem sets. Together they cover the subject from two complementary angles — one emphasizing understanding, the other emphasizing rigor.

Khan Academy's linear algebra series is completely free and requires no account.

🎓

Khan Academy
Linear Algebra

Free video lectures covering vectors, matrices, transformations, and eigenvalues — the ideal visual companion to this textbook.

▶ Watch Free Lectures

Contents

9 chapters
1
Chapter 01
Introduction to Vectors
Notation, operations, dot & cross products, applications
2
Chapter 02
Systems of Linear Equations
Gaussian elimination, row echelon form, applications
3
Chapter 03
Matrices
Operations, transpose, inverse, Markov chains, cryptography
4
Chapter 04
Determinants
Cofactor expansion, Cramer's rule, volume, eigenvalue preview
5
Chapter 05
Vector Spaces
Subspaces, basis, dimension, linear independence
6
Chapter 06
Eigenvalues & Eigenvectors
Characteristic polynomial, diagonalization, PageRank
7
Chapter 07
Linear Transformations
Kernel, range, matrix representations, applications
8
Chapter 08
Inner Product Spaces
Inner products, norm, Cauchy-Schwarz inequality, orthogonality
9
Chapter 09
Applications & Advanced Topics
Markov chains, PageRank, linear programming
Textbook
Solutions Manual

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