Math is a process, not just an answer
75 interactive apps where students drive every step and the software handles the computation. Built to work hand-in-hand with the Interactive Linear Algebra textbook.
Used by Major Universities
Why Math Atlas is Different
Traditional software treats math as a black box. We put students inside the process.
You make every decision. The app handles the rest.
Student chooses each operation step by step
App handles arithmetic and shows results instantly
Every choice has an immediate visual consequence
Understanding builds naturally through doing
75+ apps covering all of linear algebra
Enter problem. Get answer. Move on.
Student types in a problem
Software computes the answer instantly
Student copies down the result
No understanding of the steps involved
Math feels like a black box
Topics Covered
Around 75 apps covering all standard introductory linear algebra topics, from matrix basics to SVD.
Matrix Algebra
- Matrix size and notation
- Filling and reading entries
- Matrix addition & subtraction
- Scalar multiplication
- Matrix multiplication
- Transpose
- Matrix inverses
Gaussian Elimination
- Row echelon form
- Leading entries
- Elementary row operations
- Reduced row echelon form
- Gaussian elimination with hints
- LU Factorization
Linear Systems
- Augmented matrices
- Solving linear systems
- Unique / no / infinite solutions
- Under- and over-determined systems
- Free variables
Determinants
- Determinant via Gaussian elimination
- Multilinearity
- Cofactor expansion
- Determinants of singular matrices
- Faster determinant methods
Vectors & Geometry
- Plotting vectors
- Vector addition & subtraction
- Scalar multiplication
- Dot products
- Vector norm
- Angles between vectors
- Orthogonality
- Lines through points
Vector Spaces
- Linear independence
- Linear combinations
- Span and basis
- Coordinates
- Transition matrices
- Rank
- Null space / basis
Linear Transformations
- Matrix of a linear transformation
- Matrix of a composition
- Geometric interpretation
Eigenvalues & More
- Eigenvalues
- Eigenvectors
- Diagonalization
- Singular Value Decomposition (SVD)
Just got the book? Here's how to get started.
Choose your role to see the setup steps for your situation.
About us
Dr. Conrad Plaut - cplaut@utk.edu
Kishan Tailor - kt458@cornell.edu
Abhi Ravi - abhiravi.us@gmail.com
Send us a message