Matrix Calculator
Add, subtract, multiply, transpose, determinant, inverse, rank, trace and power. Up to 50×50.
Tips: adjust sizes (max 50×50). For A×B, cols(A)=rows(B). det/inverse/trace/power require square matrices.
Add, subtract, multiply, transpose, determinant, inverse, rank, trace and power. Up to 50×50.
Tips: adjust sizes (max 50×50). For A×B, cols(A)=rows(B). det/inverse/trace/power require square matrices.
Everything below runs in the calculator above — nothing here is a separate tool, and the tabs group the operations by what they do rather than by how hard they are.
The Basics tab adds and subtracts matrices of matching size, multiplies A by B in either order, and transposes either one. Scalars multiplies every entry by a number of your choosing, and Power raises a square matrix to an integer exponent by repeated squaring. The matrix multiplication calculator walks through all of these, including why AB and BA are usually different matrices and why (A·B)ᵀ reverses the order.
Row reduction is the engine underneath most of the rest. The RREF calculator takes any matrix — square or rectangular — to reduced row echelon form, stops at plain row echelon form if you prefer, solves a linear system entered as an augmented matrix, and counts the pivots to give you the rank. The step-by-step panel names every operation as it happens, in the R2 → R2 − 3·R1 notation your course uses.
The Advanced tab holds the numbers that only square matrices have: the determinant, the trace, the inverse, and the eigenvalues and eigenvectors. The trace and the determinant are worth computing together — one is the sum of the eigenvalues and the other their product, which pins down both eigenvalues of a 2×2 matrix without solving anything.
LU decomposition splits a square matrix into triangular factors you can reuse against many right-hand sides, and Cholesky does the same in half the work when the matrix is symmetric and positive definite. The characteristic polynomial lives in the same tab, computed exactly rather than by expanding a symbolic determinant.
Take A = [[4, 7], [2, 6]] and ask for the inverse. Gauss-Jordan writes the identity next to A and reduces the left half until it becomes the identity:
| 4 | 7 | 1 | 0 |
| 2 | 6 | 0 | 1 |
| 1 | 0 | 3/5 | −7/10 |
| 0 | 1 | −1/5 | 2/5 |
The right half is A⁻¹. The step-by-step panel shows each row operation that got there — R1 → (1/4)·R1, R2 → R2 − 2·R1, and so on — in exact fractions, because the engine works in rational arithmetic rather than floating point. Multiply A × B after pasting the result into B and you should get the identity back, which is how you check any inverse.
These three describe the same underlying property from different angles, and it is easy to reach for the wrong one.
Ax = b, row-reducing [A | b] is faster and more accurate.For a square matrix the three line up exactly: non-zero determinant ⟺ full rank ⟺ an inverse exists.
Each of the six pages below carries the full explanation of its operation — what the result means, how to reproduce it by hand, which method to choose, and the mistakes that cost marks: determinants, the inverse matrix, Gaussian elimination and rank, multiplication, transpose and powers, eigenvalues and eigenvectors and LU and Cholesky decomposition.