Why can't I invert my matrix?

A matrix can only be inverted if it is square (same number of rows and columns) and has full rank, meaning all rows and columns are linearly independent. Mathematically, this is equivalent to det(A) ≠ 0. If your matrix is singular (det(A) = 0 or rank(A) < n), it does not have an inverse because the transformation it represents is not one-to-one—multiple inputs map to the same output, so you cannot uniquely reverse the transformation. Common causes: (1) One row/column is a multiple of another. (2) One row/column is a linear combination of others. (3) A row/column is all zeros. To diagnose, compute the rank and determinant. If rank < n or det = 0, the matrix is non-invertible. For solving Ax = b with singular A, use RREF, pseudoinverse (via SVD), or regularization (ridge regression) to find least-squares or minimum-norm solutions.

What does 'singular' mean?

A square matrix is singular if it is not invertible, which occurs when det(A) = 0 or rank(A) < n (where n is the number of rows/columns). Geometrically, a singular matrix collapses space in at least one dimension—it maps the entire n-dimensional space onto a lower-dimensional subspace. For example, a singular 2×2 matrix maps the plane onto a line (or point), and a singular 3×3 matrix maps 3D space onto a plane, line, or point. Algebraically, singularity means the columns (or rows) are linearly dependent—at least one column can be expressed as a linear combination of others. Practical implications: (1) The system Ax = b may have no solution or infinitely many solutions, depending on b. (2) You cannot compute A⁻¹ directly. (3) Numerical algorithms may fail or produce unreliable results. Use RREF to analyze the solution structure or SVD to compute the pseudoinverse A⁺ for least-squares solutions.

What is RREF and how is it used to solve systems?

RREF (Reduced Row Echelon Form) is the simplest form of a matrix achieved through Gaussian elimination with back-substitution. A matrix is in RREF when: (1) All zero rows are at the bottom. (2) The first non-zero entry in each row (called a pivot) is 1. (3) Each pivot is the only non-zero entry in its column. (4) Pivots move strictly to the right as you go down rows. To solve Ax = b, form the augmented matrix [A | b] and reduce it to RREF. The result reveals the solution structure: (1) Unique solution: Each variable corresponds to a pivot column, and you can read x directly from the RREF. (2) Infinite solutions: Free variables (non-pivot columns) exist; express pivot variables in terms of free variables to get the general solution x = x_particular + linear combination of null space basis vectors. (3) No solution: A row of the form [0 0 ... 0 | c] with c ≠ 0 appears, indicating inconsistency (b is not in the column space of A). RREF also reveals rank(A) = number of pivots and the null space (set free variables to parameters and solve for pivot variables).

Which decomposition should I choose (LU vs QR vs SVD)?

The choice depends on your goals and matrix properties: (1) LU Decomposition: Use for solving Ax = b when A is square and well-conditioned, especially for multiple right-hand sides (solve once, reuse L and U). LU is fast and efficient but sensitive to ill-conditioning. Use partial pivoting (PA = LU) to improve stability. Ideal for dense systems without special structure. (2) QR Decomposition: Use for least-squares problems (minimize ||Ax - b||₂) when A is rectangular (more rows than columns) or for ill-conditioned square systems. QR is more numerically stable than LU and avoids squaring the condition number (unlike normal equations A⊤Ax = A⊤b). Orthogonal Q (Q⊤Q = I) preserves lengths and angles. Preferred in practice for overdetermined systems (m > n). (3) SVD (Singular Value Decomposition): Use for maximum generality and robustness—works for any matrix (square, rectangular, singular, or non-singular). SVD reveals rank, null space, range, condition number, and optimal low-rank approximations. Compute pseudoinverse A⁺ = VΣ⁺U⊤ for solving underdetermined or singular systems. SVD is the most expensive computationally but provides the most information and handles ill-conditioning best via truncation (set small singular values to zero). Use SVD for PCA, data compression, and when you need deep insight into matrix structure.

What is the condition number and why does it matter?

The condition number κ(A) = ||A|| · ||A⁻¹|| (typically using the spectral norm ||A||₂ = largest singular value) measures how sensitive the solution x to Ax = b is with respect to perturbations in A or b. Intuitively, κ(A) tells you how much errors in input (rounding, measurement noise) get amplified in the output. κ(A) ≥ 1 always; κ(A) = 1 for orthogonal matrices (ideal—no error amplification). κ(A) 10¹⁰ is severely ill-conditioned (solutions may be meaningless due to finite precision arithmetic). For example, if κ(A) = 10⁶ and you perturb b by 1%, the solution x can change by up to 10⁶% (10,000 times the input error). Ill-conditioning arises from nearly dependent columns, vastly different scales (e.g., one column has values ~10⁻⁶, another ~10⁶), or near-singularity. Remedies: (1) Use QR or SVD instead of LU. (2) Regularize: add λI to A (ridge regression). (3) Rescale/normalize columns. (4) Use higher precision arithmetic. (5) Truncate small singular values in SVD.

Why are some eigenvalues complex?

Eigenvalues can be complex even for real matrices because the characteristic polynomial det(A - λI) = 0 may have complex roots. Geometrically, complex eigenvalues (occurring in conjugate pairs λ = a ± bi for real A) indicate that the linear transformation A has a rotational component—there is no real direction that A simply stretches or shrinks. For example, a 2D rotation matrix [[cos θ, -sin θ], [sin θ, cos θ]] has eigenvalues e^(iθ) and e^(-iθ) (complex conjugates) unless θ is a multiple of π (which gives eigenvalues ±1). In higher dimensions, complex eigenvalues signal oscillatory or spiraling behavior in dynamical systems (e.g., damped oscillators, population dynamics with cycles). The real part a controls growth/decay (a > 0 ⇒ growth, a < 0 ⇒ decay), and the imaginary part b controls oscillation frequency (larger |b| ⇒ faster oscillation). Complex eigenvectors also occur (as conjugate pairs) and can be combined to form real invariant subspaces. For symmetric matrices A = A⊤, all eigenvalues are guaranteed to be real because symmetry eliminates rotational components.

What are matrix norms and which one does the tool display?

A matrix norm ||A|| is a measure of the "size" or "magnitude" of a matrix, satisfying properties like ||A|| ≥ 0, ||cA|| = |c|·||A||, ||A + B|| ≤ ||A|| + ||B||, and ||AB|| ≤ ||A||·||B||. Common norms include: (1) Frobenius norm ||A||_F = √(Σa²ᵢⱼ) (square root of sum of squared elements)—easy to compute, analogous to Euclidean vector norm. (2) Spectral norm (2-norm) ||A||₂ = largest singular value σ_max—measures maximum stretching factor for unit vectors. Used for condition number κ(A) = ||A||₂·||A⁻¹||₂ = σ_max/σ_min. (3) Infinity norm ||A||_∞ = max row sum (maximum sum of absolute values in any row)—simple to compute. (4) 1-norm ||A||₁ = max column sum. The tool typically displays the Frobenius norm (easy to compute from all entries) and/or the spectral norm (from SVD—σ_max). The spectral norm is most commonly used in numerical analysis because it directly relates to eigenvalues, condition number, and error propagation. For error analysis, ||A - B||_F or ||A - B||₂ quantifies how different two matrices are.

Matrix Operations Calculator

Perform matrix operations including add, subtract, multiply, transpose, determinant, inverse, eigenvalues, SVD, and more

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Matrix Operations Calculator

Select an operation mode, enter your matrices, and click Calculate to see results

Understanding Matrix Operations

Matrices are two-dimensional arrays of numbers used to represent linear transformations, systems of linear equations, networks, images, data sets, and more. This calculator supports all core linear algebra operations from basic arithmetic to advanced decompositions, making it a comprehensive tool for students, engineers, data scientists, and researchers.

Basic Operations

Addition / Subtraction: Element-wise operations where C = A ± B. Both matrices must have identical dimensions (same number of rows and columns). Each element c_ij = a_ij ± b_ij. Used in linear combinations, solving systems, and image processing.
Multiplication: Matrix product C = AB where A ∈ ℝ^m×n and B ∈ ℝ^n×p produces C ∈ ℝ^m×p. The inner dimensions (n) must match. Each element c_ij is the dot product of row i from A and column j from B. Unlike scalar multiplication, matrix multiplication is not commutative (AB ≠ BA in general).
Transpose: A^⊤ flips rows and columns, so if A is m×n, then A^⊤ is n×m. Element-wise: (A^⊤)_ij = A_ji. Properties: (A^⊤)^⊤ = A, (AB)^⊤ = B^⊤A^⊤. Used in least squares, normal equations (A^⊤Ax = A^⊤b), and defining symmetric matrices (A = A^⊤).
Scalar Multiplication: Multiply every element by a constant k: (kA)_ij = k·a_ij. Preserves matrix structure and is used in normalization and scaling transformations.

Determinant, Inverse, and Properties

Determinant: det(A) is a scalar that measures how much A scales area (2D) or volume (3D+). For a 2×2 matrix [[a,b],[c,d]], det(A) = ad - bc. For larger matrices, use cofactor expansion or LU decomposition. Key property: det(A) = 0 ⇔ A is singular (non-invertible). Also, det(AB) = det(A)·det(B) and det(A^⊤) = det(A).
Inverse: A^-1 is the unique matrix such that AA^-1 = A^-1A = I (identity matrix). Only square matrices with det(A) ≠ 0 (full rank) have inverses. Used to solve Ax = b as x = A^-1b. However, computing the explicit inverse is numerically unstable for large or ill-conditioned matrices—prefer LU or QR decomposition for solving systems.
Rank: The number of linearly independent rows or columns. For an m×n matrix, rank(A) ≤ min(m, n). Full rank means rank(A) = min(m, n). Rank reveals the dimension of the column space (range) and determines invertibility (square A is invertible ⇔ rank(A) = n).
Trace: The sum of diagonal elements: tr(A) = Σa_ii. For square matrices, tr(A) equals the sum of eigenvalues. Properties: tr(A + B) = tr(A) + tr(B), tr(AB) = tr(BA), and tr(A^⊤) = tr(A).

Row Reduction and Systems of Equations

RREF (Reduced Row Echelon Form): Gaussian elimination with back-substitution produces a matrix where each pivot (leading entry) is 1, and all entries above and below each pivot are 0. RREF reveals the rank, pivot columns (basis for column space), and free variables. Used to solve Ax = b: augment [A | b], reduce to RREF, and read off solutions. Infinite solutions occur when free variables exist; no solution when a row like [0 0 ... 0 | c] (c ≠ 0) appears.
Gaussian Elimination: A systematic method using elementary row operations (swap, scale, row addition) to reduce A to upper triangular or RREF form. Pivot selection (partial pivoting) improves numerical stability by choosing the largest available pivot to avoid division by very small numbers.
Null Space: The set of all x such that Ax = 0. Dimension of null space = n - rank(A) (nullity). Found by solving the homogeneous system using RREF and expressing free variables.

Eigenvalues, Eigenvectors, and Spectral Decomposition

Eigenvalues and Eigenvectors: For square A, find scalars λ (eigenvalues) and non-zero vectors v (eigenvectors) such that Av = λv. Geometrically, v is a direction that A stretches (or shrinks) by factor λ. The characteristic polynomial det(A - λI) = 0 gives eigenvalues. For each λ, solve (A - λI)v = 0 to find eigenvectors. Eigenvalues may be complex, even for real matrices.
Diagonalization: If A has n linearly independent eigenvectors, then A = PDP^-1 where D is diagonal (eigenvalues on diagonal) and P has eigenvectors as columns. Diagonal matrices simplify exponentiation (A^k = PD^kP^-1) and solving differential equations. Not all matrices are diagonalizable (e.g., defective matrices).
Symmetric Matrices: If A = A^⊤, then all eigenvalues are real and eigenvectors corresponding to distinct eigenvalues are orthogonal. Symmetric matrices always diagonalize as A = QΛQ^⊤ where Q is orthogonal (Q^⊤Q = I).

Matrix Decompositions

LU Decomposition: Factorize A = LU where L is lower triangular (1s on diagonal) and U is upper triangular. Used for efficient solving of Ax = b (solve Ly = b via forward substitution, then Ux = y via back substitution). LU is faster than computing A^-1 and numerically stable with partial pivoting (PA = LU).
QR Decomposition: A = QR where Q is orthogonal (Q^⊤Q = I) and R is upper triangular. QR is more stable than LU for least squares (minimize ||Ax - b||₂) and computing eigenvalues (QR algorithm). Gram-Schmidt or Householder reflections produce QR.
SVD (Singular Value Decomposition): Any m×n matrix A = UΣV^⊤ where U (m×m) and V (n×n) are orthogonal, and Σ (m×n) is diagonal with non-negative singular values σ_i ≥ 0. SVD reveals the rank (number of non-zero σ_i), null space, range, and optimal low-rank approximations. Applications: PCA (principal component analysis), image compression, pseudoinverse (A⁺ = VΣ⁺U^⊤), and solving least squares even when A is singular or rectangular.
Cholesky Decomposition: For positive-definite symmetric A, decompose A = LL^⊤ where L is lower triangular. Cholesky is twice as fast as LU for this special case and used in optimization, Kalman filtering, and Monte Carlo simulations.

Norms and Condition Number

Matrix Norms: Measure the "size" of a matrix. Common norms include Frobenius norm ||A||_F = √(Σa_ij²), spectral norm ||A||₂ = largest singular value, and infinity norm ||A||_∞ = max row sum. Norms are used to assess error and convergence.
Condition Number: κ(A) = ||A|| · ||A^-1|| (often using spectral norm). κ(A) measures sensitivity to perturbations—how much the solution x changes when b or A changes slightly. κ(A) ≥ 1; κ(A) = 1 for orthogonal matrices (ideal). κ(A) > 10¹⁰ indicates severe ill-conditioning, where rounding errors are amplified and solutions are unreliable. Use SVD or regularization (ridge regression) for ill-conditioned systems.

How to Use the Matrix Calculator

This calculator provides a comprehensive suite of matrix operations, from basic arithmetic to advanced decompositions. Follow these steps to perform calculations efficiently:

Choose Your Operation: Select the desired operation from the dropdown or operation tabs: Add, Subtract, Multiply, Transpose, Determinant, Inverse, Rank, Trace, RREF, Eigenvalues/Eigenvectors, LU Decomposition, QR Decomposition, or SVD. The interface adapts based on your selection—single-matrix operations (transpose, determinant, inverse, RREF, eigen, decompositions) show one input grid, while binary operations (add, subtract, multiply) show two input grids (Matrix A and Matrix B).
Set Matrix Dimensions: Specify the number of rows and columns for each matrix. For operations like addition and subtraction, ensure both matrices have identical dimensions. For multiplication A×B, the number of columns in A must equal the number of rows in B. For square-only operations (determinant, inverse, eigenvalues, Cholesky), rows must equal columns. The calculator validates dimensions and displays errors if constraints are violated.
Enter Matrix Values: Input numbers into the grid cells. You can type directly, use Tab/Shift+Tab or arrow keys to navigate between cells, or paste data from a spreadsheet (comma-separated or tab-separated values). For large matrices, consider pasting CSV data for speed. The calculator accepts integers, decimals, and scientific notation (e.g., 1.5e-3). Leave cells blank or enter 0 for zero entries.
Adjust Precision and Options: Use the precision slider or input to control decimal places in results (typically 2-10 decimal places). For operations like RREF and determinant, enable "Show Steps" if available to see intermediate row operations or cofactor expansions. Some decompositions (LU, QR, SVD) may have options for pivoting strategy or tolerance for numerical stability.
Calculate: Click the "Calculate" button to perform the operation. The calculator processes your input and displays results in organized panels. For scalar results (determinant, rank, trace, condition number), values appear prominently at the top. For matrix results (sum, product, inverse, transpose, RREF, eigenvectors), the output matrix is displayed in a grid format. Decompositions show multiple matrices (e.g., LU shows L and U; SVD shows U, Σ, V).
Interpret Results: Review the output carefully. For RREF, identify pivot columns (leading 1s) to determine rank and free variables. For eigenvalues, note that complex values may appear (displayed as a + bi). For decompositions, verify properties like Q^⊤Q = I for QR or reconstruct the original matrix (A = LU, A = QR, A = UΣV^⊤) to check correctness. If the result shows "singular" or "non-invertible," the matrix does not have an inverse (det(A) = 0 or rank deficient). Use error messages and warnings to diagnose issues like dimension mismatches, numerical instability, or overflow.

Tip: For large or complex systems, prefer decomposition-based solvers (LU, QR, SVD) over explicit inverse computation. Decompositions are faster, more numerically stable, and provide additional insights (rank, null space, condition number).

Tips & Common Workflows

Solving Linear Systems Ax = b: To solve for x, you have several options: (1) Compute A^-1 and calculate x = A^-1b (simple but numerically unstable for large or ill-conditioned A). (2) Use RREF on the augmented matrix [A | b] to read off the solution directly (best for understanding solution structure and free variables). (3) Use LU decomposition: factorize A = LU, solve Ly = b via forward substitution, then solve Ux = y via back substitution (efficient for multiple right-hand sides). (4) Use QR decomposition: A = QR, then x = R^-1Q^⊤b (more stable than LU for ill-conditioned systems). (5) Use SVD for least squares when A is rectangular or singular: x = VΣ⁺U^⊤b minimizes ||Ax - b||₂.
Check Invertibility: Before attempting to invert a matrix, verify that it is square and has full rank. Compute rank(A) and det(A). If rank(A) < n or det(A) = 0, the matrix is singular and has no inverse. Also check the condition number κ(A)—if κ(A) > 10¹⁰, the inverse may be numerically unreliable even if det(A) ≠ 0. In such cases, use regularization (add λI to A) or switch to pseudoinverse via SVD.
Matrix Multiplication Dimensions: For C = AB, ensure the number of columns in A equals the number of rows in B. If A is m×n and B is n×p, then C is m×p. Remember that matrix multiplication is associative ((AB)C = A(BC)) but not commutative (AB ≠ BA in general). Use associativity to optimize computation order—e.g., compute A(BC) instead of (AB)C if it reduces the number of scalar multiplications.
Numerical Stability: Avoid computing explicit inverses whenever possible. For solving Ax = b, prefer LU, QR, or SVD decompositions. For least squares, use QR or SVD instead of the normal equations (A^⊤Ax = A^⊤b) which can square the condition number. Enable partial pivoting in LU to improve stability. For symmetric positive-definite systems, use Cholesky decomposition which is both fast and stable.
Symmetric and Special Matrices: If A = A^⊤, all eigenvalues are real and eigenvectors are orthogonal. Symmetric positive-definite matrices (all eigenvalues > 0) have Cholesky decomposition A = LL^⊤ which is twice as fast as LU. Diagonal matrices have trivial eigenvalues (the diagonal entries) and simple multiplication/inversion. Orthogonal matrices (Q^⊤Q = I) preserve lengths and angles, have κ(Q) = 1, and Q^-1 = Q^⊤.
Data Science and PCA: SVD is the foundation of Principal Component Analysis (PCA). For a data matrix X (centered, n×p), compute X = UΣV^⊤. The columns of V are the principal components (directions of maximum variance), and the singular values σ_i (or σ_i²) represent the variance explained by each component. Truncate to the top k components for dimensionality reduction: X_k = U_kΣ_kV_k^⊤ is the best rank-k approximation in Frobenius norm.
Eigenvalue Applications: Eigenvalues reveal stability of dynamical systems (all |λ_i| < 1 ⇒ stable discrete system), vibrational modes (mechanical/structural engineering), and Google PageRank (dominant eigenvector of link matrix). For symmetric matrices, diagonalization simplifies matrix powers: A^k = QΛ^kQ^⊤ where Λ^k is trivial (raise diagonal entries to power k).
Error Checking: After computing a result, verify correctness when possible. For inverse, check that AA^-1 ≈ I. For decompositions, reconstruct A from factors (A = LU, A = QR, A = UΣV^⊤) and compare to the original. For eigenvalues, verify Av = λv for each eigenpair. Small discrepancies (on the order of machine epsilon ≈ 10^-15 for double precision) are normal due to floating-point arithmetic.

Understanding Your Results

The calculator displays results in multiple formats depending on the operation. Here's how to interpret each type of output:

Output	Meaning & Interpretation
Result Matrix	The output of the selected operation (e.g., A+B, AB, A^-1, A^⊤, RREF). Dimensions match operation rules. For RREF, identify pivot positions (leading 1s) to determine rank and free variables. For inverse, verify AA^-1 = I. For transpose, verify dimensions flipped and elements (A^⊤)_ij = A_ji.
Determinant	Scalar value measuring area/volume scaling. det(A) = 0 ⇔ matrix is singular (non-invertible, rank deficient). \|det(A)\| > 1 indicates expansion, \|det(A)\| < 1 indicates contraction. Sign indicates orientation (positive = preserves orientation, negative = reverses).
Rank	Number of linearly independent rows/columns. For m×n matrix A, rank(A) ≤ min(m,n). Full rank = min(m,n) indicates maximal independence. Rank < n ⇒ matrix is singular (if square). Nullity (dimension of null space) = n - rank(A). Rank reveals degrees of freedom in solutions to Ax = b.
Trace	Sum of diagonal elements: tr(A) = Σa_ii. For square matrices, tr(A) equals the sum of eigenvalues (even if complex). Invariant under similarity transformations: tr(A) = tr(PAP^-1). Used in characteristic polynomial and measuring matrix "size" in some contexts.
Eigenvalues / Eigenvectors	Eigenvalues λ are scalars such that Av = λv for non-zero eigenvector v. Eigenvalues reveal scaling factors along eigenvector directions. Complex eigenvalues (a ± bi) occur for real matrices with rotational components. Sum of eigenvalues = tr(A); product = det(A). Eigenvectors form a basis (if linearly independent) for diagonalization.
RREF with Pivots	Reduced row echelon form shows pivot columns (leading 1s) and free variables (non-pivot columns). Rank = number of pivots. For augmented [A \| b], consistency requires no row [0...0 \| c] with c ≠ 0. Infinite solutions exist when free variables are present. Read solution: express pivot variables in terms of free variables.
LU Decomposition	A = LU (or PA = LU with pivoting) where L is lower triangular (1s on diagonal) and U is upper triangular. Use for efficient solving: Ly = b (forward sub), then Ux = y (back sub). Faster than computing A^-1. Pivoting (row swaps in P) improves numerical stability.
QR Decomposition	A = QR where Q is orthogonal (Q^⊤Q = I) and R is upper triangular. More stable than LU for least squares (minimize \|\|Ax - b\|\|₂). Solution: x = R^-1Q^⊤b. Q preserves lengths/angles; condition number κ(Q) = 1. Used in QR algorithm for eigenvalues.
SVD (U, Σ, V)	A = UΣV^⊤ where U (m×m) and V (n×n) are orthogonal, Σ (m×n) diagonal with singular values σ_i ≥ 0. Rank = number of non-zero σ_i. Columns of U are left singular vectors (orthonormal basis for range), columns of V are right singular vectors (orthonormal basis for row space). Condition number κ(A) = σ_max/σ_min. Truncate to top k singular values for optimal low-rank approximation. Pseudoinverse: A⁺ = VΣ⁺U^⊤.
Condition Number	κ(A) = \|\|A\|\| · \|\|A^-1\|\| (typically spectral norm, \|\|A\|\|₂ = largest singular value). Measures sensitivity to perturbations. κ(A) = 1 for orthogonal matrices (ideal). κ(A) < 100 is well-conditioned. κ(A) > 10¹⁰ is severely ill-conditioned—solutions unreliable due to rounding errors. Use SVD or regularization for ill-conditioned problems.

Error Indicators: If results display NaN (Not a Number), Infinity, or error messages like "matrix is singular" or "dimension mismatch," recheck your input matrix dimensions, ensure constraints are met (e.g., square for determinant/inverse, matching inner dimensions for multiplication), and verify that the matrix has the required properties (e.g., full rank for inverse, positive-definite for Cholesky). For ill-conditioned matrices (high condition number), consider using higher precision arithmetic or regularization techniques.

Understanding Matrix Operations

Basic Operations

Addition / Subtraction: Element-wise operations where C = A ± B. Both matrices must have identical dimensions (same number of rows and columns). Each element c_ij = a_ij ± b_ij. Used in linear combinations, solving systems, and image processing.
Multiplication: Matrix product C = AB where A ∈ ℝ^m×n and B ∈ ℝ^n×p produces C ∈ ℝ^m×p. The inner dimensions (n) must match. Each element c_ij is the dot product of row i from A and column j from B. Unlike scalar multiplication, matrix multiplication is not commutative (AB ≠ BA in general).
Transpose: A^⊤ flips rows and columns, so if A is m×n, then A^⊤ is n×m. Element-wise: (A^⊤)_ij = A_ji. Properties: (A^⊤)^⊤ = A, (AB)^⊤ = B^⊤A^⊤. Used in least squares, normal equations (A^⊤Ax = A^⊤b), and defining symmetric matrices (A = A^⊤).
Scalar Multiplication: Multiply every element by a constant k: (kA)_ij = k·a_ij. Preserves matrix structure and is used in normalization and scaling transformations.

Determinant, Inverse, and Properties

Determinant: det(A) is a scalar that measures how much A scales area (2D) or volume (3D+). For a 2×2 matrix [[a,b],[c,d]], det(A) = ad - bc. For larger matrices, use cofactor expansion or LU decomposition. Key property: det(A) = 0 ⇔ A is singular (non-invertible). Also, det(AB) = det(A)·det(B) and det(A^⊤) = det(A).
Inverse: A^-1 is the unique matrix such that AA^-1 = A^-1A = I (identity matrix). Only square matrices with det(A) ≠ 0 (full rank) have inverses. Used to solve Ax = b as x = A^-1b. However, computing the explicit inverse is numerically unstable for large or ill-conditioned matrices—prefer LU or QR decomposition for solving systems.
Rank: The number of linearly independent rows or columns. For an m×n matrix, rank(A) ≤ min(m, n). Full rank means rank(A) = min(m, n). Rank reveals the dimension of the column space (range) and determines invertibility (square A is invertible ⇔ rank(A) = n).
Trace: The sum of diagonal elements: tr(A) = Σa_ii. For square matrices, tr(A) equals the sum of eigenvalues. Properties: tr(A + B) = tr(A) + tr(B), tr(AB) = tr(BA), and tr(A^⊤) = tr(A).

Row Reduction and Systems of Equations

RREF (Reduced Row Echelon Form): Gaussian elimination with back-substitution produces a matrix where each pivot (leading entry) is 1, and all entries above and below each pivot are 0. RREF reveals the rank, pivot columns (basis for column space), and free variables. Used to solve Ax = b: augment [A | b], reduce to RREF, and read off solutions. Infinite solutions occur when free variables exist; no solution when a row like [0 0 ... 0 | c] (c ≠ 0) appears.
Gaussian Elimination: A systematic method using elementary row operations (swap, scale, row addition) to reduce A to upper triangular or RREF form. Pivot selection (partial pivoting) improves numerical stability by choosing the largest available pivot to avoid division by very small numbers.
Null Space: The set of all x such that Ax = 0. Dimension of null space = n - rank(A) (nullity). Found by solving the homogeneous system using RREF and expressing free variables.

Eigenvalues, Eigenvectors, and Spectral Decomposition

Eigenvalues and Eigenvectors: For square A, find scalars λ (eigenvalues) and non-zero vectors v (eigenvectors) such that Av = λv. Geometrically, v is a direction that A stretches (or shrinks) by factor λ. The characteristic polynomial det(A - λI) = 0 gives eigenvalues. For each λ, solve (A - λI)v = 0 to find eigenvectors. Eigenvalues may be complex, even for real matrices.
Diagonalization: If A has n linearly independent eigenvectors, then A = PDP^-1 where D is diagonal (eigenvalues on diagonal) and P has eigenvectors as columns. Diagonal matrices simplify exponentiation (A^k = PD^kP^-1) and solving differential equations. Not all matrices are diagonalizable (e.g., defective matrices).
Symmetric Matrices: If A = A^⊤, then all eigenvalues are real and eigenvectors corresponding to distinct eigenvalues are orthogonal. Symmetric matrices always diagonalize as A = QΛQ^⊤ where Q is orthogonal (Q^⊤Q = I).

Matrix Decompositions

LU Decomposition: Factorize A = LU where L is lower triangular (1s on diagonal) and U is upper triangular. Used for efficient solving of Ax = b (solve Ly = b via forward substitution, then Ux = y via back substitution). LU is faster than computing A^-1 and numerically stable with partial pivoting (PA = LU).
QR Decomposition: A = QR where Q is orthogonal (Q^⊤Q = I) and R is upper triangular. QR is more stable than LU for least squares (minimize ||Ax - b||₂) and computing eigenvalues (QR algorithm). Gram-Schmidt or Householder reflections produce QR.
SVD (Singular Value Decomposition): Any m×n matrix A = UΣV^⊤ where U (m×m) and V (n×n) are orthogonal, and Σ (m×n) is diagonal with non-negative singular values σ_i ≥ 0. SVD reveals the rank (number of non-zero σ_i), null space, range, and optimal low-rank approximations. Applications: PCA (principal component analysis), image compression, pseudoinverse (A⁺ = VΣ⁺U^⊤), and solving least squares even when A is singular or rectangular.
Cholesky Decomposition: For positive-definite symmetric A, decompose A = LL^⊤ where L is lower triangular. Cholesky is twice as fast as LU for this special case and used in optimization, Kalman filtering, and Monte Carlo simulations.

Norms and Condition Number

Matrix Norms: Measure the "size" of a matrix. Common norms include Frobenius norm ||A||_F = √(Σa_ij²), spectral norm ||A||₂ = largest singular value, and infinity norm ||A||_∞ = max row sum. Norms are used to assess error and convergence.
Condition Number: κ(A) = ||A|| · ||A^-1|| (often using spectral norm). κ(A) measures sensitivity to perturbations—how much the solution x changes when b or A changes slightly. κ(A) ≥ 1; κ(A) = 1 for orthogonal matrices (ideal). κ(A) > 10¹⁰ indicates severe ill-conditioning, where rounding errors are amplified and solutions are unreliable. Use SVD or regularization (ridge regression) for ill-conditioned systems.

How to Use the Matrix Calculator

This calculator provides a comprehensive suite of matrix operations, from basic arithmetic to advanced decompositions. Follow these steps to perform calculations efficiently:

Choose Your Operation: Select the desired operation from the dropdown or operation tabs: Add, Subtract, Multiply, Transpose, Determinant, Inverse, Rank, Trace, RREF, Eigenvalues/Eigenvectors, LU Decomposition, QR Decomposition, or SVD. The interface adapts based on your selection—single-matrix operations (transpose, determinant, inverse, RREF, eigen, decompositions) show one input grid, while binary operations (add, subtract, multiply) show two input grids (Matrix A and Matrix B).
Set Matrix Dimensions: Specify the number of rows and columns for each matrix. For operations like addition and subtraction, ensure both matrices have identical dimensions. For multiplication A×B, the number of columns in A must equal the number of rows in B. For square-only operations (determinant, inverse, eigenvalues, Cholesky), rows must equal columns. The calculator validates dimensions and displays errors if constraints are violated.
Enter Matrix Values: Input numbers into the grid cells. You can type directly, use Tab/Shift+Tab or arrow keys to navigate between cells, or paste data from a spreadsheet (comma-separated or tab-separated values). For large matrices, consider pasting CSV data for speed. The calculator accepts integers, decimals, and scientific notation (e.g., 1.5e-3). Leave cells blank or enter 0 for zero entries.
Adjust Precision and Options: Use the precision slider or input to control decimal places in results (typically 2-10 decimal places). For operations like RREF and determinant, enable "Show Steps" if available to see intermediate row operations or cofactor expansions. Some decompositions (LU, QR, SVD) may have options for pivoting strategy or tolerance for numerical stability.
Calculate: Click the "Calculate" button to perform the operation. The calculator processes your input and displays results in organized panels. For scalar results (determinant, rank, trace, condition number), values appear prominently at the top. For matrix results (sum, product, inverse, transpose, RREF, eigenvectors), the output matrix is displayed in a grid format. Decompositions show multiple matrices (e.g., LU shows L and U; SVD shows U, Σ, V).
Interpret Results: Review the output carefully. For RREF, identify pivot columns (leading 1s) to determine rank and free variables. For eigenvalues, note that complex values may appear (displayed as a + bi). For decompositions, verify properties like Q^⊤Q = I for QR or reconstruct the original matrix (A = LU, A = QR, A = UΣV^⊤) to check correctness. If the result shows "singular" or "non-invertible," the matrix does not have an inverse (det(A) = 0 or rank deficient). Use error messages and warnings to diagnose issues like dimension mismatches, numerical instability, or overflow.

Tips & Common Workflows

Solving Linear Systems Ax = b: To solve for x, you have several options: (1) Compute A^-1 and calculate x = A^-1b (simple but numerically unstable for large or ill-conditioned A). (2) Use RREF on the augmented matrix [A | b] to read off the solution directly (best for understanding solution structure and free variables). (3) Use LU decomposition: factorize A = LU, solve Ly = b via forward substitution, then solve Ux = y via back substitution (efficient for multiple right-hand sides). (4) Use QR decomposition: A = QR, then x = R^-1Q^⊤b (more stable than LU for ill-conditioned systems). (5) Use SVD for least squares when A is rectangular or singular: x = VΣ⁺U^⊤b minimizes ||Ax - b||₂.
Check Invertibility: Before attempting to invert a matrix, verify that it is square and has full rank. Compute rank(A) and det(A). If rank(A) < n or det(A) = 0, the matrix is singular and has no inverse. Also check the condition number κ(A)—if κ(A) > 10¹⁰, the inverse may be numerically unreliable even if det(A) ≠ 0. In such cases, use regularization (add λI to A) or switch to pseudoinverse via SVD.
Matrix Multiplication Dimensions: For C = AB, ensure the number of columns in A equals the number of rows in B. If A is m×n and B is n×p, then C is m×p. Remember that matrix multiplication is associative ((AB)C = A(BC)) but not commutative (AB ≠ BA in general). Use associativity to optimize computation order—e.g., compute A(BC) instead of (AB)C if it reduces the number of scalar multiplications.
Numerical Stability: Avoid computing explicit inverses whenever possible. For solving Ax = b, prefer LU, QR, or SVD decompositions. For least squares, use QR or SVD instead of the normal equations (A^⊤Ax = A^⊤b) which can square the condition number. Enable partial pivoting in LU to improve stability. For symmetric positive-definite systems, use Cholesky decomposition which is both fast and stable.
Symmetric and Special Matrices: If A = A^⊤, all eigenvalues are real and eigenvectors are orthogonal. Symmetric positive-definite matrices (all eigenvalues > 0) have Cholesky decomposition A = LL^⊤ which is twice as fast as LU. Diagonal matrices have trivial eigenvalues (the diagonal entries) and simple multiplication/inversion. Orthogonal matrices (Q^⊤Q = I) preserve lengths and angles, have κ(Q) = 1, and Q^-1 = Q^⊤.
Data Science and PCA: SVD is the foundation of Principal Component Analysis (PCA). For a data matrix X (centered, n×p), compute X = UΣV^⊤. The columns of V are the principal components (directions of maximum variance), and the singular values σ_i (or σ_i²) represent the variance explained by each component. Truncate to the top k components for dimensionality reduction: X_k = U_kΣ_kV_k^⊤ is the best rank-k approximation in Frobenius norm.
Eigenvalue Applications: Eigenvalues reveal stability of dynamical systems (all |λ_i| < 1 ⇒ stable discrete system), vibrational modes (mechanical/structural engineering), and Google PageRank (dominant eigenvector of link matrix). For symmetric matrices, diagonalization simplifies matrix powers: A^k = QΛ^kQ^⊤ where Λ^k is trivial (raise diagonal entries to power k).
Error Checking: After computing a result, verify correctness when possible. For inverse, check that AA^-1 ≈ I. For decompositions, reconstruct A from factors (A = LU, A = QR, A = UΣV^⊤) and compare to the original. For eigenvalues, verify Av = λv for each eigenpair. Small discrepancies (on the order of machine epsilon ≈ 10^-15 for double precision) are normal due to floating-point arithmetic.

Understanding Your Results

The calculator displays results in multiple formats depending on the operation. Here's how to interpret each type of output:

Output	Meaning & Interpretation
Result Matrix	The output of the selected operation (e.g., A+B, AB, A^-1, A^⊤, RREF). Dimensions match operation rules. For RREF, identify pivot positions (leading 1s) to determine rank and free variables. For inverse, verify AA^-1 = I. For transpose, verify dimensions flipped and elements (A^⊤)_ij = A_ji.
Determinant	Scalar value measuring area/volume scaling. det(A) = 0 ⇔ matrix is singular (non-invertible, rank deficient). \|det(A)\| > 1 indicates expansion, \|det(A)\| < 1 indicates contraction. Sign indicates orientation (positive = preserves orientation, negative = reverses).
Rank	Number of linearly independent rows/columns. For m×n matrix A, rank(A) ≤ min(m,n). Full rank = min(m,n) indicates maximal independence. Rank < n ⇒ matrix is singular (if square). Nullity (dimension of null space) = n - rank(A). Rank reveals degrees of freedom in solutions to Ax = b.
Trace	Sum of diagonal elements: tr(A) = Σa_ii. For square matrices, tr(A) equals the sum of eigenvalues (even if complex). Invariant under similarity transformations: tr(A) = tr(PAP^-1). Used in characteristic polynomial and measuring matrix "size" in some contexts.
Eigenvalues / Eigenvectors	Eigenvalues λ are scalars such that Av = λv for non-zero eigenvector v. Eigenvalues reveal scaling factors along eigenvector directions. Complex eigenvalues (a ± bi) occur for real matrices with rotational components. Sum of eigenvalues = tr(A); product = det(A). Eigenvectors form a basis (if linearly independent) for diagonalization.
RREF with Pivots	Reduced row echelon form shows pivot columns (leading 1s) and free variables (non-pivot columns). Rank = number of pivots. For augmented [A \| b], consistency requires no row [0...0 \| c] with c ≠ 0. Infinite solutions exist when free variables are present. Read solution: express pivot variables in terms of free variables.
LU Decomposition	A = LU (or PA = LU with pivoting) where L is lower triangular (1s on diagonal) and U is upper triangular. Use for efficient solving: Ly = b (forward sub), then Ux = y (back sub). Faster than computing A^-1. Pivoting (row swaps in P) improves numerical stability.
QR Decomposition	A = QR where Q is orthogonal (Q^⊤Q = I) and R is upper triangular. More stable than LU for least squares (minimize \|\|Ax - b\|\|₂). Solution: x = R^-1Q^⊤b. Q preserves lengths/angles; condition number κ(Q) = 1. Used in QR algorithm for eigenvalues.
SVD (U, Σ, V)	A = UΣV^⊤ where U (m×m) and V (n×n) are orthogonal, Σ (m×n) diagonal with singular values σ_i ≥ 0. Rank = number of non-zero σ_i. Columns of U are left singular vectors (orthonormal basis for range), columns of V are right singular vectors (orthonormal basis for row space). Condition number κ(A) = σ_max/σ_min. Truncate to top k singular values for optimal low-rank approximation. Pseudoinverse: A⁺ = VΣ⁺U^⊤.
Condition Number	κ(A) = \|\|A\|\| · \|\|A^-1\|\| (typically spectral norm, \|\|A\|\|₂ = largest singular value). Measures sensitivity to perturbations. κ(A) = 1 for orthogonal matrices (ideal). κ(A) < 100 is well-conditioned. κ(A) > 10¹⁰ is severely ill-conditioned—solutions unreliable due to rounding errors. Use SVD or regularization for ill-conditioned problems.

Matrix Operations Calculator

Matrix Operations Calculator

Matrix Operations Calculator

Understanding Matrix Operations

Basic Operations

Determinant, Inverse, and Properties

Row Reduction and Systems of Equations

Eigenvalues, Eigenvectors, and Spectral Decomposition

Matrix Decompositions

Norms and Condition Number

How to Use the Matrix Calculator

Tips & Common Workflows

Understanding Your Results

Frequently Asked Questions

Related Math & Linear Algebra Calculators

Regression Calculator

Descriptive Statistics

Calculus Calculator

Normal Distribution

Confidence Interval

Probability Calculator

Matrix Operations Calculator

Matrix Operations Calculator

Matrix Operations Calculator

Understanding Matrix Operations

Basic Operations

Determinant, Inverse, and Properties

Row Reduction and Systems of Equations

Eigenvalues, Eigenvectors, and Spectral Decomposition

Matrix Decompositions

Norms and Condition Number

How to Use the Matrix Calculator

Tips & Common Workflows

Understanding Your Results

Frequently Asked Questions

Related Math & Linear Algebra Calculators

Regression Calculator

Descriptive Statistics

Calculus Calculator

Normal Distribution

Confidence Interval

Probability Calculator