Equana

Configure the Eigen WASM module loader.

Get the loaded Eigen WASM module.

Check if the Eigen WASM module has been loaded.

Load and initialize the Eigen WASM module.

Reset the Eigen WASM module state.

Eigen WASM Memory Management Utilities

Allocate memory for an Int32Array on the WASM heap.

Allocate memory for a pointer (used for output parameters).

Convert a column-major Float64Array to 2D row-major array.

Copy data from WASM heap memory to a new Float64Array.

Copy Int32 data from WASM heap memory to a new Int32Array.

Copy Int32 data to WASM heap memory.

Copy a JavaScript array to WASM heap memory.

Free memory on the WASM heap.

Read a pointer value from WASM memory.

Convert a 2D row-major array to column-major Float64Array.

Create a view into WASM heap memory without copying.

Get type-safe wrapped functions for all Eigen WASM operations.

Reset the cached wrappers.

Compute Cholesky decomposition: A = LL^T

Check if a matrix is positive-definite by attempting Cholesky decomposition.

Check if a matrix is symmetric within a tolerance.

Compute eigenvalue decomposition for general (non-symmetric) matrices.

Compute eigenvalue decomposition for symmetric matrices: A = V * D * V^T

Compute eigenvalues only for general matrices.

Compute eigenvalues only for symmetric matrices.

Check if all eigenvalues have negative real parts.

Check if all eigenvalues have positive real parts.

Check if all eigenvalues are real (within tolerance).

Compute the spectral radius of a matrix.

Check if a matrix is singular (has zero or near-zero determinant).

Compute LU decomposition with partial pivoting: PA = LU

Compute QR decomposition: A = QR

Compute the numerical rank of a matrix using QR decomposition.

Compute the condition number of a matrix using SVD.

Compute the Moore-Penrose pseudoinverse using SVD.

Compute the numerical rank of a matrix using SVD.

Compute the Singular Value Decomposition: A = U * diag(S) * V^T

Compute only the singular values of a matrix.

Get the number of columns in a matrix.

Get the raw data pointer for a matrix.

Free the memory associated with a matrix.

Get a single element from a matrix.

Get a column from a matrix as a Float64Array.

Get the diagonal of a matrix as a Float64Array.

Get a row from a matrix as a Float64Array.

Get the number of rows in a matrix.

Set a single element in a matrix.

Get the shape of a matrix as [rows, cols].

Get the total number of elements in a matrix.

Convert a matrix to a Float64Array (column-major order).

Convert a matrix to a 2D JavaScript array (row-major order).

Eigenwasm - High-level TypeScript API for Eigen Linear Algebra Library

Add two matrices: C = A + B

Add a scalar to all elements: C = A + scalar

Divide all elements by a scalar: C = A / scalar

Element-wise division: C = A ./ B

Element-wise multiplication: C = A .* B

Compute the power of each element: C = A .^ p

Multiply two matrices: C = A * B

Negate a matrix: C = -A

Multiply a matrix by a scalar: C = A * scalar

Compute element-wise square root: C = sqrt(A)

Subtract two matrices: C = A - B

Subtract a scalar from all elements: C = A - scalar

Create a deep copy of a matrix.

Create a diagonal matrix from a vector of diagonal values.

Create an identity matrix.

Create a matrix from a 2D JavaScript array (row-major order).

Create a matrix filled with a constant value.

Create a matrix with linearly spaced values.

Create a matrix from a Float64Array (column-major order).

Create a matrix filled with ones.

Create a matrix with random values in the range [-1, 1].

Create a matrix filled with zeros.

Check if all matrix elements are finite (not NaN or Infinity).

Compute the determinant of a square matrix.

Check if a matrix contains any infinite values.

Check if a matrix contains any NaN values.

Find the maximum element in a matrix.

Compute the mean of all elements in a matrix.

Find the minimum element in a matrix.

Compute the Frobenius norm of a matrix: ||A||_F = sqrt(sum(a_ij^2))

Compute the 1-norm (maximum absolute column sum).

Compute the infinity-norm (maximum absolute row sum).

Compute the max norm (maximum absolute element).

Compute the squared Frobenius norm: ||A||_F^2 = sum(a_ij^2)

Compute the product of all elements in a matrix.

Compute the standard deviation of all elements in a matrix.

Compute the sum of all elements in a matrix.

Compute the trace of a matrix (sum of diagonal elements).

Compute the variance of all elements in a matrix.

Compute the adjoint (conjugate transpose) of a matrix: B = A^H

Compute the complex conjugate of a matrix.

Compute the inverse of a square matrix: B = A^(-1)

Compute the normalized matrix (divide by Frobenius norm).

Normalize a matrix in-place (divide by Frobenius norm).

Set a matrix to identity in-place.

Set all matrix elements to one in-place.

Set all matrix elements to random values in [-1, 1] in-place.

Set all matrix elements to zero in-place.

Compute the transpose of a matrix: B = A^T

Create a matrix filled with zeros.

Create a matrix filled with ones.

Create an identity matrix.

Create a matrix with random values in [-1, 1].

Create a matrix from a 2D array.

Create a matrix from a Float64Array (column-major).

Create a diagonal matrix.

Create a matrix filled with a constant value.

Add two linear operators: (A + B) * x = A * x + B * x

Create an operator that applies A and then adds a constant vector.

Create a block diagonal operator from multiple operators.

Wrap an operator to cache its result for repeated calls with the same input.

Compose two linear operators: (A * B) * x = A(B(x))

Create a linear combination of operators: (alpha*A + beta*B) * x

Negate a linear operator: (-A) * x = -(A * x)

Scale a linear operator: (alpha * A) * x = alpha * (A * x)

Create an operator that applies multiple operators and returns the concatenated result.

Subtract two linear operators: (A - B) * x = A * x - B * x

Create the transpose operator: A^T where A^T(y) = A^*(y) in the dual sense.

Create a matrix-vector multiplication operator from a dense matrix.

Create a transpose matrix-vector operator: x -> A^T * x

Create a diagonal matrix-vector operator from a vector of diagonal elements.

Create an identity operator: x -> x (copy)

Create a matrix-vector multiplication operator from a sparse matrix.

Create a symmetric matrix-vector operator with only lower triangular storage.

Create a zero operator: x -> 0

Solve the linear system Ax = b using Cholesky decomposition.

Solve the normal equations A^T A x = A^T b using Cholesky decomposition.

Solve the least squares problem: minimize ||Ax - b||_2

Solve least squares with full diagnostic output.

Solve weighted least squares: minimize ||W^(1/2)(Ax - b)||_2

Compute the inverse of a matrix using LU decomposition.

Solve the linear system Ax = b using LU decomposition.

Solve a least squares problem: minimize ||Ax - b||_2

Solve the linear system Ax = b using QR decomposition.

Solve the linear system Ax = b.

Solve multiple linear systems with the same coefficient matrix.

Destroy a sparse matrix and free its memory.

Create an empty sparse matrix with given dimensions.

Create a sparse diagonal matrix.

Create a sparse identity matrix.

Create a sparse matrix from COO (Coordinate) format arrays.

Create a sparse matrix from a dense matrix, keeping only non-zero elements.

Create a sparse matrix from a list of triplets (COO format).

Create a sparse matrix-vector product function (closure).

Get the density of a sparse matrix (nnz / total elements).

Check if a sparse matrix is empty (has no non-zeros).

Get the number of non-zero elements in a sparse matrix.

Compute sparse matrix-vector product: y = S * x

Solve sparse linear system with automatic method selection.

Solve sparse linear system Ax = b using BiCGSTAB method.

Solve sparse linear system Ax = b using Conjugate Gradient method.

Solve sparse linear system Ax = b using SparseLU (direct method).

Create an empty sparse matrix with given dimensions.

Create a sparse matrix from triplets (COO format).

Create a sparse matrix from separate COO arrays.

Create a sparse matrix from a dense 2D array.

Create a sparse identity matrix.

Create a sparse diagonal matrix.

Create a sparse tridiagonal matrix.

Create a sparse matrix from a banded structure.

Extract a submatrix (block) from a matrix.

Create a block diagonal matrix from multiple matrices.

Extract a column from a matrix as a vector.

Extract the diagonal of a matrix as a vector.

Flatten a matrix to a vector.

Horizontally concatenate matrices: [A, B, C, ...]

Reshape a matrix to new dimensions.

Extract a row from a matrix as a vector.

Extract columns from a matrix.

Extract rows from a matrix.

Set a block of a matrix to values from another matrix.

Set a column of a matrix from a vector.

Set the diagonal of a matrix from a vector.

Set a row of a matrix from a vector.

Vertically concatenate matrices: [A; B; C; ...]

Compute the condition number of a matrix using SVD.

Compute the Frobenius norm of a matrix.

Compute the nuclear norm (trace norm) of a matrix.

Compute an orthonormal basis for the null space of A.

Compute the numerical rank using a tolerance.

Compute the Moore-Penrose pseudoinverse of a matrix.

Compute an orthonormal basis for the column space (range) of A.

Compute the matrix rank using SVD.

Compute the spectral norm (2-norm) of a matrix.

Check if a matrix is diagonal.

Check if two matrices are equal within a tolerance.

Check if a matrix is the identity matrix.

Check if a matrix is invertible (non-singular).

Check if a matrix is lower triangular.

Check if a matrix is orthogonal (A^T * A = I).

Check if a matrix is positive definite.

Check if a matrix is positive semi-definite.

Check if a matrix is singular (determinant is zero).

Check if a matrix is square (rows == cols).

Check if a matrix is symmetric (A == A^T) within a tolerance.

Check if a matrix is upper triangular.

Check if a matrix is the zero matrix.

Get the raw data pointer for a vector.

Free the memory associated with a vector.

Get the first element of a vector.

Get a single element from a vector.

Get the last element of a vector.

Set a single element in a vector.

Get the size (length) of a vector.

Get a slice of a vector as a new Float64Array.

Convert a vector to a Float64Array.

Convert a vector to a JavaScript array.

Create a vector with values from start to end with given step.

Create a standard basis vector (one-hot vector).

Create a copy of a vector.

Create a vector filled with a constant value.

Create a vector with linearly spaced values.

Create a vector filled with ones.

Create a vector with random values in [-1, 1].

Create a vector from a Float64Array.

Create a vector from a JavaScript array.

Create a vector filled with zeros.

Add two vectors: w = u + v

Compute the angle between two vectors in radians.

Compute the cross product of two 3D vectors: u × v

Compute the distance between two vectors: ||u - v||_2

Compute the dot product of two vectors: u · v

Compute element-wise product: w_i = u_i * v_i

Find the maximum element.

Compute the mean of all elements.

Find the minimum element.

Negate a vector: w = -v

Compute the L1 norm (Manhattan norm): ||v||_1 = sum(|v_i|)

Return a normalized copy of a vector.

Normalize a vector in-place (divide by its norm).

Compute the infinity norm (max norm): ||v||_∞ = max(|v_i|)

Compute the squared Euclidean norm: ||v||_2^2 = sum(v_i^2)

Compute the Euclidean (L2) norm of a vector: ||v||_2 = sqrt(sum(v_i^2))

Scale a vector: w = scalar * v

Subtract two vectors: w = u - v

Compute the sum of all elements.

Create a vector filled with zeros.

Create a vector filled with ones.

Create a vector with random values in [-1, 1].

Create a vector from a JavaScript array.

Create a vector from a Float64Array.

Create a vector filled with a constant value.

Create a standard basis vector (one-hot).

Create a vector with linearly spaced values.