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mathjslab

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MathJSLab - An interpreter with language syntax like MATLAB®/Octave, ISBN 978-65-00-82338-7.

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import { ComplexType } from './Complex'; import { MultiArray } from './MultiArray'; import { type NodeReturnList } from './AST'; /** * `LinearAlgebra` configuration options type. */ type LinearAlgebraConfig = { /** * LU Waste. */ wasteLU: number; qrPhaseEpsilon: number; }; export declare const LinearAlgebraConfigKeyTable: (keyof LinearAlgebraConfig)[]; /** * # LinearAlgebra * * LinearAlgebra abstract class. Implements static methods related to linear algebra operations and algorithms. * * ## References * * * [Linear Algebra at Wolfram MathWorld](https://mathworld.wolfram.com/LinearAlgebra.html) * * [Fundamental Theorem of Linear Algebra at Wolfram MathWorld](https://mathworld.wolfram.com/FundamentalTheoremofLinearAlgebra.html) * * [Linear algebra at Wikipedia](https://en.wikipedia.org/wiki/Linear_algebra) */ declare abstract class LinearAlgebra { /** * `LinearAlgebra` default settings. */ static readonly defaultSettings: LinearAlgebraConfig; /** * `LinearAlgebra` current settings. */ static readonly settings: LinearAlgebraConfig; /** * Set configuration options for `LinearAlgebra`. * @param config Configuration options. */ static readonly set: (config: Partial<LinearAlgebraConfig>) => void; /** * Identity matrix * @param args * * `eye(N)` - create identity N x N * * `eye(N,M)` - create identity N x M * * `eye([N,M])` - create identity N x M * @returns Identity matrix */ static readonly eye: (...args: MultiArray[] | ComplexType[]) => MultiArray | ComplexType; /** * Sum of diagonal elements. * @param M Matrix. * @returns Trace of matrix. */ static readonly trace: (M: MultiArray) => ComplexType; /** * Transpose and apply function. * @param M Matrix. * @returns Transpose matrix with `func` applied to each element. */ private static readonly applyTranspose; /** * Transpose. * @param M Matrix. * @returns Transpose matrix. */ static readonly transpose: (M: MultiArray) => MultiArray; /** * Complex conjugate transpose. * @param M Matrix. * @returns Complex conjugate transpose matrix. */ static readonly ctranspose: (M: MultiArray) => MultiArray; /** * Matrix power (multiple multiplication). * @param left * @param right * @returns */ static readonly power: (left: MultiArray, right: ComplexType) => MultiArray; /** * Matrix determinant using LU decomposition with pivot sign correction. * Uses `LinearAlgebra.luDecomposition`. * @param M Matrix. * @returns Matrix determinant. */ static readonly det: (M: MultiArray) => ComplexType; /** * Computes the LU decomposition with partial pivoting. * @param M Input square matrix. * @returns An object { L, U, P, swaps } where: * - L: lower-triangular with unit diagonal (MultiArray) * - U: upper-triangular (MultiArray) * - P: permutation matrix (MultiArray) * - swaps: number of row swaps performed (integer) * * ## References * * https://www.codeproject.com/Articles/1203224/A-Note-on-PA-equals-LU-in-Javascript * * https://rosettacode.org/wiki/LU_decomposition#JavaScript */ static readonly luDecomposition: (A: MultiArray) => { L: MultiArray; U: MultiArray; P: MultiArray; swaps: number; }; /** * PLU matrix factorization. * @param M Matrix. * @returns L, U and P matrices as multiple output. */ static readonly lu: (M: MultiArray) => NodeReturnList; /** * Returns the inverse of matrix `M`. * inv(A) wrapper using LAPACK.getrf_blocked + LAPACK.getrs. * Behavior: MATLAB-like: if factorization reports info !== 0, emit warning and return matrix filled with Inf. * @param M Matrix. * @returns Inverted matrix. */ static readonly inv: (A: MultiArray) => MultiArray; /** * Gaussian elimination algorithm for solving systems of linear equations. * Adapted from: https://github.com/itsravenous/gaussian-elimination * ## References * * https://mathworld.wolfram.com/GaussianElimination.html * @param M Matrix. * @param m Vector. * @returns Solution of linear system. */ static readonly gauss: (M: MultiArray, m: MultiArray) => MultiArray; /** * High-performance dot product. Fully ND-aware, column-major, no index * conversions (≈2-3× faster). Computes sum(conj(A).*B, dim) with minimal * per-element overhead. * C = dot(A,B) or C = dot(A,B,dim) * Sums conj(A).*B along the specified dimension (zero-based operateDim). If dim is omitted, * use the first non-singleton dimension (zero-based). * @param A First array (MultiArray). * @param B Second array (MultiArray). * @param dim (optional) Dimension along which to operate (ComplexType representing integer, 1-based externally). * @returns Scalar (ComplexType) if result is single value, else a MultiArray. */ static readonly dot: (A: MultiArray, B: MultiArray, dim?: ComplexType) => MultiArray | ComplexType; /** * Cross product along dimension `dim` (MATLAB semantics). * A and B must have the same size except along `dim` where size must be 3. * dim is optional and is 1-based like MATLAB; internally converted to 0-based. * @param A * @param B * @param dim * @returns */ static readonly cross: (A: MultiArray, B: MultiArray, dim?: any) => MultiArray; /** * * @param A * @param B * @returns */ static readonly kron: (A: MultiArray, B: MultiArray) => MultiArray; /** * Normalize phases so that R diagonal becomes real non-negative: * For k = 0..minmn-1: * phi = R[k][k] / |R[k][k]| * R[k, j] := R[k, j] / phi (j = k..n-1) * Q[i, k] := Q[i, k] * phi (i = 0..m-1) * @param Q * @param R * @param phis */ static readonly qrPhaseNormalize: (phis: ComplexType[], R: MultiArray, Q?: MultiArray) => void; /** * * @param A * @param result * @returns */ static readonly qrDecomposition: (A: MultiArray, result: 1 | 2 | 3) => { Q?: MultiArray; R: MultiArray; P?: MultiArray; }; /** * * @param M * @returns */ static readonly qr: (M: MultiArray) => NodeReturnList; /** * LinearAlgebra functions. */ static functions: { [F in keyof LinearAlgebra]: Function; }; } export { LinearAlgebra }; declare const _default: { LinearAlgebra: typeof LinearAlgebra; }; export default _default;