Class Summary Class Description DenseFloatCholeskyDecomposition For a symmetric, positive definite matrix`A`, the Cholesky decomposition is a lower triangular matrix`L`so that`A = L*L'`; If the matrix is not symmetric or positive definite, the constructor returns a partial decomposition and sets an internal flag that may be queried by the`isSymmetricPositiveDefinite()`method.DenseFloatEigenvalueDecomposition Eigenvalues and eigenvectors of a real matrix`A`.DenseFloatLUDecomposition For an`m x n`matrix`A`with`m >= n`, the LU decomposition is an`m x n`unit lower triangular matrix`L`, an`n x n`upper triangular matrix`U`, and a permutation vector`piv`of length`m`so that`A(piv,:) = L*U`; If`m < n`, then`L`is`m x m`and`U`is`m x n`.DenseFloatLUDecompositionQuick A low level version of`DenseFloatLUDecomposition`

, avoiding unnecessary memory allocation and copying.DenseFloatQRDecomposition For an`m x n`matrix`A`with`m >= n`, the QR decomposition is an`m x n`orthogonal matrix`Q`and an`n x n`upper triangular matrix`R`so that`A = Q*R`.DenseFloatSingularValueDecomposition For an`m x n`matrix`A`, the singular value decomposition is an`m x m`orthogonal matrix`U`, an`m x n`diagonal matrix`S`, and an`n x n`orthogonal matrix`V`so that`A = U*S*V'`.SparseFloatCholeskyDecomposition For a symmetric, positive definite matrix`A`, the Cholesky decomposition is a lower triangular matrix`L`so that`A = L*L'`; If the matrix is not symmetric positive definite, the IllegalArgumentException is thrown.SparseFloatLUDecomposition For a square matrix`A`, the LU decomposition is an unit lower triangular matrix`L`, an upper triangular matrix`U`, and a permutation vector`piv`so that`A(piv,:) = L*U`SparseFloatQRDecomposition For an`m x n`matrix`A`with`m >= n`, the QR decomposition is an`m x n`orthogonal matrix`Q`and an`n x n`upper triangular matrix`R`so that`A = Q*R`.

## Package cern.colt.matrix.tfloat.algo.decomposition Description

Martrix decompositions.

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