add documentation to DoubleLinearOpsTensorAlgebra

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/api/LinearOpsTensorAlgebra.kt

@@ -81,7 +81,7 @@ public interface LinearOpsTensorAlgebra<T> :
      * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
      * For more information: https://pytorch.org/docs/stable/linalg.html#torch.linalg.svd
      *
-     * @return the determinant.
+     * @return triple `(U, S, V)`.
      */
     public fun Tensor<T>.svd(): Triple<Tensor<T>, Tensor<T>, Tensor<T>>
 

kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/algebras/DoubleLinearOpsTensorAlgebra.kt

@@ -20,7 +20,10 @@ import space.kscience.kmath.tensors.core.luPivotHelper
 import space.kscience.kmath.tensors.core.pivInit
 import kotlin.math.min
 
-
+/**
+ * Implementation of common linear algebra operations on double numbers.
+ * Implements the LinearOpsTensorAlgebra<Double> interface.
+ */
 public object DoubleLinearOpsTensorAlgebra :
     LinearOpsTensorAlgebra<Double>,
     DoubleTensorAlgebra() {
@@ -29,12 +32,41 @@ public object DoubleLinearOpsTensorAlgebra :
 
     override fun Tensor<Double>.det(): DoubleTensor = detLU(1e-9)
 
+    /**
+     * Computes the LU factorization of a matrix or batches of matrices `input`.
+     * Returns a tuple containing the LU factorization and pivots of `input`.
+     *
+     * @param epsilon permissible error when comparing the determinant of a matrix with zero
+     * @return pair of `factorization` and `pivots`.
+     * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor.
+     * The `pivots`  has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows.
+     */
     public fun Tensor<Double>.luFactor(epsilon: Double): Pair<DoubleTensor, IntTensor> =
         computeLU(tensor, epsilon)
             ?: throw IllegalArgumentException("Tensor contains matrices which are singular at precision $epsilon")
 
+    /**
+     * Computes the LU factorization of a matrix or batches of matrices `input`.
+     * Returns a tuple containing the LU factorization and pivots of `input`.
+     * Uses an error of ``1e-9`` when calculating whether a matrix is degenerate.
+     *
+     * @return pair of `factorization` and `pivots`.
+     * The `factorization` has the shape ``(*, m, n)``, where``(*, m, n)`` is the shape of the `input` tensor.
+     * The `pivots`  has the shape ``(∗, min(m, n))``. `pivots` stores all the intermediate transpositions of rows.
+     */
     public fun Tensor<Double>.luFactor(): Pair<DoubleTensor, IntTensor> = luFactor(1e-9)
 
+    /**
+     * Unpacks the data and pivots from a LU factorization of a tensor.
+     * Given a tensor [luTensor], return tensors (P, L, U) satisfying ``P * luTensor = L * U``,
+     * with `P` being a permutation matrix or batch of matrices,
+     * `L` being a lower triangular matrix or batch of matrices,
+     * `U` being an upper triangular matrix or batch of matrices.
+     *
+     * @param luTensor the packed LU factorization data
+     * @param pivotsTensor the packed LU factorization pivots
+     * @return triple of P, L and U tensors
+     */
     public fun luPivot(
         luTensor: Tensor<Double>,
         pivotsTensor: Tensor<Int>
@@ -66,6 +98,18 @@ public object DoubleLinearOpsTensorAlgebra :
         return Triple(pTensor, lTensor, uTensor)
     }
 
+    /**
+     * QR decomposition.
+     *
+     * Computes the QR decomposition of a matrix or a batch of matrices, and returns a pair `(Q, R)` of tensors.
+     * Given a tensor `input`, return tensors (Q, R) satisfying ``input = Q * R``,
+     * with `Q` being an orthogonal matrix or batch of orthogonal matrices
+     * and `R` being an upper triangular matrix or batch of upper triangular matrices.
+     *
+     * @param epsilon permissible error when comparing tensors for equality.
+     * Used when checking the positive definiteness of the input matrix or matrices.
+     * @return pair of Q and R tensors.
+     */
     public fun Tensor<Double>.cholesky(epsilon: Double): DoubleTensor {
         checkSquareMatrix(shape)
         checkPositiveDefinite(tensor, epsilon)
@@ -98,6 +142,18 @@ public object DoubleLinearOpsTensorAlgebra :
     override fun Tensor<Double>.svd(): Triple<DoubleTensor, DoubleTensor, DoubleTensor> =
         svd(epsilon = 1e-10)
 
+    /**
+     * Singular Value Decomposition.
+     *
+     * Computes the singular value decomposition of either a matrix or batch of matrices `input`.
+     * The singular value decomposition is represented as a triple `(U, S, V)`,
+     * such that ``input = U.dot(diagonalEmbedding(S).dot(V.T))``.
+     * If input is a batch of tensors, then U, S, and Vh are also batched with the same batch dimensions as input.
+     *
+     * @param epsilon permissible error when calculating the dot product of vectors,
+     * i.e. the precision with which the cosine approaches 1 in an iterative algorithm.
+     * @return triple `(U, S, V)`.
+     */
     public fun Tensor<Double>.svd(epsilon: Double): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val size = tensor.linearStructure.dim
         val commonShape = tensor.shape.sliceArray(0 until size - 2)
@@ -125,7 +181,14 @@ public object DoubleLinearOpsTensorAlgebra :
     override fun Tensor<Double>.symEig(): Pair<DoubleTensor, DoubleTensor> =
         symEig(epsilon = 1e-15)
 
-    //For information: http://hua-zhou.github.io/teaching/biostatm280-2017spring/slides/16-eigsvd/eigsvd.html
+    /**
+     * Returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices,
+     * represented by a pair (eigenvalues, eigenvectors).
+     *
+     * @param epsilon permissible error when comparing tensors for equality
+     * and when the cosine approaches 1 in the SVD algorithm.
+     * @return a pair (eigenvalues, eigenvectors)
+     */
     public fun Tensor<Double>.symEig(epsilon: Double): Pair<DoubleTensor, DoubleTensor> {
         checkSymmetric(tensor, epsilon)
         val (u, s, v) = tensor.svd(epsilon)
@@ -139,6 +202,13 @@ public object DoubleLinearOpsTensorAlgebra :
         return eig to v
     }
 
+    /**
+     * Computes the determinant of a square matrix input, or of each square matrix in a batched input
+     * using LU factorization algorithm.
+     *
+     * @param epsilon error in the LU algorithm - permissible error when comparing the determinant of a matrix with zero
+     * @return the determinant.
+     */
     public fun Tensor<Double>.detLU(epsilon: Double = 1e-9): DoubleTensor {
 
         checkSquareMatrix(tensor.shape)
@@ -164,6 +234,15 @@ public object DoubleLinearOpsTensorAlgebra :
         return detTensor
     }
 
+    /**
+     * Computes the multiplicative inverse matrix of a square matrix input, or of each square matrix in a batched input
+     * using LU factorization algorithm.
+     * Given a square matrix `a`, return the matrix `aInv` satisfying
+     * ``a.dot(aInv) = aInv.dot(a) = eye(a.shape[0])``.
+     *
+     * @param epsilon error in the LU algorithm - permissible error when comparing the determinant of a matrix with zero
+     * @return the multiplicative inverse of a matrix.
+     */
     public fun Tensor<Double>.invLU(epsilon: Double = 1e-9): DoubleTensor {
         val (luTensor, pivotsTensor) = luFactor(epsilon)
         val invTensor = luTensor.zeroesLike()
@@ -177,6 +256,18 @@ public object DoubleLinearOpsTensorAlgebra :
         return invTensor
     }
 
+    /**
+     * LUP decomposition
+     *
+     * Computes the LUP decomposition of a matrix or a batch of matrices.
+     * Given a tensor `input`, return tensors (P, L, U) satisfying ``P * input = L * U``,
+     * with `P` being a permutation matrix or batch of matrices,
+     * `L` being a lower triangular matrix or batch of matrices,
+     * `U` being an upper triangular matrix or batch of matrices.
+     *
+     * @param epsilon permissible error when comparing the determinant of a matrix with zero
+     * @return triple of P, L and U tensors
+     */
     public fun Tensor<Double>.lu(epsilon: Double = 1e-9): Triple<DoubleTensor, DoubleTensor, DoubleTensor> {
         val (lu, pivots) = this.luFactor(epsilon)
         return luPivot(lu, pivots)