cholesky-solve - npm Package Compare versions

Comparing version 0.1.0 to 0.2.0

index.js

		@@ -164,15 +164,15 @@ function ldl_symbolic
		function ldl_permt(
		n, /* size of X, B, and P */
		X, /* output of size n. */
		B, /* input of size n. */
		P /* input permutation array of size n. */
		n, /* size of X, B, and P */
		X, /* output of size n. */
		B, /* input of size n. */
		P /* input permutation array of size n. */
		) {
		var j
		for (j = 0; j < n; j++)
		{
		X [P[j]] = B[j]
		}
		var j
		for (j = 0; j < n; j++)
		{
		X [P[j]] = B[j]
		}
		}

		function choleskySolve (M, b, n, P) {
		function prepare (M, n, P) {
		const ANZ = M.length
		@@ -273,9 +273,12 @@
		if (d === n) {
		ldl_perm(n, bp1, b, P);
		ldl_lsolve(n, bp1, Lp, Li, Lx)
		ldl_dsolve(n, bp1, D)
		ldl_ltsolve(n, bp1, Lp, Li, Lx)
		ldl_permt(n, x, bp1, P);
		return function(b) {
		ldl_perm(n, bp1, b, P);
		ldl_lsolve(n, bp1, Lp, Li, Lx)
		ldl_dsolve(n, bp1, D)
		ldl_ltsolve(n, bp1, Lp, Li, Lx)
		ldl_permt(n, x, bp1, P);

		return x
		return x
		}

		} else {
		@@ -286,2 +289,2 @@ return null

		module.exports = choleskySolve
		module.exports.prepare = prepare

package.json

		{
		"name": "cholesky-solve",
		"version": "0.1.0",
		"version": "0.2.0",
		"description": "This module solves sparse symmetric positive definite linear systems by using the Cholesky decomposition",
		@@ -5,0 +5,0 @@ "main": "index.js",

README.md

		# cholesky-solve[WIP]

		This module solves sparse symmetric positive definite linear systems,
		by finding the Cholesky decomposition, and then doing forward
		substitution and backward substitution. It is basically a Javascript
		port of the paper "Algorithm 8xx: a concise sparse Cholesky
		factorization package". This kind of solver has many applications in
		digital geometry processing.
		by finding the Cholesky decomposition(the `LDL^T` decomposition, and not
		the `LL^T` decomposition), and then doing forward substitution and
		backward substitution. It is basically a Javascript port of the paper
		"Algorithm 8xx: a concise sparse Cholesky factorization package". This
		kind of solver has many applications in digital geometry processing.

		@@ -49,6 +49,11 @@ ## Install
		var P = require('cuthill-mckee')(M, n)
		// solve the equation

		// finally, solve the equation
		// Mx = b
		// and print x
		console.log(choleskySolve(M, b, n, P))

		// the `prepare` method returns a function that can be used to solve
		// the equation for any value of b.
		var solve = choleskySolve.prepare(M, n, P)
		console.log(solve(b))
		```
		@@ -58,7 +63,9 @@

		### `require("cholesky-solve")(M, b, n, [P])`
		Solves the equation `Mx = b` by using the Cholesky decomposition.
		### `require("cholesky-solve").prepare(M, n, [P])`

		Decomposes `M` into the Cholesky decomposition of the form `LDL^T`. A
		function is returned that can be used to solve the equation `Mx = b`,
		for some given value of `b`.

		* `M` a list of the matrix coefficients of the sparse matrix `M`.
		* `b` the vector on the right-hand side. A regular array of length `n`
		* `n` the dimension of the matrix `M`
		@@ -70,2 +77,3 @@ * `P` encodes a permutation matrix that preconditions `M` before the Cholesky decomposition is solved for. A possible algorithm for finding a good permutation is

		Returns An array encoding the solution to the equation `Mx = b`.
		Returns A function that takes a single argument `b`. The function
		returns the solution to the equation `Mx = b`, encoded as a simple array.

test.js

		@@ -10,4 +10,9 @@ var choleskySolve = require('./')

		function choleskySolveHelper(M, b, n, P) {
		var solve = choleskySolve.prepare(M, n, P)
		return solve(b)
		}

		function solveAndAssert(t, n, M, b, P, expectedSolution) {
		var foundSolution = choleskySolve(M, b, n, P)
		var foundSolution = choleskySolveHelper(M, b, n, P)

		@@ -159,3 +164,3 @@ for(var i=0; i< n; ++i) {
		// solve.
		var foundSolution = choleskySolve(M, b, n, P)
		var foundSolution = choleskySolveHelper(M, b, n, P)

		@@ -162,0 +167,0 @@ // check that the residual vector is 0.

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