| #------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| # |
| #------------------------------------------------------------- |
| |
| source("scalable_linalg/lu.dml") as decomp |
| |
| test_LU = function() { |
| print("Testing LU Decomposition.") |
| n = 1000 |
| b = 100 # smallest block size |
| eps = n*n*1e-12 # for lareger matrices eps should be larger |
| |
| # create random square matrix |
| A = rand(rows=n, cols=n, min=-1.0, max=1.0, pdf="uniform") |
| |
| [P, L, U] = decomp::LU(A, b) |
| |
| # check if PA = LU. Infinity norm of (PA - LU) must be close to zero |
| diff = P %*% A - L %*% U |
| sup_norm = max(abs(diff)) |
| print(sup_norm) |
| if (sup_norm > eps) { |
| print("ERROR: LU decomposition does not reproduce original matrix") |
| } |
| } |
| |
| tmp = test_LU() |