blob: 307535e57884731bd9e9c0313eea55ac28927efc [file] [log] [blame]
#-------------------------------------------------------------
#
# 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()