#!/usr/bin/env python import time import sys # import whrandom from matrix import * #from Numeric import * #import LinearAlgebra # # Eine 40x40 Matrix # S = Matrix([[6.95, -8.87, 5.93, -5.64, -1.83, 6.25, -4.98, 0.42, -3.87, -0.16, 3.32, 0.73, -4.73, 3.33, -5.41, 2.43, -0.38, -7.89, -4.49, 5.61, -7.16, 7.24, -8.12, -2.98, 9.69, -7.99, -5.51, -6.48, -7.71, -4.41, 2.2, -9.41, 5.33, 0.84, 0.39, 2.76, -7.48, -2.09, 4.71, -5.08], [-2.05, 8.69, 6.52, 5.4, -7.66, -9.45, -8.02, -9.04, 2.35, 9.48, 5.98, 6.68, -4.94, -1.9, 8.49, -9.1, -6.22, -7.11, -9.31, 7.4, 6.33, -4.78, 1.84, -2.1, 9.44, -9.6, 2.34, -7.39, -0.84, 1.87, -2.25, -4.77, -1.05, -6.29, -2.89, -3.29, -6.57, -0.77, -6.34, 0.4], [0.77, -2.87, -2.9, -3.01, -6.39, 0.27, -3.98, 0.04, -4.0, 2.02, -9.19, -5.17, 3.97, -5.73, 8.51, -1.53, -1.16, -1.71, 5.42, 0.41, 0.44, -8.9, -4.25, -1.41, -0.78, -2.2, -3.56, -0.67, -5.68, -2.24, 7.59, -7.37, -5.29, 8.3, 5.91, 6.41, 4.53, -1.26, -8.56, -9.97], [1.77, 5.56, -1.81, 7.25, -4.14, 6.0, 8.67, 6.65, -4.78, -7.16, -3.01, -4.29, 1.76, -5.83, -2.05, -2.22, 5.74, 5.51, -8.24, -3.83, 2.3, 5.67, 4.34, -5.36, -4.75, -2.48, -6.35, -9.2, -5.83, 6.59, 5.49, -1.55, 1.15, 2.03, -5.9, -8.7, 4.63, -9.61, -3.37, 5.84], [-3.82, 8.97, -0.74, -5.54, 3.96, -5.3, 4.53, 7.3, -8.18, -0.32, -1.64, 5.48, 6.45, -6.56, 2.9, -6.5, 9.93, 7.87, 0.81, -6.95, 9.5, -7.0, 1.6, -2.88, -5.36, 5.41, 2.23, 3.57, 7.48, 6.29, 4.25, 7.72, -2.65, -3.07, 3.95, 2.57, -4.99, 8.82, -2.99, 2.99], [3.61, 3.3, -2.36, 9.32, 0.69, 0.93, -2.68, 6.35, -7.77, -0.25, 3.23, -9.3, 6.81, -8.79, -6.17, 8.54, -1.44, 4.49, 1.1, 1.61, -0.7, -7.05, 6.94, -8.56, 4.23, -3.18, 1.66, -2.55, -8.41, 5.55, -1.27, -2.52, 4.0, -3.73, 1.86, -1.36, -9.8, -6.6, 2.12, -6.95], [-2.81, -0.16, 3.18, 8.86, 1.15, 5.51, -2.39, 3.18, 3.45, -5.81, 3.88, 8.36, -7.91, -9.03, 9.25, -8.37, 4.21, 5.47, 2.11, -0.78, -4.9, -7.05, 7.03, -0.4, -1.14, -2.06, -8.35, 0.76, -8.0, -1.99, -1.02, -3.4, -4.89, 4.03, 6.49, 6.79, 5.81, 5.92, 9.37, -8.93], [1.64, 3.66, -3.41, -1.04, 5.44, 1.13, -9.3, -1.93, -0.45, -0.06, -0.72, 3.11, -9.73, 9.83, 3.65, 9.16, -1.58, 6.53, -0.08, -5.25, -6.81, -1.08, -4.38, 7.34, 4.89, 3.2, -2.08, 6.48, -8.89, -3.75, 1.01, 7.55, -3.08, 4.75, 7.98, -6.64, -0.15, 9.51, -5.08, 7.76], [-5.33, -1.24, 7.0, 6.16, 7.72, 7.59, -3.52, 0.07, 5.84, 6.08, -5.48, -1.29, 2.28, 8.59, 4.75, 6.09, -9.14, 4.85, -3.14, -9.46, -2.21, -3.81, -9.58, 3.32, 8.17, -7.81, 8.14, -6.46, 3.66, -9.63, 1.68, -5.79, 4.4, 8.03, 5.6, 5.18, 5.79, 2.05, -8.67, -9.92], [9.36, -6.52, 3.71, 3.52, -4.8, 6.81, 9.02, -6.98, 7.47, -3.03, -0.08, -1.51, 9.22, -8.26, -7.65, -8.21, -4.52, 5.67, 4.26, 0.92, 4.12, -9.44, -9.32, 6.47, 2.59, 7.01, -4.12, 3.37, 1.52, 3.86, -2.36, -3.73, 4.1, -1.07, -4.7, -3.18, -0.34, 6.21, -4.26, -8.33], [-1.9, -7.58, -9.54, 9.35, 6.96, -3.09, -0.27, 8.84, 4.17, 6.91, 0.88, -4.46, -2.4, -0.9, 9.14, 6.44, -1.69, 8.83, 9.09, 6.92, -2.82, -5.88, 0.55, 8.94, -9.9, 4.13, -4.26, -8.87, 4.99, 6.57, -7.79, 9.36, 3.09, 8.25, 6.79, -0.33, -5.53, -4.5, 0.61, -9.74], [-5.67, -0.54, -9.51, -6.31, -0.54, -6.27, -6.31, 1.71, 1.39, -2.03, -0.21, 1.56, 6.06, 9.0, 3.16, -2.43, -0.41, -4.23, -0.75, -0.99, 8.8, 5.5, 1.12, 7.49, -7.95, -2.17, 1.44, 5.06, 6.59, 5.92, 3.6, 1.66, -6.39, 9.33, -9.3, 1.47, 4.04, 3.26, 2.06, -9.31], [-1.87, -2.7, 4.77, 0.28, -3.5, 8.52, -9.77, -3.35, -6.2, 0.5, 0.59, -4.12, -7.94, -5.5, 3.05, 0.95, 4.91, -2.68, 2.3, -3.27, 8.75, -4.59, -8.5, -2.97, 1.44, -7.53, -8.66, -0.7, 2.0, -0.38, -5.67, -0.26, 7.17, 7.21, 7.63, -8.56, -7.73, -2.72, 2.81, -6.15], [-0.92, -1.22, -6.41, 8.1, 7.94, 9.34, 8.78, 0.04, 1.8, 8.95, 0.69, -9.97, -2.8, -7.99, 8.6, 6.1, 9.56, 2.53, 7.15, -8.83, 1.77, -1.53, -3.62, -7.35, 4.25, -8.11, -6.52, 3.39, -3.93, 4.19, -9.19, -3.36, 0.69, -0.04, 7.6, -4.19, 2.71, 7.4, -8.23, -1.56], [1.11, -6.04, 2.09, 2.28, -5.05, -7.43, 1.04, -6.51, -3.83, -3.66, 1.96, -7.37, 0.81, 7.91, -7.7, -4.0, -8.27, 9.01, 4.36, -7.12, -8.44, -9.74, -1.48, 6.15, -7.25, 1.8, -9.9, 8.61, -7.28, 7.78, 1.44, 3.22, -1.47, -2.04, -3.76, -1.05, -6.92, -7.23, 2.74, 7.58], [1.38, 5.08, 6.61, -6.66, 8.51, 3.54, 7.59, 1.56, -0.87, -3.97, 4.84, 3.56, 8.62, -5.89, -2.78, -0.91, 8.75, -2.28, -0.53, 8.82, -7.86, -4.5, -8.15, -8.01, -4.05, 5.11, 0.74, -7.36, 4.73, -3.36, -8.69, -3.14, -9.4, -5.6, -7.0, 6.84, 2.0, 4.25, -3.96, 4.01], [-0.14, 5.91, -1.38, 6.79, 5.12, 4.18, 3.7, 5.07, 7.65, 5.87, -0.18, 9.79, 0.09, 3.02, 5.02, -9.48, -5.06, 6.06, -9.19, -1.47, 9.29, 2.09, -1.31, 9.86, 1.81, 7.66, -7.52, 2.49, -7.78, 4.38, -1.84, 9.57, 2.85, 7.88, -5.34, 4.69, 2.79, 0.33, 3.31, 1.11], [8.5, -7.12, -5.6, 5.03, 8.46, 1.77, -1.6, 4.62, -7.42, -8.14, 5.84, -5.52, -9.45, 3.87, 6.26, 0.01, -2.14, -9.94, -0.92, 8.87, 1.06, -0.91, 3.05, 1.18, 3.81, -6.83, 9.43, -0.37, -2.19, 9.68, 9.16, 1.57, 0.97, 8.36, -4.04, 5.03, -6.8, 8.17, 8.0, -8.51], [-4.18, 2.64, -3.59, 3.69, -7.07, -3.59, -0.13, 9.73, 3.58, 9.13, -2.75, 4.62, 2.41, 8.25, 9.49, 5.9, -4.06, 4.08, -4.49, 3.45, -4.25, 1.72, -1.25, 8.91, 2.54, -2.97, -5.23, -2.18, 2.32, -1.45, -9.37, 0.95, -5.08, 3.8, 8.96, -4.44, 7.89, 7.55, -4.24, -9.92], [3.19, 6.14, -4.44, -2.11, 1.33, -8.98, 8.85, 0.49, -8.98, -6.61, -5.7, 7.67, 6.96, 9.85, 1.63, -2.15, 5.17, 8.89, 2.71, -5.58, -3.66, -9.55, 7.65, 7.15, -8.91, 8.48, 7.39, -6.22, 9.61, -3.25, -6.36, 0.5, 8.25, -1.05, 3.6, -1.59, -9.84, -4.16, 9.36, -8.06], [-6.98, -3.42, -5.55, -1.98, -2.23, 5.53, -8.43, 8.42, 6.39, -7.95, 7.11, -3.39, -2.65, -7.95, 0.96, -0.58, 9.62, -0.91, 2.25, -1.17, 6.03, 4.1, -1.7, -0.67, -1.17, -6.46, -9.48, 0.8, -0.52, -2.26, 4.26, -8.44, -5.4, -6.98, -9.25, -2.61, 5.77, 3.32, 4.82, 7.88], [5.67, -9.97, 8.81, 1.8, -3.69, 6.81, 5.14, 1.38, -0.99, 6.95, 3.29, 2.49, -6.51, 6.81, -8.02, -7.72, -1.42, -5.07, -0.73, -0.75, 0.19, -5.32, -7.92, -1.03, 4.08, 5.8, 3.8, 0.44, 7.51, -5.79, -5.29, 3.84, 4.87, 9.2, -9.43, 0.44, 2.05, -5.24, 1.33, 8.59], [-9.64, 8.89, 5.93, -3.4, 0.46, -7.26, -0.85, 4.2, 9.76, 8.26, -4.4, -5.47, 0.94, 6.55, -5.34, -8.2, 9.01, 6.49, 4.83, 6.83, -2.41, 6.14, -1.26, -7.46, 8.44, 9.16, -7.67, 0.95, 5.42, 8.49, -6.86, 2.05, -1.65, -8.16, 5.22, -1.54, -8.84, 4.56, -1.89, 5.05], [-0.24, 6.65, -6.13, 9.24, -8.18, 1.64, 5.64, -4.01, -1.78, 2.68, 8.45, -9.24, 2.83, 0.47, 5.58, 9.76, 4.99, 0.55, -2.86, 5.2, 3.04, 8.41, -7.62, 5.37, -1.1, -3.04, 9.63, -0.89, 9.25, 9.54, 2.47, 5.7, -9.18, -6.6, -6.35, -4.15, -9.52, -3.39, -2.5, -4.13], [-3.05, -3.87, -1.88, 7.63, -0.52, -8.09, 4.43, 2.13, 5.49, -3.72, -5.38, -8.89, 3.36, 7.83, -8.85, -9.47, -1.4, 1.67, 7.69, -1.23, 3.72, 4.2, 9.3, 1.0, 9.27, -5.26, 3.93, -2.79, 8.05, -3.05, -8.0, -8.4, -1.17, 6.48, -3.88, -0.48, 9.87, -6.68, -2.46, -6.95], [3.0, 7.02, -1.4, 8.15, 4.25, -4.76, -9.92, -0.19, -1.06, 9.74, -3.06, -1.07, 6.49, 8.13, 7.44, 0.72, 2.28, 3.28, 7.69, 0.26, 1.98, 9.8, 8.36, 7.51, 3.73, 2.2, -8.63, -0.53, 8.84, -0.34, 0.94, -1.99, 9.68, -0.21, -6.59, 9.26, -5.45, -5.63, -3.63, -0.66], [9.63, 3.22, -4.09, 7.08, 9.25, -1.75, -6.35, -7.59, 3.81, -3.0, 3.14, -0.34, -3.53, 5.64, -7.18, -6.96, -1.31, -4.62, 1.57, -6.25, 3.3, -9.34, 8.4, 6.55, -2.9, -4.09, 4.55, -7.9, 0.71, -4.62, 3.62, -2.4, -0.98, -4.46, 2.65, -3.05, -7.81, -8.06, 2.04, -1.21], [1.19, -7.54, 5.99, 4.29, -1.65, -2.13, -6.89, 1.63, 3.4, 4.96, -6.84, 7.95, 3.48, -4.22, -7.38, -4.25, 6.17, -0.36, 0.4, 2.26, 7.68, -4.61, 1.16, 5.73, 4.63, 0.9, 9.47, -2.02, 6.24, -1.33, -3.67, 0.3, 9.7, 6.38, -4.16, 3.84, 5.02, 3.23, 2.93, 8.25], [4.05, -2.91, 2.05, -3.43, 4.22, 9.09, -3.9, -6.46, -0.05, -2.85, -3.75, 5.58, -1.94, -7.73, -4.97, -7.35, 1.84, -9.26, -1.39, 7.03, -4.33, 6.72, 1.95, -4.27, 9.46, -0.84, -6.62, 5.05, -5.53, 7.51, 0.57, 5.07, 1.14, 2.22, 6.49, 7.09, -0.67, -7.54, -7.52, 0.23], [-0.8, 2.03, 4.57, 0.85, -0.49, -0.59, 9.26, -8.7, -1.23, -0.57, -4.95, 9.88, 6.01, -3.13, -4.72, 2.36, 7.76, 8.36, -7.81, 6.28, 7.96, -8.82, -6.43, -1.36, -2.19, 0.95, -4.32, 9.69, 5.33, 3.21, -3.53, -7.72, -6.1, 3.33, -8.1, -9.44, -0.06, -2.84, -3.76, -0.3], [-0.27, -4.27, -1.48, -8.68, 8.17, -9.07, 1.0, 3.05, -3.77, 3.35, -9.05, 0.61, 2.71, -2.68, 2.91, -0.81, -4.54, -5.66, 6.78, 2.82, 9.25, 7.7, -6.87, 0.18, -3.36, -1.36, -2.95, -3.21, 6.46, 3.47, 2.14, 6.03, -6.81, -7.52, 8.05, -4.41, 2.64, 4.89, -7.52, 5.73], [8.36, 1.41, -1.65, -4.12, -7.26, -9.89, 2.85, 6.56, 1.32, -6.62, -5.96, -8.14, 5.23, 5.91, 3.67, -6.51, -5.86, 5.0, -9.72, -6.93, -4.44, -2.94, -7.8, -2.63, -5.49, -1.74, -8.73, -2.09, 5.14, -3.01, -1.03, -7.7, -1.43, -5.7, -9.1, 9.54, -4.96, -5.56, 3.12, 4.77], [1.89, -9.34, 2.12, 1.63, 2.43, -7.34, -4.87, -1.2, 0.73, 5.47, 1.01, 9.01, 4.3, 3.35, 6.36, 5.82, -8.04, -9.03, -2.54, -3.35, 8.27, 9.3, -3.74, -9.78, -8.36, 1.39, 5.72, 7.56, 7.02, 1.39, 6.85, -4.34, 1.81, -8.29, -8.93, -1.55, -1.44, -2.94, 9.39, 3.67], [-5.23, 9.21, -1.15, -0.46, 4.12, -7.17, 1.12, -7.88, 6.56, -3.25, -7.47, 3.03, -9.86, 8.35, -2.19, -9.98, -9.31, -6.76, -9.34, 1.3, -0.42, 8.83, -7.95, -6.83, 3.34, -2.05, -5.7, 2.43, -9.3, -4.24, 7.94, -9.12, 4.59, 0.12, 8.96, -8.34, -3.4, -5.11, 8.23, -1.49], [6.38, -9.92, -2.36, 4.07, -9.05, 7.72, -0.15, 5.27, -2.94, 9.09, -2.3, -3.82, 4.98, -6.31, 8.77, -9.78, 6.97, 5.29, 1.57, -3.44, -2.53, 4.92, 0.56, 2.55, 0.53, 4.21, 9.68, 0.97, -4.23, -1.64, 0.43, -9.1, -2.89, 1.56, 4.35, 0.24, -2.46, -8.13, -8.31, -0.95], [-7.63, -2.73, 5.58, 0.8, 0.82, -1.03, -4.67, -0.72, -9.13, 4.52, -1.89, 8.16, -0.19, 0.86, -7.41, -2.68, 1.36, -7.37, 5.83, -7.26, 1.15, 0.45, -8.04, 5.65, -3.21, 8.46, -0.87, -3.65, -5.97, -1.83, -3.93, 5.7, -0.88, -7.58, 6.91, -6.22, 5.93, -8.66, -1.37, 0.41], [-4.23, -2.35, -8.84, -2.97, -7.63, -1.25, 9.86, -0.89, -0.15, 5.24, -5.22, -8.25, 2.67, -1.34, 4.48, 4.15, 9.48, -9.43, 8.74, -1.32, -9.59, -6.16, -4.88, -9.61, 1.38, 5.81, -9.49, 5.99, -7.64, 9.22, -6.08, -6.16, 7.23, -8.44, -5.41, 3.58, 7.5, 8.74, 1.57, 0.31], [1.5, 1.39, -6.88, 0.5, -7.76, -5.61, -3.64, 8.94, 3.48, -6.16, 4.07, -8.55, 1.06, 8.0, -3.32, -2.13, -1.8, 8.6, -4.57, 3.89, 5.71, -4.53, -2.33, 7.14, -4.82, 4.69, -3.51, 8.76, 9.5, -8.68, 4.21, -9.93, 9.84, -4.46, 3.1, -9.07, 9.22, -6.53, 6.53, 2.73], [8.31, -4.84, -7.89, 1.16, 8.99, -1.92, 5.83, 0.01, -7.2, 5.57, 0.76, 6.36, -8.5, -5.27, 6.05, -9.64, -4.39, 8.89, -2.89, 6.42, 6.34, 2.4, 4.62, 8.44, -6.86, 9.87, 8.6, -0.54, -7.77, -2.53, 0.05, 0.34, 7.89, -3.9, 2.8, -8.93, -7.1, 0.24, 0.37, 3.72], [-9.13, 8.97, -9.61, 2.86, 8.5, 9.21, -4.06, -2.69, 3.52, 6.79, 8.88, 8.6, -0.63, 5.97, -1.84, -7.79, 8.62, -1.92, 8.15, -9.76, 6.11, -3.54, 9.13, -7.79, 6.68, 9.48, 2.41, 9.95, 6.52, -3.69, 9.96, -5.09, -0.38, 6.33, 8.8, -8.76, 1.91, 0.33, -2.94, 4.56]]) # # Ein Vektor mit 40 Einträgen # t = Matrix([[1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10]]) print "Starte ..." temp_time=time.time() invs = S.inverse_swap() print "Invertieren der Matrix Swap ---> ",time.time()-temp_time temp_time=time.time() invs = S.inverse_gauss() print "Invertieren der Matrix Gauss ---> ",time.time()-temp_time #temp_time=time.time() #InvS = LinearAlgebra.inverse(array(S.matrix)) #print "Invertieren der Matrix NumPy ---> ",time.time()-temp_time temp_time=time.time() v = S.solve_by_gauss(t) print "Lösen des Gleichungssystems mit Gauss ---> ",time.time()-temp_time #temp_time=time.time() #v = LinearAlgebra.solve_linear_equations(array(S.matrix), array(t.transpose().matrix)) #print "Lösen des Gleichungssystems mit NumPy ---> ",time.time()-temp_time temp_time=time.time() Pot4 = S**4 print "Die Matrix hoch 4 ---> ",time.time()-temp_time # Python 2.1.3, AMD AthlonXP 1800+ 1533MHz, Linux 2.4.20 # # Invertieren der Matrix Swap ---> 0.984109044075 # Invertieren der Matrix Gauss ---> 0.702221989632 # Lösen des Gleichungssystems mit Gauss ---> 0.135791063309 # Die Matrix hoch 4 ---> 1.60789108276 # Python 2.1.3, Intel Celeron 1.7GHz, Linux 2.4.20 # # Invertieren der Matrix Swap ---> 1.28098595142 # Invertieren der Matrix Gauss ---> 0.920455932617 # Lösen des Gleichungssystems mit Gauss ---> 0.179495930672 # Die Matrix hoch 4 ---> 2.10689294338 # Python 2.1.3, TI UltraSparc IIi, Linux 2.4.19 # # Invertieren der Matrix Swap ---> 7.05080699921 # Invertieren der Matrix Gauss ---> 7.85198402405 # Lösen des Gleichungssystems mit Gauss ---> 1.0110449791 # Die Matrix hoch 4 ---> 11.6615979671 # Python 1.5.2, AMD K6-2+ 350 (buggy Chipset), Linux 2.4.0 # # Invertieren der Matrix Swap ---> 18.1176300049 # Invertieren der Matrix Gauss ---> 13.0604690313 # Lösen des Gleichungssystems mit Gauss ---> 2.49682307243 # Die Matrix hoch 4 ---> 30.2569999695 # Python 1.5.2, AMD K6-2/MMX 350, Linux 2.4.0 # # Invertieren der Matrix Swap ---> 4.76227402687 # Invertieren der Matrix Gauss ---> 3.42340004444 # Lösen des Gleichungssystems mit Gauss ---> 0.665073990822 # Die Matrix hoch 4 ---> 7.94247198105 # Python 1.5.2, AMD K6-2/MMX 350, Linux 2.2.14 (buggy settings) # # Invertieren der Matrix Swap ---> 4.13347196579 # Invertieren der Matrix Gauss ---> 2.82970297337 # Lösen des Gleichungssystems mit Gauss ---> 0.558990955353 # Die Matrix hoch 4 ---> 6.31180596352 # Python 1.5.2, AMD K6/MMX 300, Linux 2.2.9, egcs 1.1.2 # # Invertieren der Matrix Swap ---> 4.74252104759 # Invertieren der Matrix Gauss ---> 3.40161705017 # Invertieren der Matrix NumPy ---> 0.123339891434 # Lösen des Gleichungssystems mit Gauss ---> 0.656967997551 # Lösen des Gleichungssystems mit NumPy ---> 0.0150020122528 # Die Matrix hoch 4 ---> 7.50581800938 # Python 1.5.2, AMD K6/MMX 300, Linux 2.2.5, egcs 1.1.2 # # Invertieren der Matrix Swap ---> 4.9821549654 # Invertieren der Matrix Gauss ---> 3.44473195076 # Invertieren der Matrix NumPy ---> 0.087623000145 # Lösen des Gleichungssystems mit Gauss ---> 0.67236995697 # Lösen des Gleichungssystems mit NumPy ---> 0.0165759325027 # Die Matrix hoch 4 ---> 8.01982009411 # Python 1.5.1, Cyrix 6x86P166+, Linux 2.0.35, pgcc1.1a # # Invertieren der Matrix Swap ---> 14.9848210812 # Invertieren der Matrix Gauss ---> 10.7640630007 # Lösen des Gleichungssystems mit Gauss ---> 2.14224302769 # Die Matrix hoch 4 ---> 24.837067008 # Python 1.5.1, Cyrix 6x86P166+, Linux 2.0.35, pgcc1.0.3a # # Invertieren der Matrix Swap ---> 15.9372220039 # Invertieren der Matrix Gauss ---> 12.2701330185 # Lösen des Gleichungssystems mit Gauss ---> 2.26797509193 # Die Matrix hoch 4 ---> 27.3394620419 # Python 1.5.1, Cyrix 6x86P166+, Linux 2.0.35, egcs1.0.2 # # Invertieren der Matrix Swap ---> 16.7838639021 # Invertieren der Matrix Gauss ---> 12.4332569838 # Lösen des Gleichungssystems mit Gauss ---> 2.33447802067 # Die Matrix hoch 4 ---> 29.1540719271 # Python 1.5b2, Cyrix 6x86P166+, Linux 2.0.32 # # Invertieren der Matrix ---> 15.888874054 # Lösen des Gleichungssystems mit Gauss ---> 2.22598099709 # Die Matrix hoch 4 ---> 27.0752999783 # Python 1.4, Cyrix 6x86P166+, Linux 2.0.32 # # Invertieren der Matrix ---> 23.226432085 # Lösen des Gleichungssystem mit Gauss ---> 3.15597999096 # Die Matrix hoch 4 ---> 37.4428179264