Linear Algebra python
Problem Statement :
The NumPy module also comes with a number of built-in routines for linear algebra calculations. These can be found in the sub-module linalg.
linalg.det
The linalg.det tool computes the determinant of an array.
print numpy.linalg.det([[1 , 2], [2, 1]]) #Output : -3.0
linalg.eig
The linalg.eig computes the eigenvalues and right eigenvectors of a square array.
vals, vecs = numpy.linalg.eig([[1 , 2], [2, 1]])
print vals #Output : [ 3. -1.]
print vecs #Output : [[ 0.70710678 -0.70710678]
# [ 0.70710678 0.70710678]]
linalg.inv
The linalg.inv tool computes the (multiplicative) inverse of a matrix.
print numpy.linalg.inv([[1 , 2], [2, 1]]) #Output : [[-0.33333333 0.66666667]
# [ 0.66666667 -0.33333333]]
Other routines can be found here
Task
You are given a square matrix A with dimensions N X N. Your task is to find the determinant. Note: Round the answer to 2 places after the decimal.
Input Format
The first line contains the integer N.
The next N lines contains the N space separated elements of array A.
Output Format
Print the determinant of A.
Solution :
Solution in C :
import numpy
N = int(input())
A = numpy.array([input().split() for _ in range(N)], float)
print(numpy.linalg.det(A))
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