Metode Numerik soal 21 - Jaringan pipa
Revision as of 14:14, 30 September 2019 by IlliyyinLafi (talk | contribs)
Pada tugas hiburan ini
dikerjakan menggunakan hukum kontinuitas massa dimana masa yang masuk akan sama dengan yang dikeluarkan sehingga didapati rumus Q*p=Q*p sehingga akan mendapatakan 4 persamaan dengan 4 variabel
6C1 - 4C2 = 50
-2C1 - 1C3 + 4C4 = 50
7C2 - 3C3 - 4C4 = 0
-4C1 + 4C3 = 0
6C1 - 4C2 + 0C3 + 0C4 = 50
-2C1 + 0C2 - 1C3 + 4C4 = 50
0C1 + 7C2 - 3C3 - 4C4 = 0
-4C1 + 0C2 + 4C3 + 0C4 = 0
yang kemudian dijadikan dalam modue python lalu didapatkan nilai C1 = 275/9 , C2 = 100/3 , C3 = 275/9, C4 = 425/12
import numpy as np
class GEPP():
def __init__(self, A, b, doPricing=True):
#super(GEPP, self).__init__()
self.A = A # input: A is an n x n numpy matrix
self.b = b # b is an n x 1 numpy array
self.doPricing = doPricing
self.n = None # n is the length of A
self.x = None # x is the solution of Ax=b
self._validate_input() # method that validates input
self._elimination() # method that conducts elimination
self._backsub() # method that conducts back-substitution
def _validate_input(self):
self.n = len(self.A)
if self.b.size != self.n:
raise ValueError("Invalid argument: incompatible sizes between" +
"A & b.", self.b.size, self.n)
def _elimination(self):
"""
k represents the current pivot row. Since GE traverses the matrix in the
upper right triangle, we also use k for indicating the k-th diagonal
column index.
:return
"""
# Elimination
for k in range(self.n - 1):
if self.doPricing:
# Pivot
maxindex = abs(self.A[k:, k]).argmax() + k
if self.A[maxindex, k] == 0:
raise ValueError("Matrix is singular.")
# Swap
if maxindex != k:
self.Ak, maxindex = self.Amaxindex, k
self.bk, maxindex = self.bmaxindex, k
else:
if self.A[k, k] == 0:
raise ValueError("Pivot element is zero. Try setting doPricing to True.")
# Eliminate
for row in range(k + 1, self.n):
multiplier = self.A[row, k] / self.A[k, k]
self.A[row, k:] = self.A[row, k:] - multiplier * self.A[k, k:]
self.b[row] = self.b[row] - multiplier * self.b[k]
def _backsub(self):
# Back Substitution
self.x = np.zeros(self.n)
for k in range(self.n - 1, -1, -1):
self.x[k] = (self.b[k] - np.dot(self.A[k, k + 1:], self.x[k + 1:])) / self.A[k, k]
def main():
A = np.array([[6., -4., 0., 0.],
[-4., 0., 4., 0.],
[-2., 0., -1., 4.],
[0., 7., -3., -4.]])
b = np.array([[50.],
[0.],
[50.],
[0.]])
print("ini matriks awal nya")
print(A)
print("ini hasil yang awal")
print(b)
GaussElimPiv = GEPP(np.copy(A), np.copy(b), doPricing=False)
print("ini hasil akhirnya")
print(GaussElimPiv.x)
print(GaussElimPiv.A)
print(GaussElimPiv.b)
GaussElimPiv = GEPP(A, b)
print(GaussElimPiv.x)
if __name__ == "__main__":
main()
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