def gauss_seidel(a: Dmatrix, b: Dvector, x0: Dvector, N: int = N):
k = 0
x = x0.copy()
xo = Dvector(1, N) # xo[1...N]
while True:
# xo <- x_k, x <- x_{k+1}
for i in range(1, N + 1):
xo[i] = x[i] # x_k に x_(k+1) を代入
# i=1 の処理
x[1] = (b[1] - sum(
(a[1][j] * xo[j] for j in range(2, N + 1)))) / a[1][1]
# i=2,3,...N の処理
for i in range(2, N + 1):
s = sum((a[i][j] * x[j] for j in range(1, i))) # i-1列までの和
t = sum((a[i][j] * xo[j] for j in range(i + 1, N + 1))) # i+1列以降の和
x[i] = (b[i] - s - t) / a[i][i]
for i in range(1, N + 1):
xo[i] = xo[i] - x[i]
eps = vector_norm_max(xo)
k += 1
if eps <= EPS or k >= KMAX:
break
if k == KMAX:
print("答えが見つかりませんでした")
exit(1)
else:
print(f"反復回数は{k}回です")
return x
def inverse_iteration(a: Dmatrix, a_qr: Dmatrix, eps: float) -> Dmatrix:
mu = 0.0
y = Dvector(1, N)
a_ii = a_qr.copy()
for i in range(1, N + 1):
lambda_ = a_ii[i][i] # 近似固有値の代入
y[i] = 1.0 # 初期値設定
# 行列の作成およびLU分解
lu = a.copy()
for k in range(1, N + 1):
lu[k][k] -= lambda_
lu, p = lu_decomp(lu, N) # LU分解
# 逆反復法
while True:
muo = mu
v = y.copy()
v = lu_solve(lu, v, p, N) # 固有ベクトルの計算
mu = inner_product(v, y) # 補正
v2s = sqrt(inner_product(v, v))
for j in range(1, N + 1):
y[j] = v[j] / v2s
if abs((mu - muo) / mu) < eps:
break
# 結果の代入(固有ベクトルはaのi列に)
for j in range(1, N + 1):
a_ii[j][i] = y[j]
return a_ii
def main():
print("データの個数を入力してください--->")
n = int(input())
n -= 1 # データ数が n なので, n <- n-1 として添字を0,1,...,n とする
print("補間点を入力してください--->")
xi = float(input())
x = Dvector(0, n) # x[0...n]
y = Dvector(0, n) # y[0...n]
# ファイルのオープン
with open("input_lag.dat", "r") as fin:
with open("output_lag.dat", "w") as fout:
input_vector(x, "x", fin, fout) # ベクトル x の入出力
input_vector(y, "y", fin, fout) # ベクトル y の入出力
print("補間の結果は, P({:.6f})={:.6f}".format(xi,
lagrange(x, y, 0, n, xi)))
# グラフを描くために結果をファイルに出力
xi = x[0]
while xi <= x[n]:
fout.write("{:.6f} \t {:.6f}\n".format(
xi, lagrange(x, y, 0, n, xi)))
xi += 0.01
def adams(y0: float, y: Dvector, a: float, b: float, n: int, N: int,
eps: float, f) -> Dvector:
y = Dvector(0, n) # y[0,1,...n] の確保
F = Dvector(0, 4) # F[0,1,...4] の確保
h = (b - a) / n # 刻み幅の設定
# スタータ
y = rk4(y0, y, a, b, n, f)
x = a
for i in range(4):
F[i] = f(x, y[i])
x += h
# 反復計算
for i in range(3, n):
# アダムス・バッシュフォース法
F[3] = f(x - h, y[i])
yp = y[i] + h * (55.0 * F[3] - 59.0 * F[2] + 37.0 * F[1] -
9.0 * F[0]) / 24.0
for j in range(1, N + 1):
# アダムス・ムルトン法
F[4] = f(x, yp)
y[i + 1] = y[i] + h * (9.0 * F[4] + 19.0 * F[3] - 5.0 * F[2] +
F[1]) / 24.0
if abs(y[i + 1] - yp) < eps:
break
yp = y[i + 1]
for j in range(1, 5):
F[j - 1] = F[j] # F[i] の更新
x += h
return y
def __init__(self, row_head_idx: int, row_last_idx: int, col_head_idx: int, col_last_idx: int):
matrix = Dvector(row_head_idx, row_last_idx)
for i in range(row_head_idx, row_last_idx+1):
matrix[i] = Dvector(col_head_idx, col_last_idx)
self.matrix = matrix
self.row_head_idx = row_head_idx
self.row_last_idx = row_last_idx
self.col_head_idx = col_head_idx
self.col_last_idx = col_last_idx
def main():
global N
# ベクトルの定義, 配列 a,b の添字は 1～N
a, b = Dvector(1, N), Dvector(1, N)
for i in range(1, N + 1):
a[i] = i / 20.0
b[i] = i / 10.0
print(f"a と b の内積は{inner_product(a, b)} です")
def householder(a: Dmatrix, n: int) -> Dmatrix:
u = Dvector(1, n)
f = Dvector(1, n)
g = Dvector(1, n)
a_hh = a.copy()
for k in range(1, n - 1):
# v の計算
for i in range(1, k + 1):
u[i] = 0.0
for i in range(k + 1, n + 1):
u[i] = a_hh[i][k]
# s の計算
ss = 0.0
for i in range(k + 2, n + 1):
ss += u[i] * u[i]
if abs(ss) <= 0.0: # 消去が必要ない場合の処理
continue
s = sqrt(ss + u[k + 1] * u[k + 1])
if u[k + 1] > 0.0:
s = -s
# uの計算
u[k + 1] -= s
uu = sqrt(ss + u[k + 1] * u[k + 1])
for i in range(k + 1, n + 1):
u[i] /= uu
# f, gの計算
for i in range(1, n + 1):
f[i], g[i] = 0.0, 0.0
for j in range(k + 1, n + 1):
f[i] += a_hh[i][j] * u[j]
g[i] += a_hh[j][i] * u[j]
# gammaの計算
gamma = 0.0
for j in range(1, n + 1):
gamma += u[j] * g[j]
# f, gの計算
for i in range(1, n + 1):
f[i] -= gamma * u[i]
g[i] -= gamma * u[i]
# A の計算
for i in range(1, n + 1):
for j in range(1, n + 1):
a_hh[i][j] -= 2.0 * u[i] * g[j] + 2.0 * f[i] * u[j]
return a_hh
def main():
global M, N
x = Dvector(1, M) # x[1...M]
y = Dvector(1, M) # y[1...M]
# ファイルのオープン
with open("input_func.dat", "r") as fin:
with open("output_func.dat", "w") as fout:
input_vector(x, 'x', fin, fout) # ベクトル x の入出力
input_vector(y, 'y', fin, fout) # ベクトル y の入出力
least_square(x, y, fout) # 最小2乗近似
def main():
a = 0.0
b = 3.1415926535897932
N = 2 # N変数
y = Dvector(1, N)
f = Dvector(1, N)
y[1], y[2] = 1.0, 0.0 # 初期値の設定
print("分割数を入力してください--->", end="")
n = int(input())
rk4_m(y, f, N, a, b, n, FUNC) # ルンゲ・クッタ法
def jacobi_lin(a: Dmatrix, b: Dvector, x: Dvector) -> Dvector:
k = 0
xn = Dvector(1, N) # xn[1...N]
# x <- x_k, xn <- x_{k+1}
while True:
for i in range(1, N+1):
xn[i] = b[i]
for j in range(1, N+1):
xn[i] -= a[i][j] * x[j]
xn[i] += a[i][i] * x[i] # 余分に引いた分を加える
xn[i] /= a[i][i]
for i in range(1, N+1):
x[i] = xn[i] - x[i]
eps = vector_norm_max(x) # 最大値ノルムの計算
for i in range(1, N+1):
x[i] = xn[i] # 値を更新
k += 1
if eps <= EPS or k >= KMAX:
break
if k == KMAX:
print("答えが見つかりませんでした")
else:
print(f"反復回数は{k}回です")
return x
def least_square(x: Dvector, y: Dvector, fout):
global M, N
p = Dmatrix(1, N + 1, 1, N + 1) # p[1...N+1][1...N+1]
a = Dvector(1, N + 1) # a[1...N-1]
# 右辺ベクトルの作成
for i in range(1, N + 2):
a[i] = 0.0
for j in range(1, M + 1):
a[i] += y[j] * (x[j]**(i - 1))
# 係数行列の作成
for i in range(1, N + 2):
for j in range(1, i + 1):
p[i][j] = 0.0
for k in range(1, M + 1):
p[i][j] += x[k]**(i + j - 2)
p[j][i] = p[i][j]
# 連立一次方程式を解く. 結果は a に上書き
a = gauss2(p, a, N + 1)
# 結果の出力
fout.write("最小2乗近似式は y=\n")
for i in range(N + 1, 0, -1):
fout.write("+ {:5.2f} x^{} ".format(a[i], i - 1))
fout.write("\n")
def main():
a = Dmatrix(1, N, 1, N) # 行列 a[1...N][1...N]
b = Dvector(1, N) # b[1...N]
x = Dvector(1, N) # x[1...N]
# ファイルのオープン
with open("input_sp.dat", "r") as fin:
with open("output_sp.dat", "w") as fout:
input_matrix( a, 'A', fin, fout ) # 行列 A の入出力
input_vector( b, 'b', fin, fout ) # ベクトル b の入出力
input_vector( x, 'x', fin, fout ) # 初期ベクトル x0 の入出力
x = jacobi_lin( a, b, x ) # ヤコビ法
# 結果の出力
fout.write("Ax=b の解は次の通りです\n")
for i in range(1, N+1):
fout.write("{:.6f}\n".format(x[i]))
def main():
global N
a = Dmatrix(1, N, 1, N) # 行列 a[1...N][1...N]
b = Dvector(1, N) # b[1...N]
with open("input.dat", "r") as fin:
with open("output.dat", "w") as fout:
input_matrix(a, 'A', fin, fout)
input_vector(b, 'b', fin, fout)
def matrix_vector_product(a: Dmatrix, b: Dvector):
N = b.last_idx - b.head_idx + 1
c = Dvector(1, N)
for i in range(1, N + 1):
c[i] = sum((a[i][j] * b[j] for j in range(1, N + 1)))
return c
def main():
a = Dmatrix(1, N, 1, N) # 行列 a[1...N][1...N]
b = Dvector(1, N) # b[1...N]
x0 = Dvector(1, N) # x[1...N]
# ファイルのオープン
with open("input_sp.dat", "r") as fin:
with open("output_sp.dat", "w") as fout:
input_matrix(a, 'A', fin, fout) # 行列 A の入出力
input_vector(b, 'b', fin, fout) # ベクトル b の入出力
input_vector(x0, 'x0', fin, fout) # 初期ベクトル x0 の入出力
x = gauss_seidel(a, b, x0) # ガウス・ザイデル法
# 結果の出力
fout.write("Ax=b の解は次の通りです\n")
for i in range(1, N + 1):
fout.write("{:.6f}\n".format(x[i]))
def cg(a: Dmatrix, b: Dvector, x0: Dvector):
k = 0
r = Dvector(1, N) # r[1...N]
p = Dvector(1, N) # p[1...N]
x = x0.copy()
tmp = matrix_vector_product(a, x) # tmp <- A b
for i in range(1, N + 1):
p[i] = b[i] - tmp[i]
r[i] = p[i]
while True:
# alpha の計算
tmp = matrix_vector_product(a, p) # tmp <- A p_k
work = inner_product(p, tmp) # work <- (p, Ap_k)
alpha = inner_product(p, r) / work
# x_{k+1} と r_{k+1} の計算
for i in range(1, N + 1):
x[i] += alpha * p[i]
for i in range(1, N + 1):
r[i] -= alpha * tmp[i]
# 収束判定
eps = vector_norm1(r)
k += 1 # 反復回数の更新
if eps < EPS:
break
# beta と p_{k+1} の計算
beta = -inner_product(r, tmp) / work
for i in range(1, N + 1):
p[i] = r[i] + beta * p[i]
if k >= KMAX:
break
if k == KMAX:
print("答えが見つかりませんでした")
exit(1)
else:
print(f"反復回数は{k}回です") # 反復回数を画面に表示
return x
def qr(a: Dmatrix, eps: float, n: int) -> Dmatrix:
# 領域の確保
q = Dmatrix(1, n, 1, n)
work = Dvector(1, n)
a_qr = a.copy()
m = n
while m > 1:
# 収束判定
if abs(a_qr[m][m-1]) < eps:
m = m - 1
continue
# 原点移動
s = a_qr[n][n]
if m == n: # m=n のときは原点移動なし
s = 0.0
for i in range(1, m+1):
a_qr[i][i] -= s
# QR法
for i in range(1, m+1):
for j in range(1, m+1):
q[i][j] = 0.0 # Q を m x m 単位行列で初期化
q[i][i] = 1.0
# R と Q の計算
for i in range(1, m):
r = sqrt( a_qr[i][i]*a_qr[i][i] + a_qr[i+1][i]*a_qr[i+1][i] )
if r == 0.0:
sint = 0.0
cost = 0.0
else:
sint = a_qr[i+1][i] / r
cost = a_qr[i][i] / r
for j in range(i+1, m+1):
tmp = a_qr[i][j]*cost + a_qr[i+1][j]*sint
a_qr[i+1][j] = -a_qr[i][j]*sint + a_qr[i+1][j]*cost
a_qr[i][j] = tmp
a_qr[i+1][i] = 0.0
a_qr[i][i] = r
for j in range(1, m+1): # Q は P の転置
tmp = q[j][i]*cost + q[j][i+1]*sint
q[j][i+1] = -q[j][i]*sint + q[j][i+1]*cost
q[j][i] = tmp
# RQ の計算
for i in range(1, m+1):
for j in range(i, m+1):
work[j] = a_qr[i][j]
for j in range(1, m+1):
a_qr[i][j] = sum( ( work[k] * q[k][j] for k in range(i, m+1) ) )
# 原点移動後の処理
for i in range(1, m+1):
a_qr[i][i] += s
return a_qr
def main():
a = Dmatrix(1, N, 1, N) # 行列領域の確保
x = Dvector(1, N) # ベクトル領域の確保
# ファイルのオープン
with open("input_eigen.dat", "r") as fin:
with open("result_eigen.dat", "w") as fout:
input_matrix( a, 'A', fin, fout ) # 行列 A の入出力
input_vector( x, 'x', fin, fout ) # ベクトル x の入出力
power_method( a, x, fout ) # べき乗法
def matrix_norm1(a: Dmatrix):
m1, m2 = a.row_head_idx, a.row_last_idx
n1, n2 = a.col_head_idx, a.col_last_idx
work = Dvector(n1, n2) # ベクトルwork[n1...n2]
# 列和の計算
for j in range(n1, n2 + 1):
work[j] = 0.0
for i in range(m1, m2 + 1):
work[j] += abs(a[i][j])
return max(work)
def matrix_norm_max(a: Dmatrix):
m1, m2 = a.row_head_idx, a.row_last_idx
n1, n2 = a.col_head_idx, a.col_last_idx
work = Dvector(m1, m2)
# 行和の計算
for i in range(m1, m2 + 1):
work[i] = 0.0
for j in range(n1, n2 + 1):
work[i] += abs(a[i][j])
return max(work)
def rk4_m(y: Dvector, f: Dvector, N: int, a: float, b: float, n: int, F):
k1 = Dvector(1, N)
k2 = Dvector(1, N)
k3 = Dvector(1, N)
k4 = Dvector(1, N)
tmp = Dvector(1, N)
# 初期値の設定・表示
h = (b - a) / n # 刻み幅
x = a
print("x={:.6f} \t y1={:6f} \t y2={:.6f} ".format(x, y[1], y[2]))
# ルンゲ・クッタ法(N 変数版)
for i in range(n):
# k1 の計算
FUNC(x, y, f)
for j in range(1, N + 1):
k1[j] = f[j]
for j in range(1, N + 1):
tmp[j] = y[j] + h * k1[j] / 2.0
# k2 の計算
FUNC(x + h / 2.0, tmp, f)
for j in range(1, N + 1):
k2[j] = f[j]
for j in range(1, N + 1):
tmp[j] = y[j] + h * k2[j] / 2.0
# k3 の計算
FUNC(x + h / 2.0, tmp, f)
for j in range(1, N + 1):
k3[j] = f[j]
for j in range(1, N + 1):
tmp[j] = y[j] + h * k3[j]
# k4 の計算
FUNC(x + h, tmp, f)
for j in range(1, N + 1):
k4[j] = f[j]
for j in range(1, N + 1):
y[j] = y[j] + h / 6.0 * (k1[j] + 2.0 * k2[j] + 2.0 * k3[j] + k4[j])
x += h
print("x={:.6f} \t y1={:.6f} \t y2={:.6f} ".format(x, y[1], y[2]))
def sor(a: Dmatrix, b: Dvector, x0: Dvector, omega: float, N: int = N):
k = 0
x = x0.copy()
xo = Dvector(1, N) # xo[1...N]
while True:
# xo <- x_k, x <- x_{k+1}
for i in range(1, N + 1):
xo[i] = x[i] # x_k に x_(k+1) を代入
# i=1 の処理
x[1] = (b[1] - sum(
(a[1][j] * xo[j] for j in range(2, N + 1)))) / a[1][1]
# i=2,3,...N の処理
for i in range(2, N + 1):
s = sum((a[i][j] * x[j] for j in range(1, i))) # i-1列までの和
t = sum((a[i][j] * xo[j] for j in range(i + 1, N + 1))) # i+1列以降の和
x[i] = (b[i] - s - t) / a[i][i]
# ここまではガウス・ザイデル法と同じ
# SOR法
for i in range(1, N + 1):
x[i] = xo[i] + omega * (x[i] - xo[i]) # 補正
for i in range(1, N + 1):
xo[i] = xo[i] - x[i]
eps = vector_norm_max(xo)
k += 1
if eps <= EPS or k >= KMAX:
break
if k == KMAX:
print("答えが見つかりませんでした")
exit(1)
else:
print(f"反復回数は{k}回です") # 反復回数を画面に表示
return x
def cholesky_solve(a_cd: Dmatrix, b: Dvector, N: int = N):
b_cs = b.copy()
# LDy = b
b_cs[1] = b[1] / a_cd[1][1]
for i in range(2, N + 1):
b_cs[i] = (b_cs[i] - sum((a_cd[j][j] * a_cd[i][j] * b_cs[j]
for j in range(1, i)))) / a_cd[i][i]
# L^t x = y
for i in range(N - 1, 0, -1):
b_cs[i] -= sum((a_cd[j][i] * b_cs[j] for j in range(i + 1, N + 1)))
return b_cs
def newton2(x: float, y: float, z: float):
global N
k = 0
d = Dvector(1, N) # d[1...N]
xk = Dvector(1, N) # xk[1...N]
J = Dmatrix(1, N, 1, N) # 行列 J[1...N][1...N]
xk[1] = x
xk[2] = y
xk[3] = z
while True:
# 右辺ベクトルの作成
d[1] = -f(xk[1], xk[2], xk[3])
d[2] = -g(xk[1], xk[2], xk[3])
d[3] = -h(xk[1], xk[2], xk[3])
# ヤコビ行列の作成
J[1][1] = f_x(xk[1], xk[2], xk[3])
J[1][2] = f_y(xk[1], xk[2], xk[3])
J[1][3] = f_z(xk[1], xk[2], xk[3])
J[2][1] = g_x(xk[1], xk[2], xk[3])
J[2][2] = g_y(xk[1], xk[2], xk[3])
J[2][3] = g_z(xk[1], xk[2], xk[3])
J[3][1] = h_x(xk[1], xk[2], xk[3])
J[3][2] = h_y(xk[1], xk[2], xk[3])
J[3][3] = h_z(xk[1], xk[2], xk[3])
d = gauss(J, d, N) # 連立一次方程式を解く
for i in range(1, N + 1):
xk[i] += d[i]
k += 1
if vector_norm1(d) <= EPS or k >= KMAX:
break
if k == KMAX:
print("答えが見つかりませんでした")
else:
print(f"答えは x={xk[1]}, y={xk[2]}, z={xk[3]} です")
def main():
a, b, y0 = 0.0, 1.0, 1.0
print("分割数を入力してください--->", end="")
n = int(input())
y = Dvector(0, n)
y = rk4(y0, y, a, b, n, func) # ルンゲクッタ法
# 結果の表示
h = (b - a) / n
for i in range(n + 1):
print("x={:.6f} \t y={:.6f} ".format(a + i * h, y[i]))
def main():
print("分割数を入力してください--->", end="")
n = int(input())
u = Dvector(1,n-1)
u_bvp = bvp( u, 0.0, 1.0, 0.0, 0.0, n, func )
h = 1.0 / n
print("求める答え u と誤差の最大値 e は次の通りです.")
for i in range(1,n):
print("u[{}]={:.6f}".format(i, u_bvp[i]))
for i in range(1,n):
u_bvp[i] -= exact(i*h)
print("e={:.6f}".format(vector_norm_max(u_bvp)))
def lu_solve(a: Dmatrix, b: Dvector, p: List[int], N:int=N) -> Dvector:
b_lu = b.copy() # 値渡し
# 右辺の行交換
for k in range(1, N):
b_lu[k], b_lu[p[k]] = b_lu[p[k]], b_lu[k]
# 前進代入
for i in range(k+1, N+1):
b_lu[i] += a[i][k] * b_lu[k]
# 後退代入
b_lu[N] /= a[N][N]
for k in range(N-1, 0, -1):
b_lu[k] = ( b_lu[k] - sum( (a[k][j] * b_lu[j] for j in range(k+1, N+1)) ) ) / a[k][k]
return b_lu
def main():
global N
a = Dmatrix(1, N, 1, N) # 行列 a[1...N][1...N]
b = Dvector(1, N) # b[1...N]
# ファイルのオープン
with open("input.dat", "r") as fin:
with open("output.dat", "w") as fout:
input_matrix( a, 'A', fin, fout ) # 行列 A の入出力
input_vector( b, 'b', fin, fout ) # ベクトル b の入出力
b = simple_gauss( a, b ) # ガウス消去法
# 結果の出力
fout.write("Ax=b の解は次の通りです\n")
for i in range(1, N+1):
fout.write("{:.6f}\n".format(b[i]))
def main():
a, b, y0 = 0.0, 1.0, 1.0
eps = 10.0**-8.0
N = 10 # 最大反復回数N
print("分割数を入力してください--->", end="")
n = int(input())
y = Dvector(0, n)
# アダムス法
y = adams(y0, y, a, b, n, N, eps, func)
# 結果の表示
h = (b - a) / n # 刻み幅
for i in range(n + 1):
print("x={:.6f} \t y={:.6f} ".format(a + i * h, y[i]))
def main():
global N
a = Dmatrix(1, N, 1, N)
b = Dvector(1, N)
# ファイルのオープン
with open("input.dat", "r") as fin:
with open("output.dat", "w") as fout:
input_matrix(a, 'A', fin, fout) # 行列 A の入出力
input_vector(b, 'b', fin, fout) # ベクトル b の入出力
b = gauss(a, b) # ガウス消去法
# 結果の出力
fout.write("Ax=b の解は次の通りです\n")
for i in range(1, N + 1):
fout.write(f"{b[i]}\n")
