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from scipy.optimize import linprog c = [50, 0.2, 50, 0.2, 50, 0.2, 4, 4, 4] A = [ [1, 0, -1, 0, 0, 0, 0, 0, 0], [0, 1, 0, -1, 0, 0, 0, 0, 0], [0, 0, 1 阅读全文
posted @ 2024-10-21 22:04
Tsuki*
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import numpy as np import cvxpy as cp n = 2 x = cp.Variable(n,integer = True) A1 = np.array([[0,5], [6,2], [1,1]]) b1 = np.array([15,24,5]) A2 = np.ar 阅读全文
posted @ 2024-10-21 21:51
Tsuki*
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import numpy as np from scipy.optimize import least_squares def f(x): return (np.abs(x + 1) - np.abs(x - 1)) / 2 + np.sin(x) def g(x): return (np.abs( 阅读全文
posted @ 2024-10-21 21:16
Tsuki*
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import numpy as np from scipy.optimize import fsolve def f(x): return (abs(x + 1) - abs(x - 1)) / 2 + np.sin(x) def g(x): return (abs(x + 3) - abs(x - 阅读全文
posted @ 2024-10-21 21:15
Tsuki*
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import numpy as np matrix = np.array([[-1, 1, 0], [-4, 3, 0], [1, 0, 2]]) eigenvalues, eigenvectors = np.linalg.eig(matrix) print("特征值:", eigenvalues) 阅读全文
posted @ 2024-10-21 21:15
Tsuki*
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from sympy import symbols, pi, integrate, sqrt y = symbols('y') curve1 = y - sqrt(4*y - y2) curve2 = sqrt(4 - y) volume = pi * integrate((curve12 - cu 阅读全文
posted @ 2024-10-21 21:14
Tsuki*
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import numpy as np A = np.zeros((1000, 1000)) np.fill_diagonal(A, 4) np.fill_diagonal(A[:, 1:], 1) np.fill_diagonal(A[1:, :], 1) b = np.arange(1, 1001 阅读全文
posted @ 2024-10-21 21:12
Tsuki*
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from sympy import symbols, Eq, solve x, y = symbols('x y') equations = (Eq(x**2 - y - x - 3, 0), Eq(x + 3*y - 2, 0)) symbolic_solution = solve(equatio 阅读全文
posted @ 2024-10-21 21:12
Tsuki*
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import numpy as np A = np.array([[4, 2, -1], [3, -1, 2], [11, 3, 0]]) b = np.array([2, 10, 8]) x = np.linalg.lstsq(A, b) if np.linalg.matrix_rank(A) = 阅读全文
posted @ 2024-10-21 21:09
Tsuki*
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import pandas as pd import numpy as np x = np.arange(0, 101, 1) y = np.arange(0, 101, 1) z = np.random.randint(0, 1001, size=(101, 101)) df = pd.DataF 阅读全文
posted @ 2024-10-21 21:08
Tsuki*
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