এসসিপি

from scipy import optimize

result = optimize.minimize(
    lambda x: (x[0] - 3) ** 2 + (x[1] - 2) ** 2,
    [0, 0]
)

print(result.x)

from scipy import optimize

result = optimize.minimize_scalar(
    lambda x: (x - 5) ** 2 + 3
)

print(result.x)
print(result.fun)

from scipy import optimize

def equation(x):
    return [
        x[0] + x[1] - 5,
        x[0] - x[1] - 1
    ]

result = optimize.least_squares(
    equation,
    [0, 0]
)

print(result.x)

from scipy import optimize

x = [1, 2, 3, 4]
y = [3, 5, 7, 9]

def equation(x, a, b):
    return a * x + b

result, covariance = optimize.curve_fit(
    equation,
    x,
    y
)

print(result)

from scipy import optimize

def equation(x):
    return [
        x[0] ** 2 + x[1] - 5,
        x[0] + x[1] ** 2 - 5
    ]

result = optimize.root(
    equation,
    [1, 1]
)

print(result.x)

from scipy import optimize

result = optimize.root_scalar(
    lambda x: x ** 2 - 9,
    bracket=[0, 5]
)

print(result.root)

from scipy import optimize

result = optimize.brentq(
    lambda x: x ** 2 - 9,
    0,
    5
)

print(result)

from scipy import optimize

result = optimize.brenth(
    lambda x: x ** 2 - 16,
    0,
    6
)

print(result)

from scipy import optimize

result = optimize.bisect(
    lambda x: x ** 2 - 25,
    0,
    10
)

print(result)

from scipy import optimize

result = optimize.newton(
    lambda x: x ** 2 - 16,
    3
)

print(result)

from scipy import optimize

def equation(x):
    return [
        x[0] ** 2 + x[1] - 5,
        x[0] + x[1] ** 2 - 5
    ]

result = optimize.fsolve(
    equation,
    [1, 1]
)

print(result)

from scipy import optimize

result = optimize.differential_evolution(
    lambda x: (
        (x[0] - 2) ** 2 +
        (x[1] - 3) ** 2
    ),
    [
        (-10, 10),
        (-10, 10)
    ]
)

print(result.x)
print(result.fun)

from scipy import optimize

result = optimize.dual_annealing(
    lambda x: (
        (x[0] - 4) ** 2 +
        (x[1] - 1) ** 2
    ),
    [
        (-10, 10),
        (-10, 10)
    ]
)

print(result.x)
print(result.fun)

from scipy import optimize

result = optimize.shgo(
    lambda x: (
        x[0] ** 2 +
        x[1] ** 2
    ),
    [
        (-5, 5),
        (-5, 5)
    ]
)

print(result.x)
print(result.fun)

from scipy import optimize

result = optimize.basinhopping(
    lambda x: (
        (x[0] - 2) ** 2 +
        (x[1] - 4) ** 2
    ),
    [0, 0],
    niter=20
)

print(result.x)
print(result.fun)