# -*- coding: utf-8 -*-
# Program Name: lagrange_minimization_analytic_visual.py
# Creation Date: 20250111
# Overview: Symbolic and numerical minimization with plotting
# Usage: Run with `python lagrange_minimization_analytic_visual.py`

# --- Install required libraries / 必要なライブラリのインストール ---
# !pip install numpy matplotlib scipy sympy

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
import sympy as sp

# -----------------------------
# Problem (1) Analytic Solution
# x + 2y = 5, minimize x^2 + y^2

# SymPy symbolic variables / 記号変数定義
x, y, λ = sp.symbols('x y λ')

# Lagrangian / ラグランジュ関数
L = x**2 + y**2 - λ * (x + 2*y - 5)

# Stationary conditions / 停留条件
grad_L = [sp.diff(L, var) for var in (x, y, λ)]
solution_1 = sp.solve(grad_L, (x, y, λ))

x_val_1 = solution_1[x]
y_val_1 = solution_1[y]
min_val_1 = x_val_1**2 + y_val_1**2

print("\n--- Problem (1) Analytic Solution ---")
print(f"x = {sp.simplify(x_val_1)}")
print(f"y = {sp.simplify(y_val_1)}")
print(f"Minimum value = {sp.simplify(min_val_1)}")

# -----------------------------
# Problem (1) Geometric Visualization / 可視化
x_line = np.linspace(-10, 10, 400)
y_line = (5 - x_line) / 2

plt.plot(x_line, y_line, label="x + 2y = 5")
plt.scatter([float(x_val_1)], [float(y_val_1)], color="red", label="Minimum Point")
plt.title("Problem (1): Minimizing x^2 + y^2 with x + 2y = 5")
plt.xlabel("x")
plt.ylabel("y")
plt.axis("equal")
plt.legend()
plt.show()

# -----------------------------
# Problem (2) Analytic Solution
# x + y + 2z = 1, x - 3y + 4z = 1, minimize x^2 + y^2 + z^2

# Symbolic variables / 記号変数定義
x, y, z, λ1, λ2 = sp.symbols('x y z λ1 λ2')

# Lagrangian / ラグランジュ関数
L2 = x**2 + y**2 + z**2 - λ1 * (x + y + 2*z - 1) - λ2 * (x - 3*y + 4*z - 1)

# Stationary conditions / 停留条件
grad_L2 = [sp.diff(L2, var) for var in (x, y, z, λ1, λ2)]
solution_2 = sp.solve(grad_L2, (x, y, z, λ1, λ2))

x_val_2 = solution_2[x]
y_val_2 = solution_2[y]
z_val_2 = solution_2[z]
min_val_2 = x_val_2**2 + y_val_2**2 + z_val_2**2

print("\n--- Problem (2) Analytic Solution ---")
print(f"x = {sp.simplify(x_val_2)}")
print(f"y = {sp.simplify(y_val_2)}")
print(f"z = {sp.simplify(z_val_2)}")
print(f"Minimum value = {sp.simplify(min_val_2)}")

# -*- coding: utf-8 -*-
# Program Name: inequality_max_min_analytic.py
# Creation Date: 20250111
# Overview: Analytic solution using SymPy (fractional output)
# Usage: Run with `python inequality_max_min_analytic.py`

# --- Install required libraries ---
# !pip install sympy matplotlib numpy

import sympy as sp
import numpy as np
import matplotlib.pyplot as plt

# -----------------------------
# Problem (1) Analytic Solution
x, y = sp.symbols('x y', real=True, nonnegative=True)

# Constraint: 2x + y = 8
y_expr = 8 - 2 * x

# Objective: xy
xy_expr = x * y_expr

# Expand and differentiate
xy_derivative = sp.diff(xy_expr, x)

# Solve for critical points
critical_x = sp.solve(xy_derivative, x)
critical_y = [y_expr.subs(x, val) for val in critical_x]
critical_xy = [xy_expr.subs(x, val) for val in critical_x]

# Evaluate boundary at x = 0 and x = 4
boundary_points = [0, 4]
boundary_y = [y_expr.subs(x, val) for val in boundary_points]
boundary_xy = [xy_expr.subs(x, val) for val in boundary_points]

# Collect all candidates
candidates = list(zip(critical_x + boundary_points, critical_y + boundary_y, critical_xy + boundary_xy))

# Display Results
print("\n--- Problem (1) Analytic Candidates ---")
for idx, (xi, yi, xyi) in enumerate(candidates, 1):
    print(f"Candidate {idx}: x = {sp.simplify(xi)}, y = {sp.simplify(yi)}, xy = {sp.simplify(xyi)}")

# -----------------------------
# Problem (2) Analytic Solution
# Objective: x^2 y^2 + 4x^2 + y^2
g_expr = x**2 * y_expr**2 + 4 * x**2 + y_expr**2

# Differentiate and solve
g_derivative = sp.diff(g_expr, x)
critical_x_g = sp.solve(g_derivative, x)

# Evaluate candidates
critical_y_g = [y_expr.subs(x, val) for val in critical_x_g]
critical_g = [g_expr.subs(x, val) for val in critical_x_g]

# Evaluate boundary at x = 0 and x = 4
boundary_y_g = [y_expr.subs(x, val) for val in boundary_points]
boundary_g = [g_expr.subs(x, val) for val in boundary_points]

# Collect all candidates
candidates_g = list(zip(critical_x_g + boundary_points, critical_y_g + boundary_y_g, critical_g + boundary_g))

# Display Results
print("\n--- Problem (2) Analytic Candidates ---")
for idx, (xi, yi, gi) in enumerate(candidates_g, 1):
    print(f"Candidate {idx}: x = {sp.simplify(xi)}, y = {sp.simplify(yi)}, g(x,y) = {sp.simplify(gi)}")

# -----------------------------
# Optional Visualization
x_vals = np.linspace(0, 4, 100)
y_vals = 8 - 2 * x_vals
xy_vals = x_vals * y_vals
g_vals = x_vals**2 * y_vals**2 + 4 * x_vals**2 + y_vals**2

plt.plot(x_vals, xy_vals, label="xy = x(8 - 2x)")
plt.title("Problem (1) xy Plot")
plt.xlabel("x")
plt.ylabel("xy")
plt.legend()
plt.show()

plt.plot(x_vals, g_vals, label="g(x,y) = x^2 y^2 + 4x^2 + y^2")
plt.title("Problem (2) g(x,y) Plot")
plt.xlabel("x")
plt.ylabel("g(x,y)")
plt.legend()
plt.show()

# -*- coding: utf-8 -*-
# Program Name: symbolic_max_min_solver.py
# Creation Date: 20250111
# Overview: Solves maximum and minimum problems analytically using SymPy
# Usage: Run with `python symbolic_max_min_solver.py`

# --- Install required libraries ---
# !pip install sympy

import sympy as sp

# -----------------------------
# Problem (1) Analytic Solution
x, y, λ = sp.symbols('x y λ')

# Lagrangian definition / ラグランジュ関数定義
L1 = 2 * x + y**2 - λ * (2 * x**2 + y**2 - 2)

# Stationary conditions / 停留条件
grad_L1 = [sp.diff(L1, var) for var in (x, y, λ)]
solution1 = sp.solve(grad_L1, (x, y, λ), dict=True)

print("\n--- Problem (1) Analytic Solutions ---")
for sol in solution1:
    val = 2 * sol[x] + sol[y]**2
    print(f"x = {sp.simplify(sol[x])}, y = {sp.simplify(sol[y])}, Value = {sp.simplify(val)}")

# -----------------------------
# Problem (2) Analytic Solution
x, y, λ = sp.symbols('x y λ')

# Lagrangian definition / ラグランジュ関数定義
L2 = x**2 + 2 * y**2 + 6 * y - λ * (x**2 + y**2 + 2 * y - 3)

# Stationary conditions / 停留条件
grad_L2 = [sp.diff(L2, var) for var in (x, y, λ)]
solution2 = sp.solve(grad_L2, (x, y, λ), dict=True)

print("\n--- Problem (2) Analytic Solutions ---")
for sol in solution2:
    val = sol[x]**2 + 2 * sol[y]**2 + 6 * sol[y]
    print(f"x = {sp.simplify(sol[x])}, y = {sp.simplify(sol[y])}, Value = {sp.simplify(val)}")

# -*- coding: utf-8 -*-
# Program Name: unconstrained_and_bounded_optimization.py
# Creation Date: 20250111
# Overview: Analytically solve unconstrained and bounded optimization problems.
# Usage: Run with `python unconstrained_and_bounded_optimization.py`

# --- Install required libraries ---
# !pip install sympy scipy numpy matplotlib

import sympy as sp
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize_scalar

# -----------------------------
# Problem (1): Unconstrained Minimization / 無制約最小化
x, y = sp.symbols('x y')
f = x**2 + 2*x + y**2 - 4*y + 1

# Gradient and Stationary Point / 勾配と停留点
grad_f = [sp.diff(f, var) for var in (x, y)]
stationary_point = sp.solve(grad_f, (x, y), dict=True)

print("\n--- Problem (1) Unconstrained Minimization ---")
for sol in stationary_point:
    val = f.subs(sol)
    print(f"x = {sol[x]}, y = {sol[y]}, Minimum Value = {sp.simplify(val)}")

# -----------------------------
# Problem (2): Bounded Maximization and Minimization / 有界最適化
f_func = sp.lambdify((x, y), f, 'numpy')

x_bounds = np.linspace(1, 3, 100)
y_bounds = np.linspace(-1, 4, 100)
X, Y = np.meshgrid(x_bounds, y_bounds)
Z = f_func(X, Y)

max_val = np.max(Z)
min_val = np.min(Z)
max_loc = np.unravel_index(np.argmax(Z), Z.shape)
min_loc = np.unravel_index(np.argmin(Z), Z.shape)

print("\n--- Problem (2) Bounded Optimization ---")
print(f"Maximum Value: {max_val:.4f} at x = {X[max_loc]:.4f}, y = {Y[max_loc]:.4f}")
print(f"Minimum Value: {min_val:.4f} at x = {X[min_loc]:.4f}, y = {Y[min_loc]:.4f}")

plt.contourf(X, Y, Z, levels=50)
plt.colorbar()
plt.scatter([X[max_loc]], [Y[max_loc]], color='red', label="Maximum")
plt.scatter([X[min_loc]], [Y[min_loc]], color='blue', label="Minimum")
plt.title("Problem (2) Bounded Optimization")
plt.xlabel("x")
plt.ylabel("y")
plt.legend()
plt.show()

# -----------------------------
# Problem (3): Unconstrained Minimization / 無制約最小化
g = x**2 + 2*x*y + 2*y**2 + 4*x - 2*y + 1
grad_g = [sp.diff(g, var) for var in (x, y)]
stationary_point_g = sp.solve(grad_g, (x, y), dict=True)

print("\n--- Problem (3) Unconstrained Minimization ---")
for sol in stationary_point_g:
    val = g.subs(sol)
    print(f"x = {sp.simplify(sol[x])}, y = {sp.simplify(sol[y])}, Minimum Value = {sp.simplify(val)}")

# -*- coding: utf-8 -*-
# Program Name: constraint_optimization_examples.py
# Creation Date: 20250111
# Overview: Analytical solutions to constrained optimization problems using SymPy
# Usage: Run with `python constraint_optimization_examples.py`

# --- Install required libraries ---
# !pip install sympy

import sympy as sp

# -----------------------------
# Problem (1)
x, y, λ = sp.symbols('x y λ', positive=True)
L1 = 2 * x + y - λ * (x * y - 4)
grad_L1 = [sp.diff(L1, var) for var in (x, y, λ)]
solution1 = sp.solve(grad_L1, (x, y, λ), dict=True)

print("\n--- Problem (1) Solution ---")
for sol in solution1:
    val = 2 * sol[x] + sol[y]
    print(f"x = {sp.simplify(sol[x])}, y = {sp.simplify(sol[y])}, Minimum Value = {sp.simplify(val)}")

# -----------------------------
# Problem (2)
x, y, λ = sp.symbols('x y λ', positive=True)
L2 = x * y - λ * (2 * x + y - 4)
grad_L2 = [sp.diff(L2, var) for var in (x, y, λ)]
solution2 = sp.solve(grad_L2, (x, y, λ), dict=True)

print("\n--- Problem (2) Solution ---")
for sol in solution2:
    val = sol[x] * sol[y]
    print(f"x = {sp.simplify(sol[x])}, y = {sp.simplify(sol[y])}, Maximum Value = {sp.simplify(val)}")

# -----------------------------
# Problem (3)
x, y, z, λ = sp.symbols('x y z λ', positive=True)
L3 = x * y * z - λ * (x + y + z - 12)
grad_L3 = [sp.diff(L3, var) for var in (x, y, z, λ)]
solution3 = sp.solve(grad_L3, (x, y, z, λ), dict=True)

print("\n--- Problem (3) Solution ---")
for sol in solution3:
    val = sol[x] * sol[y] * sol[z]
    print(f"x = {sp.simplify(sol[x])}, y = {sp.simplify(sol[y])}, z = {sp.simplify(sol[z])}, Maximum Volume = {sp.simplify(val)}")

# -*- coding: utf-8 -*-
# Program Name: lagrange_optimization_symbolic.py
# Creation Date: 20250111
# Overview: Symbolic solutions for optimization problems with equality constraints
# Usage: Run with `python lagrange_optimization_symbolic.py`

# --- Install required libraries ---
# !pip install sympy

import sympy as sp

# -----------------------------
# Problem (1) Max/Min of 2x + y with x^2 + y^2 = 4
x, y, λ = sp.symbols('x y λ')
L1 = 2 * x + y - λ * (x**2 + y**2 - 4)
grad_L1 = [sp.diff(L1, var) for var in (x, y, λ)]
sol1 = sp.solve(grad_L1, (x, y, λ), dict=True)

print("\n--- Problem (1) Max/Min of 2x + y ---")
for s in sol1:
    val = 2 * s[x] + s[y]
    print(f"x = {sp.simplify(s[x])}, y = {sp.simplify(s[y])}, Value = {sp.simplify(val)}")

# -----------------------------
# Problem (2) Min of x^2 + 4y^2 with x + y = 5
x, y, λ = sp.symbols('x y λ')
L2 = x**2 + 4 * y**2 - λ * (x + y - 5)
grad_L2 = [sp.diff(L2, var) for var in (x, y, λ)]
sol2 = sp.solve(grad_L2, (x, y, λ), dict=True)

print("\n--- Problem (2) Min of x^2 + 4y^2 ---")
for s in sol2:
    val = s[x]**2 + 4 * s[y]**2
    print(f"x = {sp.simplify(s[x])}, y = {sp.simplify(s[y])}, Minimum Value = {sp.simplify(val)}")

# -----------------------------
# Problem (3) Min of x^2 + y^2 + z^2 with x + 2y + 3z = 7
x, y, z, λ = sp.symbols('x y z λ')
L3 = x**2 + y**2 + z**2 - λ * (x + 2 * y + 3 * z - 7)
grad_L3 = [sp.diff(L3, var) for var in (x, y, z, λ)]
sol3 = sp.solve(grad_L3, (x, y, z, λ), dict=True)

print("\n--- Problem (3) Min of x^2 + y^2 + z^2 ---")
for s in sol3:
    val = s[x]**2 + s[y]**2 + s[z]**2
    print(f"x = {sp.simplify(s[x])}, y = {sp.simplify(s[y])}, z = {sp.simplify(s[z])}, Minimum Value = {sp.simplify(val)}")

# -*- coding: utf-8 -*-
# Program Name: advanced_optimization_sympy.py
# Creation Date: 20250111
# Overview: Symbolic maximization and minimization with multiple constraints
# Usage: Run with `python advanced_optimization_sympy.py`

# --- Install required libraries ---
# !pip install sympy

import sympy as sp

# -----------------------------
# Problem (1)
x, y, λ = sp.symbols('x y λ')
L1 = x * y + x + y - λ * (x**2 - x * y + y**2 - 1)
grad_L1 = [sp.diff(L1, var) for var in (x, y, λ)]
sol1 = sp.solve(grad_L1, (x, y, λ), dict=True)

print("\n--- Problem (1) Max/Min of xy + x + y ---")
for s in sol1:
    val = s[x] * s[y] + s[x] + s[y]
    print(f"x = {sp.simplify(s[x])}, y = {sp.simplify(s[y])}, Value = {sp.simplify(val)}")

# -----------------------------
# Problem (2)
x, y, z, λ1, λ2 = sp.symbols('x y z λ1 λ2')
L2 = x**3 + y**3 + z**3 - λ1 * (x + y + z - 2) - λ2 * (x**2 + y**2 + z**2 - 2)
grad_L2 = [sp.diff(L2, var) for var in (x, y, z, λ1, λ2)]
sol2 = sp.solve(grad_L2, (x, y, z, λ1, λ2), dict=True)

print("\n--- Problem (2) Max/Min of x^3 + y^3 + z^3 ---")
for s in sol2:
    val = s[x]**3 + s[y]**3 + s[z]**3
    print(f"x = {sp.simplify(s[x])}, y = {sp.simplify(s[y])}, z = {sp.simplify(s[z])}, Value = {sp.simplify(val)}")

# -----------------------------
# Problem (3)
x, y, z, λ1, λ2 = sp.symbols('x y z λ1 λ2')
L3 = x * y * z - λ1 * (x + y + z - 6) - λ2 * (2 * (x * y + y * z + z * x) - 18)
grad_L3 = [sp.diff(L3, var) for var in (x, y, z, λ1, λ2)]
sol3 = sp.solve(grad_L3, (x, y, z, λ1, λ2), dict=True)

print("\n--- Problem (3) Max Volume with Fixed Perimeter and Surface Area ---")
for s in sol3:
    val = s[x] * s[y] * s[z]
    print(f"x = {sp.simplify(s[x])}, y = {sp.simplify(s[y])}, z = {sp.simplify(s[z])}, Volume = {sp.simplify(val)}")

# -*- coding: utf-8 -*-
# Program Name: inequality_constraints_optimization.py
# Creation Date: 20250111
# Overview: Numerical solution for inequality constrained optimization using scipy
# Usage: Run with `python inequality_constraints_optimization.py`

# --- Install required libraries ---
# !pip install numpy scipy

import numpy as np
from scipy.optimize import minimize

# -----------------------------
# 共通制約条件 / Common Constraints
def constraint1(vars):
    x, y = vars
    return x + 2 * y - 4  # x + 2y >= 4

def constraint2(vars):
    x, y = vars
    return x - 3 * y + 6  # x - 3y >= -6  <=> x - 3y + 6 >= 0

def constraint3(vars):
    x, y = vars
    return 12 - 3 * x - y  # 3x + y <= 12  <=> 12 - 3x - y >= 0

constraints = [{'type': 'ineq', 'fun': constraint1},
               {'type': 'ineq', 'fun': constraint2},
               {'type': 'ineq', 'fun': constraint3}]

# -----------------------------
# Utility function to run min/max
def optimize_objective(func, bounds=(-100, 100)):
    result_min = minimize(func, x0=np.array([0.0, 0.0]), constraints=constraints)
    result_max = minimize(lambda vars: -func(vars), x0=np.array([0.0, 0.0]), constraints=constraints)
    return result_min, result_max

# -----------------------------
# Problem (1) Max/Min of 2x + y
def objective1(vars):
    x, y = vars
    return 2 * x + y

min1, max1 = optimize_objective(objective1)

# -----------------------------
# Problem (2) Min/Max of x^2 + y^2
def objective2(vars):
    x, y = vars
    return x**2 + y**2

min2, max2 = optimize_objective(objective2)

# -----------------------------
# Problem (3) Min/Max of (y - 1) / (x + 1)
def objective3(vars):
    x, y = vars
    if x + 1 == 0:
        return np.inf
    return (y - 1) / (x + 1)

min3, max3 = optimize_objective(objective3)

# -----------------------------
# Result Display
def display_results(label, min_res, max_res):
    print(f"\n--- {label} ---")
    print(f"Minimum: {min_res.fun:.4f} at x = {min_res.x[0]:.4f}, y = {min_res.x[1]:.4f}")
    print(f"Maximum: {-max_res.fun:.4f} at x = {max_res.x[0]:.4f}, y = {max_res.x[1]:.4f}")

display_results("Problem (1) 2x + y", min1, max1)
display_results("Problem (2) x^2 + y^2", min2, max2)
display_results("Problem (3) (y - 1) / (x + 1)", min3, max3)

# -*- coding: utf-8 -*-
# Program Name: lagrange_quadratic_optimization.py
# Creation Date: 20250111
# Overview: Symbolic maximization and minimization with quadratic equality constraints
# Usage: Run with `python lagrange_quadratic_optimization.py`

# --- Install required libraries ---
# !pip install sympy

import sympy as sp

# -----------------------------
# Problem (1)
x, y, λ = sp.symbols('x y λ')
# Objective function / 目的関数
f1 = x**2 + 2 * x * y + 3 * y**2
# Constraint / 制約条件
constraint1 = x**2 + y**2 - 1
# Lagrangian / ラグランジュ関数
L1 = f1 - λ * constraint1

# Gradient and Stationary Conditions / 勾配と停留条件
grad_L1 = [sp.diff(L1, var) for var in (x, y, λ)]
sol1 = sp.solve(grad_L1, (x, y, λ), dict=True)

print("\n--- Problem (1) Max/Min of x^2 + 2xy + 3y^2 ---")
for s in sol1:
    val = f1.subs(s)
    print(f"x = {sp.simplify(s[x])}, y = {sp.simplify(s[y])}, Value = {sp.simplify(val)}")

# -----------------------------
# Problem (2)
x, y, λ = sp.symbols('x y λ')
# Objective function / 目的関数
f2 = 2 * x**2 + x * y + 4 * y**2
# Constraint / 制約条件
constraint2 = 4 * x**2 + 9 * y**2 - 36
# Lagrangian / ラグランジュ関数
L2 = f2 - λ * constraint2

# Gradient and Stationary Conditions / 勾配と停留条件
grad_L2 = [sp.diff(L2, var) for var in (x, y, λ)]
sol2 = sp.solve(grad_L2, (x, y, λ), dict=True)

print("\n--- Problem (2) Max/Min of 2x^2 + xy + 4y^2 ---")
for s in sol2:
    val = f2.subs(s)
    print(f"x = {sp.simplify(s[x])}, y = {sp.simplify(s[y])}, Value = {sp.simplify(val)}")

# -*- coding: utf-8 -*-
# Program Name: quadratic_fraction_optimization.py
# Creation Date: 20250111
# Overview: Solves equality and bounded optimization problems using SymPy and SciPy
# Usage: Run with `python quadratic_fraction_optimization.py`

# --- Install required libraries ---
# !pip install sympy numpy scipy

import sympy as sp
import numpy as np
from scipy.optimize import minimize

# -----------------------------
# Problem (1) Quadratic Constraint Optimization
x, y, λ = sp.symbols('x y λ')
# Objective / 目的関数
f1 = x**2 + y**2
# Constraint / 制約条件
constraint1 = 4 * x**2 - 8 * x * y + 10 * y**2 - 1
# Lagrangian / ラグランジュ関数
L1 = f1 - λ * constraint1

# Stationary Conditions / 勾配と停留条件
grad_L1 = [sp.diff(L1, var) for var in (x, y, λ)]
sol1 = sp.solve(grad_L1, (x, y, λ), dict=True)

print("\n--- Problem (1) Max/Min of x^2 + y^2 ---")
for s in sol1:
    val = f1.subs(s)
    print(f"x = {sp.simplify(s[x])}, y = {sp.simplify(s[y])}, Value = {sp.simplify(val)}")

# -----------------------------
# Problem (2) Bounded Fractional Optimization
def fractional_objective(vars):
    x, y = vars
    numerator = 2 * x * y
    denominator = 4 * x**2 + y**2
    if denominator == 0:
        return -np.inf
    return - numerator / denominator  # Negative for maximization

bounds = [(1, 2), (2, 4)]
initial_guess = [1.5, 3]

result = minimize(fractional_objective, initial_guess, bounds=bounds)

# Convert back to maximization result
max_value = -result.fun
x_opt, y_opt = result.x

print("\n--- Problem (2) Maximum of 2xy / (4x^2 + y^2) ---")
print(f"Maximum Value: {max_value:.4f} at x = {x_opt:.4f}, y = {y_opt:.4f}")

# -*- coding: utf-8 -*-
# Program Name: bounded_and_constrained_optimization.py
# Creation Date: 20250111
# Overview: Numerical solution of bounded and inequality constrained optimization problems
# Usage: Run with `python bounded_and_constrained_optimization.py`

# --- Install required libraries ---
# !pip install numpy scipy

import numpy as np
from scipy.optimize import minimize

# -----------------------------
# Problem (1)
def objective1(vars):
    x, y = vars
    return x * y + x - 2 * y + 1  # 正しいreturn文に修正

bounds1 = [(3, 4), (-2, 0)]
result_min1 = minimize(objective1, [3.5, -1], bounds=bounds1)
result_max1 = minimize(lambda v: -objective1(v), [3.5, -1], bounds=bounds1)

# -----------------------------
# Problem (2)
def objective2(vars):
    x, y = vars
    return x**2 - y**2

constraints2 = [
    {'type': 'ineq', 'fun': lambda v: v[0] + 2 * v[1] - 4},
    {'type': 'ineq', 'fun': lambda v: v[0] - 3 * v[1] + 6},
    {'type': 'ineq', 'fun': lambda v: 12 - 3 * v[0] - v[1]}
]

result_min2 = minimize(objective2, [1.0, 1.0], constraints=constraints2)
result_max2 = minimize(lambda v: -objective2(v), [1.0, 1.0], constraints=constraints2)

# -----------------------------
# Problem (3)
def objective3(vars):
    x, y, z = vars
    return x**2 + y**2 + z**2 - x * y - y * z - z * x

bounds3 = [(0, 1), (1, 2), (2, 3)]
result_min3 = minimize(objective3, [0.5, 1.5, 2.5], bounds=bounds3)

# -----------------------------
# Result Display Function
def display(label, result_min, result_max=None):
    print(f"\n--- {label} ---")
    print(f"Minimum: {result_min.fun:.4f} at x = {result_min.x}")
    if result_max:
        print(f"Maximum: {-result_max.fun:.4f} at x = {result_max.x}")

display("Problem (1) xy + x - 2y + 1", result_min1, result_max1)
display("Problem (2) x^2 - y^2", result_min2, result_max2)
display("Problem (3) x^2 + y^2 + z^2 - xy - yz - zx", result_min3)

# -*- coding: utf-8 -*-
# Program Name: lagrange_fraction_optimization.py
# Creation Date: 20250111
# Overview: Symbolic maximization and minimization with linear equality constraints
# Usage: Run with `python lagrange_fraction_optimization.py`

# --- Install required libraries ---
# !pip install sympy

import sympy as sp

# -----------------------------
# 共通設定
x, y, λ = sp.symbols('x y λ')
f = x**2 - 2 * x * y + 2 * y**2  # 目的関数

# 検討する等式制約のリスト
constraints = [
    x - 2 * y + 3,  # x - 2y = -3
    x - 2 * y - 4,  # x - 2y = 4
    2 * x - y - 3,  # 2x - y = 3
    2 * x - y - 6   # 2x - y = 6
]

# 結果格納
results = []

# 各等式制約について解析
for idx, constraint in enumerate(constraints, start=1):
    L = f - λ * constraint  # ラグランジュ関数
    grad_L = [sp.diff(L, var) for var in (x, y, λ)]
    sols = sp.solve(grad_L, (x, y, λ), dict=True)
    
    print(f"\n--- Constraint {idx}: {sp.simplify(constraint)} = 0 ---")
    for sol in sols:
        val = f.subs(sol)
        results.append((sol[x], sol[y], val))
        print(f"x = {sp.simplify(sol[x])}, y = {sp.simplify(sol[y])}, Value = {sp.simplify(val)}")