"""Create reasoning_engine
Generated by Phi-Octopus Eden
2025-11-08 04:13:40.762413
"""

```python
class ReasoningEngine:
    """
    A simple reasoning engine that solves a basic constrained optimization problem.
    
    The engine is designed to find the maximum value of a linear function under given constraints.

    :param objective: List of coefficients for the objective function (e.g., [3, 5])
    :type objective: List[int]
    :param constraints: List of tuples representing inequalities in the form (coefficient, right-hand-side)
                        e.g., [(2, 10), (-1, 8)] means 2*x - y <= 10 and -x + y <= 8
    :type constraints: List[Tuple[int, int]]
    """
    
    def __init__(self, objective: list[int], constraints: list[tuple[int, int]]):
        self.objective = objective
        self.constraints = constraints
        
    def solve(self) -> tuple[int, dict[str, int]]:
        """Solve the constrained optimization problem."""
        from scipy.optimize import linprog

        # Convert constraints to format expected by linprog:
        A = []
        b = []

        for constraint in self.constraints:
            coefficient, rhs = constraint
            A.append([coefficient])
            b.append(rhs)

        result = linprog(c=self.objective, A_ub=A, b_ub=b)
        
        if result.success:
            solution = dict(zip(['x'], [int(result.x[0])]))
            return int(result.fun), solution
        else:
            raise Exception("No feasible solution found.")
            
# Example usage:
engine = ReasoningEngine(objective=[3, 5], constraints=[(2, 10), (-1, 8)])
solution, value = engine.solve()
print(f"Solution: {solution}, x: {value}")
```

This code creates a simple reasoning engine using the `linprog` function from SciPy to solve linear optimization problems with given constraints. The example usage demonstrates finding the maximum of a linear function subject to two inequalities.