1 problem.Problem

problem.Problem(name=None)

An optimization problem with objective and constraints.

1.1 Example

x = Variable(“x”, lb=0) y = Variable(“y”, lb=0) prob = Problem() prob.minimize(x2 + y2) prob.subject_to(x + y >= 1) solution = prob.solve() print(solution.values) # {‘x’: 0.5, ‘y’: 0.5}

1.2 Note

The Problem class is not thread-safe. Compiled callables are cached per instance and reused across multiple solve() calls for performance. Any mutation (adding constraints, changing objective) invalidates the cache.

1.3 Attributes

Name	Description
constraints	List of constraints.
n_constraints	Number of constraints.
n_variables	Number of decision variables.
objective	The objective function expression.
sense	The optimization sense (minimize or maximize).
variables	All decision variables in the problem.

1.4 Methods

Name	Description
get_bounds	Get variable bounds as a list of (lb, ub) tuples.
maximize	Set the objective function to maximize.
minimize	Set the objective function to minimize.
solve	Solve the optimization problem.
subject_to	Add a constraint or list of constraints to the problem.

1.4.1 get_bounds

problem.Problem.get_bounds()

Get variable bounds as a list of (lb, ub) tuples.

1.4.1.1 Returns

Name	Type	Description
	list[tuple[float | None, float | None]]	List of bounds in variable order.

1.4.2 maximize

problem.Problem.maximize(expr)

Set the objective function to maximize.

1.4.2.1 Parameters

Name	Type	Description	Default
expr	Expression	Expression to maximize. Must be an optyx Expression, Variable, or numeric constant (int/float).	required

1.4.2.2 Returns

Name	Type	Description
	Problem	Self for method chaining.

1.4.2.3 Raises

Name	Type	Description
	InvalidOperationError	If expr is not a valid expression type.

1.4.2.4 Example

prob.maximize(revenue - cost)

1.4.3 minimize

problem.Problem.minimize(expr)

Set the objective function to minimize.

1.4.3.1 Parameters

Name	Type	Description	Default
expr	Expression	Expression to minimize. Must be an optyx Expression, Variable, or numeric constant (int/float).	required

1.4.3.2 Returns

Name	Type	Description
	Problem	Self for method chaining.

1.4.3.3 Raises

Name	Type	Description
	InvalidOperationError	If expr is not a valid expression type.

1.4.3.4 Example

prob.minimize(x2 + y2) prob.minimize(x + 2*y - 5)

1.4.4 solve

problem.Problem.solve(method='auto', strict=False, **kwargs)

Solve the optimization problem.

1.4.4.1 Parameters

Name	Type	Description	Default
method	str	Solver method. Options: - “auto” (default): Automatically select the best method: - Linear problems → linprog (HiGHS) - Unconstrained, n ≤ 3 → Nelder-Mead - Unconstrained, n > 1000 → L-BFGS-B - Unconstrained, else → BFGS - Bounds only → L-BFGS-B - Equality constraints → trust-constr - Inequality only → SLSQP - “linprog”: Force LP solver (scipy.optimize.linprog) - “SLSQP”: Sequential Least Squares Programming - “trust-constr”: Trust-region constrained optimization - “L-BFGS-B”: Limited-memory BFGS with bounds - “BFGS”: Broyden-Fletcher-Goldfarb-Shanno - “Nelder-Mead”: Derivative-free simplex method	'auto'
strict	bool	If True, raise ValueError when the problem contains features that the solver cannot handle exactly (e.g., integer/binary variables with SciPy). If False (default), emit a warning and use the best available approximation.	False
**kwargs		Additional arguments passed to the solver.	{}

1.4.4.2 Returns

Name	Type	Description
	Solution	Solution object with results.

1.4.4.3 Raises

Name	Type	Description
	ValueError	If no objective has been set, or if strict=True and the problem contains unsupported features.

1.4.5 subject_to

problem.Problem.subject_to(constraint)

Add a constraint or list of constraints to the problem.

1.4.5.1 Parameters

Name	Type	Description	Default
constraint	Constraint | list[Constraint]	Constraint or list of constraints to add. Lists are typically produced by vectorized constraints like x >= 0 on VectorVariable.	required

1.4.5.2 Returns

Name	Type	Description
	Problem	Self for method chaining.

1.4.5.3 Raises

Name	Type	Description
	ConstraintError	If constraint is not a valid Constraint type.

1.4.5.4 Example

x = VectorVariable(“x”, 100) prob.subject_to(x >= 0) # Adds 100 constraints