Given a set of constraints and an objective function, find the optimal value. Z3 supports both hard constraints (must hold) and soft constraints (weighted preferences), as well as lexicographic multi-objective optimization.

Step 1: Formulate the problem

Action: Write constraints and an objective using (minimize ...) or (maximize ...) directives, followed by (check-sat) and (get-model).

Expectation: A valid SMT-LIB2 formula with at least one optimization directive and all variables declared.

Result: If the formula is well-formed, proceed to Step 2. For multi-objective problems, list directives in priority order for lexicographic optimization.

Example: minimize x + y subject to x >= 1, y >= 2, x + y <= 10:

(declare-const x Int)
(declare-const y Int)
(assert (>= x 1))
(assert (>= y 2))
(assert (<= (+ x y) 10))
(minimize (+ x y))
(check-sat)
(get-model)

Step 2: Run the optimizer

Action: Invoke optimize.py with the formula or file path.

Expectation: The script prints sat with the optimal assignment, unsat, unknown, or timeout. A run entry is logged to z3agent.db.

Result: On sat: proceed to Step 3 to read the optimal values. On unsat or timeout: check constraints for contradictions or simplify.

python3 scripts/optimize.py --file scheduling.smt2
python3 scripts/optimize.py --formula "<inline smt-lib2>" --debug

Step 3: Interpret the output

Action: Parse the objective value and satisfying assignment from the output.

Expectation: sat with a model containing the optimal value, unsat indicating infeasibility, or unknown/timeout.

Result: On sat: report the optimal value and assignment. On unsat: the constraints are contradictory, no feasible solution exists. On unknown/timeout: relax constraints or try simplify.

Parameters