felipecustodio Overview & Purpose
Optimization Reasoning identifies the mathematically or logically best solution from a set of available alternatives subject to defined constraints. It turns vague trade-offs into formal objective functions.
When to Use
- Resource Allocation: Budgeting, memory allocation, thread pool sizing, scheduling.
- Trade-off Analysis: Maximizing performance while minimizing cost or latency constraints.
Execution Workflow
- Identify Decision Variables: Define the tunable variables ($x_1, x_2, \dots, x_n$).
- create Objective Function: State the goal explicitly (e.g., $\max f(x)$ or $\min g(x)$).
- Define Hard Constraints: List strict boundaries (e.g., $h_i(x) \le C$).
- Evaluate Feasible Region: Find the optimal combination of variables satisfying all constraints.
Expected Output Contract
### Optimization Model
- **Objective**: [Maximize / Minimize Statement]
- **Variables**: [List of decision variables]
- **Constraints**: [Hard limits]
- **Optimal Solution**: [Recommended Configuration]
Scripts
scripts/optimization_reasoning.py- Deterministic evaluation, state validation, and CLI tool for optimization-reasoning.