Mathematical Optimization
Roles: objective-function, constraint, feasible-region, global-optimum, local-optimum, search-space, gradient, cost-function, algorithm
The mathematics of finding the best solution from a set of alternatives subject to constraints. As a source domain, optimization supplies concepts of search landscapes (hills, valleys, plateaus), trade-offs (Pareto frontiers), and the fundamental tension between exploration and exploitation. When we say we are “optimizing for” something, searching for the “best fit,” or “stuck in a local maximum,” we are borrowing from this frame. Its metaphorical power comes from making the structure of choice legible as landscape navigation; its danger lies in assuming that all problems have well-defined objective functions and that “optimal” is meaningful when applied to domains where the evaluation criteria are contested or shifting.