overminimization

Overminimization is a term used in optimization and related disciplines to describe a situation in which the process of minimization proceeds so aggressively that essential properties, constraints, or diversity are lost. Although not a formal, standardized term, it is used to describe outcomes where the objective is reduced beyond what is desirable, compromising validity or usefulness.

In optimization and machine learning, overminimization can occur when the objective dominates at the expense of feasibility or other criteria. For example, a solver might minimize an energy or cost function without sufficiently respecting hard constraints, producing infeasible or unsafe solutions. In multiobjective settings, excessive weighting toward the primary minimization objective can erode other important objectives, reducing robustness and generalization. In data processing or model simplification, overminimization can refer to excessive reduction of features, rules, or code, leading to loss of interpretability, expressiveness, or intended behavior.

Causes of overminimization include poorly chosen objective functions, mis-specified constraints, numerical precision limits, and overreliance on automated minimization without adequate domain knowledge. A lack of validation across representative scenarios can also mask such issues until deployment.

Consequences include violations of feasibility, degraded performance, brittle models, or unsafe outcomes. Mitigation strategies emphasize preserving essential constraints and diversity while pursuing minimization. These include explicitly encoding hard constraints, employing multiobjective or lexicographic optimization, monitoring feasibility during optimization, performing thorough validation and stress-testing, and incorporating human judgment to maintain interpretability and safety.

See also: overfitting, underfitting, regularization, optimization, Pareto efficiency, model simplification, code minification.