minségfaktor

Minségfaktor is a term used in optimization and decision analysis to denote a weight that scales a minimization term within a composite objective. The precise definition varies between fields and authors, and there is no universally standardized meaning for the concept.

In practice, many optimization problems combine multiple criteria into a single scalar objective. A typical form is J(x) = f1(x) + k · f2(x) + ... , where k is the minségfaktor. By adjusting k, the relative importance of the second criterion f2(x) is controlled, effectively shaping the trade-off between competing goals. A small k emphasizes f1, while a large k places more emphasis on minimizing f2. This interpretation makes the minségfaktor resemble other trade-off parameters or regularization strengths found in optimization.

Estimation and selection of a minségfaktor often rely on parameter tuning methods such as grid search, cross-validation, or Bayesian optimization. Sensitivity analysis is commonly performed to understand how changes in k affect the chosen solution and the overall performance of the system.

Applications and limitations: the concept appears across engineering, economics, operations research, and machine learning when multiple criteria must be balanced. While convenient, the use of a minségfaktor introduces subjectivity and potential bias, and the resulting solution can be highly sensitive to the chosen value. Clear reporting of how k was selected and accompanying robustness analyses are recommended.

See also: trade-off parameter, regularization parameter, multi-objective optimization, Lagrange multiplier.