The term "functions for solving combinatorial problems" refers to software functionalities specifically designed to address complex mathematical and logical problems involving the selection, arrangement, or combination of elements. These problems often arise in optimization, planning, and decision-making and typically require specialized algorithms to find optimal or near-optimal solutions.

Typical software functions in the area of "functions for solving combinatorial problems":

- Optimization Algorithms: Implementation of algorithms such as Branch-and-Bound, Genetic Algorithms, or Simulated Annealing for finding optimal solutions to combinatorial problems.
- Randomized Methods: Use of Monte Carlo simulations or other stochastic methods to approximate solutions, especially in very large or complex problem spaces.
- Heuristic Methods: Application of heuristics like Greedy Algorithms, Tabu Search, or Local Search to quickly find good, but not necessarily optimal, solutions.
- Constraint Satisfaction Problems: Functions to solve problems where a set of constraints must be satisfied, such as Sudoku puzzles or scheduling problems.
- Graph-Based Algorithms: Implementation of algorithms for problems based on graphs, such as the Traveling Salesman Problem (TSP) or the Maximum Flow Problem.
- Solution Visualization: Tools for graphical representation of solutions and search paths to facilitate the interpretation and analysis of results.
- Analysis and Reporting: Functions for analyzing results, generating reports, and documenting the solutions found and the methods used.

Utilization analysis according to loss classes