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	Programowanie z ograniczeniami	Programowanie całkowitoliczbowe
Dziedzina	Optymalizacja	Optymalizacja
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2006	1958
Twórca	Rossi, van Beek & Walsh	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)
Typ	Declarative combinatorial optimization	Mathematical optimisation — exact combinatorial method
Źródło pierwotne	Rossi, F., van Beek, P., & Walsh, T. (Eds.). (2006). Handbook of Constraint Programming. Elsevier. ISBN: 978-0-444-52726-4	Wolsey, L.A. (1998). Integer Programming. Wiley. ISBN: 9780471283669
Inne nazwy	Constraint Satisfaction Programming, Constraint-Based Optimization, Kısıt Programlama, CSP Optimization	IP, MIP, mixed-integer programming, mixed-integer linear programming
Pokrewne	3	4
Podsumowanie	Constraint Programming (CP) is a declarative optimization paradigm in which a problem is formulated as a set of variables, finite domains, and constraints, and a solver systematically searches for assignments that satisfy all constraints. Formalized comprehensively by Rossi, van Beek, and Walsh in their 2006 Handbook of Constraint Programming, CP unifies propagation-based pruning with intelligent backtracking search to tackle combinatorial problems across scheduling, planning, and configuration domains.	Integer programming (IP), also called mixed-integer programming (MIP) when only some variables are restricted to whole numbers, is a branch of mathematical optimisation in which some or all decision variables must take integer or binary values. Building on linear programming, it was formalised through Ralph Gomory's cutting-plane method (1958) and the Land-and-Doig branch-and-bound algorithm (1960), and it has since become the standard exact framework for scheduling, assignment, routing, and resource-allocation problems.