Võrdle meetodeid

	Dünamiline programmeerimine	Kitsendustega programmeerimine	Täisarvuline programmeerimine – IP ja segaarvuline programmeerimine (MIP)
Valdkond	Optimeerimine	Optimeerimine	Optimeerimine
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	1957	2006	1958
Looja	Richard Bellman	Rossi, van Beek & Walsh	Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)
Tüüp	Exact combinatorial optimization via recursive decomposition	Declarative combinatorial optimization	Mathematical optimisation — exact combinatorial method
Algallikas	Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6	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
Rööpnimetused	DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama	Constraint Satisfaction Programming, Constraint-Based Optimization, Kısıt Programlama, CSP Optimization	IP, MIP, mixed-integer programming, mixed-integer linear programming
Seotud	3	3	4
Kokkuvõte	Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure.	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.