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In
computer science Computer science is the study of computation, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, ...
, a problem is said to have optimal substructure if an optimal solution can be constructed from optimal solutions of its subproblems. This property is used to determine the usefulness of greedy algorithms for a problem.{{cite book, title=Introduction to Algorithms , edition=3rd, last1=Cormen, first1=Thomas H. , last2=Leiserson , first2=Charles E. , last3=Rivest, first3=Ronald L. , last4= Stein , first4=Clifford, date=2009 , isbn=978-0-262-03384-8, publisher=
MIT Press The MIT Press is the university press of the Massachusetts Institute of Technology (MIT), a private research university in Cambridge, Massachusetts. The MIT Press publishes a number of academic journals and has been a pioneer in the Open Ac ...
, authorlink1=Thomas H. Cormen , authorlink2=Charles E. Leiserson, authorlink3=Ron Rivest , authorlink4=Clifford Stein Typically, a
greedy algorithm A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage. In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally ...
is used to solve a problem with optimal substructure if it can be proven by induction that this is optimal at each step. Otherwise, provided the problem exhibits
overlapping subproblems In computer science, a Computational problem, problem is said to have overlapping subproblems if the problem can be broken down into subproblems which are reused several times or a recursive algorithm for the problem solves the same subproblem over ...
as well, divide-and-conquer methods or dynamic programming may be used. If there are no appropriate greedy algorithms and the problem fails to exhibit overlapping subproblems, often a lengthy but straightforward search of the solution space is the best alternative. In the application of dynamic programming to
mathematical optimization Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. It is generally divided into two subfiel ...
,
Richard Bellman Richard Ernest Bellman (August 26, 1920 – March 19, 1984) was an American applied mathematician, who introduced dynamic programming in 1953, and made important contributions in other fields of mathematics, such as biomathematics. He foun ...
's Principle of Optimality is based on the idea that in order to solve a dynamic optimization problem from some starting period ''t'' to some ending period ''T'', one implicitly has to solve subproblems starting from later dates ''s'', where ''tBellman equation A Bellman equation, named after Richard E. Bellman, is a necessary condition for optimality associated with the mathematical Optimization (mathematics), optimization method known as dynamic programming. It writes the "value" of a decision problem ...
, which shows how the value of the problem starting from ''t'' is related to the value of the problem starting from ''s''.


Example

Consider finding a
shortest path In graph theory, the shortest path problem is the problem of finding a path between two vertices (or nodes) in a graph such that the sum of the weights of its constituent edges is minimized. The problem of finding the shortest path between two ...
for traveling between two cities by car, as illustrated in Figure 1. Such an example is likely to exhibit optimal substructure. That is, if the shortest route from Seattle to Los Angeles passes through Portland and then Sacramento, then the shortest route from Portland to Los Angeles must pass through Sacramento too. That is, the problem of how to get from Portland to Los Angeles is nested inside the problem of how to get from Seattle to Los Angeles. (The wavy lines in the graph represent solutions to the subproblems.) As an example of a problem that is unlikely to exhibit optimal substructure, consider the problem of finding the cheapest airline ticket from Buenos Aires to Moscow. Even if that ticket involves stops in Miami and then London, we can't conclude that the cheapest ticket from Miami to Moscow stops in London, because the price at which an airline sells a multi-flight trip is usually not the sum of the prices at which it would sell the individual flights in the trip.


Definition

A slightly more formal definition of optimal substructure can be given. Let a "problem" be a collection of "alternatives", and let each alternative have an associated cost, ''c''(''a''). The task is to find a set of alternatives that minimizes ''c''(''a''). Suppose that the alternatives can be partitioned into subsets, i.e. each alternative belongs to only one subset. Suppose each subset has its own cost function. The minima of each of these cost functions can be found, as can the minima of the global cost function, ''restricted to the same subsets''. If these minima match for each subset, then it's almost obvious that a global minimum can be picked not out of the full set of alternatives, but out of only the set that consists of the minima of the smaller, local cost functions we have defined. If minimizing the local functions is a problem of "lower order", and (specifically) if, after a finite number of these reductions, the problem becomes trivial, then the problem has an optimal substructure.


Problems with optimal substructure

*
Longest common subsequence problem A longest common subsequence (LCS) is the longest subsequence common to all sequences in a set of sequences (often just two sequences). It differs from the longest common substring: unlike substrings, subsequences are not required to occupy conse ...
* Longest increasing subsequence * Longest palindromic substring * All-Pairs Shortest Path * Any problem that can be solved by dynamic programming.


Problems ''without'' optimal substructure

* Longest path problem * Addition-chain exponentiation * ''Least-cost airline fare.'' Using online flight search, we will frequently find that the cheapest flight from airport A to airport B involves a single connection through airport C, but the cheapest flight from airport A to airport C involves a connection through some other airport D. However, if the problem takes the maximum number of layovers as a parameter, then the problem has optimal substructure. The cheapest flight from A to B that involves at most ''k'' layovers is either the direct flight; or the cheapest flight from A to some airport C that involves at most ''t'' layovers for some integer ''t'' with ''0≤t

See also

* Dynamic Programming * Principle of optimality *
Divide and conquer algorithm In computer science, divide and conquer is an algorithm design paradigm. A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved dir ...


References

Dynamic programming