Dynamic Programming | DAA

Dynamic Programming:

Dynamic Programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions using a memory-based data structure (array, map,etc). Each of the subproblem solutions is indexed in some way, typically based on the values of its input parameters, so as to facilitate its lookup. So the next time the same subproblem occurs, instead of recomputing its solution, one simply looks up the previously computed solution, thereby saving computation time. This technique of storing solutions to subproblems instead of recomputing them is called memoization.

Technique is among the most powerful for designing algorithms for optimization problems. Dynamic programming problems are typically optimization problems (find the minimum or maximum cost solution, subject to various constraints). The technique is related to divide-and-conquer, in the sense that it breaks problems down into smaller problems that it solves recursively. However, because of the somewhat different nature of dynamic programming problems, standard divide-and-conquer solutions are not usually efficient. The basic elements that characterize a dynamic programming algorithm are:

·        Substructure:
Decompose your problem into smaller (and hopefully simpler) subproblems. Express the solution of the original problem in terms of solutions for smaller problems.

·        Table-structure:
Store the answers to the sub-problems in a table. This is done because subproblem solutions are reused many times.

·        Bottom-up computation:
Combine solutions on smaller subproblems to solve larger subproblems. (We also discuss a top-down alternative, called memorization)

The most important question in designing a DP solution to a problem is how to set up the subproblem structure. This is called the formulation of the problem. Dynamic programming is not applicable to all optimization problems. There are two important elements that a problem must have in order for DP to be applicable.

Optimal substructure:
(Sometimes called the principle of optimality.) It states that for the global problem to be solved optimally, each subproblem should be solved optimally. (Not all optimization problems satisfy this. Sometimes it is better to lose a little on one subproblem in order to make a big gain on another.)

Polynomially many subproblems:
An important aspect to the efficiency of DP is that the total number of subproblems to be solved should be at most a polynomial number.


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