In this Chapter we present some general algorithmic techniques that have proved to be useful in speeding up the computation of some families of dynamic programming recurrences which have applications in sequence alignment, paragraph formation and prediction of RNA secondary structure. The material presented in this chapter is related to the computation of Levenshtein distances and approximate string matching that have been discussed in the previous three chapters. Dynamic programming is a general technique for solving discrete optimization (minimization or maximization) problems that can be represented by decision processes and for which the principle of optimality holds. We can view a decision process as a directed graph in which nodes represent the states of the process and edges represent decisions. The optimization problem at hand is represented as a decision process by decomposing it into a set of subproblems of smaller size. Such recursive decomposition is continued until we get only trivial subproblems, which can be solved directly. Each node in the graph corresponds to a subproblem and each edge (a, b) indicates that one way to solve subproblem a optimally is to solve first subproblem b optimally. Then, an optimal solution, or policy, is typically given by a path on the graph that minimizes or maximizes some objective function. The correctness of this approach is guaranteed by the principle of optimality which must be satisfied by the optimization problem: An optimal policy has the property that whatever the initial node (state) and initial edge (decision) are, the remaining edges (decisions) must be an optimal policy with regard to the node (state) resulting from the first transition. Another consequence of the principle of optimality is that we can express the optimal cost (and solution) of a subproblem in terms of optimal costs (and solutions) of problems of smaller size. That is, we can express optimal costs through a recurrence relation. This is a key component of dynamic programming, since we can compute the optimal cost of a subproblem only once, store the result in a table, and look it up when needed.
On the principle of optimality for nonstationary deterministic dynamic programming
