haskell dynamic programming

static types and dynamic types have strengths, and static types allow the compiler to help you find more errors, because the compiler can do most of the checking at compile time. C # is a static type of language, but it adds dynamic types of language features that can be used more efficiently to solve problems.First, Directory 38. Understanding the merits and demerits of

R.bellman and others in 1951 in the study of multi-stage decision-making process optimization problems created a new method to solve such process optimization problems.The inverse recursive method is called the Dynamic Programming Method (programming).Multi-stage decision makingThere is one type of problem that can break down its activity process into several int

Multi-stage decision-making (multistep decision process) refers to a special kind of activity process, which can be decomposed into several interconnected phases in chronological order, and the decision-making is a decision sequence in every stage. Dynamic programming (dynamical programming) algorithm is a common method to solve the optimization problem of multi-

Complex problems often occur. Instead of simply breaking down them into several subproblems, they may break down a series of subproblems. Simply resolve a large problem into a sub-problem, and combine the sub-problem solution to export the solution of the big problem. the time consumed for solving the problem increases in a power series according to the scale of the problem. To reduce the time required to repeatedly find the same subproblem, an array is introduced, no matter whether they are use

different stages in a two-dimensional array.The biggest difference between the division and the method is: suitable for the problem solved by the dynamic programming method, the sub-problems obtained after decomposition are often not independent of each other (that is, the next sub-stage solution is based on the solution of the previous sub-stage, and further solution). Third, the application of the

First, the basic concept of dynamic planning process is: each decision depends on the current state, and then cause the transfer of state. A decision sequence is generated in the state of change, so the process of solving this problem by multi-stage optimization is called dynamic programming.The basic thought and strategy are similar to the Division method, and the problem to be solved is decomposed into se

First, the basic idea of dynamic planning In general, as long as the problem can be divided into smaller sub-problems, and the original problem of the optimal solution contains the sub-problem of the optimal solution, you can consider the dynamic programming solution. The essence of dynamic

First, let's take a look at the four steps of dynamic planning:1. Identify the nature of the optimal solution and characterize its structure;2. Recursive definition of the optimal solution;3. Describe the optimal value in a bottom-up manner;4. The optimal solution is constructed according to the information obtained when the optimal value is calculated.The improved dynamic

repetition, we only solve each sub-problem once and save the different states of different stages in a two-dimensional array.The biggest difference between the division and the method is: suitable for the problem solved by the dynamic programming method, the sub-problems obtained after decomposition are often not independent of each other (that is, the next sub-stage solution is based on the solution of th

different stages in a two-dimensional array.The biggest difference between the division and the method is: suitable for the problem solved by the dynamic programming method, the sub-problems obtained after decomposition are often not independent of each other (that is, the next sub-stage solution is based on the solution of the previous sub-stage, and further solution).Third, the application of the situati

Optimal substructureIn computer science, a problem are said to having optimal substructure if an optimal solution can be constructed efficiently From Optimal solutions of its subproblems. The used to determine the usefulness of dynamic programming and greedy algorithms for a problem.Typically, a greedy algorithm is used to solve a problem with optimal substructure if it can be proved by inductionC0>this is

third feature, you can consider using greedy or dynamic programming method.The fourth feature relates to the efficiency of division and treatment, if the sub-problems are not independent, then divide and conquer the law to do a lot of unnecessary work, repeated to solve the common sub-problem, although the use of divided treatment method, but generally with the dynamic