Learning Dynamic Planning

Start learning dynamic planning today:

First, we have to think about these questions?

1. What is dynamic planning?

2. Is dynamic planning an algorithm or an idea?

3. What is the model and significance of dynamic planning?

4. What is the basic idea of dynamic planning?

5. What are the application conditions for dynamic planning? What questions should I use? What questions should not be used?

6. What are the functions of dynamic planning?

1. The multi-stage process is transformed into a series of single-stage problems, and the relationship between each stage is used to solve them one by one. A new method for solving such process optimization problems-Dynamic Planning

2. Dynamic Programming is a way and method to solve the optimization problem, rather than a special algorithm. Unlike the search or numeric calculations described above, there is a standard mathematical expression and a clear and clear solution. Dynamic Programming is usually aimed at an optimization problem. Due to the different nature of various problems, the conditions for determining the optimal solution are also different. Therefore, the design method of dynamic planning is different for different problems, there are different solutions, but there is no universal dynamic planning algorithm, can solve all kinds of optimization problems. Therefore, in addition to a correct understanding of the basic concepts and methods, readers must analyze and handle specific problems, build models with rich imagination, and use creative techniques to solve them. We can also analyze and discuss Dynamic Planning Algorithms for several representative problems, and gradually learn and master this design method. (From Baidu encyclopedia)

3.

Optimization of the multi-stage decision-making process.

Contains recursive ideas and various mathematical principles (addition principle, multiplication principle, etc ).

In real life, there is a kind of activity process. Due to its particularity, the process can be divided into several interrelated stages, and decision-making is required at each stage of the process, so that the entire process achieves the best activity effect. Of course, the decision-making in each stage is not randomly determined. It depends on the current status and affects future development. When the decision-making in each stage is determined, it forms a decision sequence, therefore, an activity route for the entire process is determined. (view the entry chart)

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This kind of problem is regarded as a multi-stage process with a chain structure, which is called a multi-stage decision-making process. This problem is called a multi-stage decision-making problem. (From Baidu encyclopedia)

4.

A dynamic planning algorithm is usually used to solve a problem with an optimal nature. In such problems, there may be many feasible solutions. Each solution corresponds to a value. We hope to find a solution with the optimal value. The dynamic planning algorithm is similar to the divide and conquer method. Its basic idea is to break down the problem to be solved into several subproblems, first solve the subproblems, and then obtain the original solution from the subproblems. Different from the Division and control method, it is suitable for Dynamic Planning and solving problems. After decomposition, subproblems are often not independent of each other. If you use the divide and conquer method to solve this type of problem, the number of subproblems obtained by the decomposition is too large, and some subproblems are repeatedly calculated many times. If we can save the answers to the resolved sub-questions and find the answers we have obtained when necessary, we can avoid a large number of repeated computations and save time. We can use a table to record the answers to all resolved subquestions. Whether or not this subproblem is used in the future, as long as it has been computed, the results will be filled in the table. This is the basic idea of dynamic planning. The specific dynamic planning algorithms are diverse, but they have the same table filling format. (From Baidu encyclopedia)

5. (1) determine the decision object of the problem. (2) divide the decision-making process into stages. (3) determine the status variables for each stage. (4) determine the billing and target functions based on the status variables. (5) establish the transition process of state variables in each stage and determine the state transition equation.

6.

Applicable Conditions

Any method of thinking has certain limitations. If it exceeds the specific conditions, it will lose its function. Similarly, dynamic planning is not omnipotent. The problem of dynamic planning must meet the Optimization Principle and have no aftereffect.

1. the Optimization Principle (optimal sub-structure nature) optimization principle can be elaborated as follows: an optimization strategy has this nature, regardless of the past status and decision-making, for the status formed by the previous decision, the remaining decisions must constitute an optimal policy. In short, the sub-strategy of an optimization policy is always optimal. A problem that satisfies the optimization principle is also known as its optimal sub-structure.

2. after the stages are arranged in a certain order, the status of a given stage cannot directly affect its future decision-making, you can only use the current status. In other words, each State is a complete summary of past history. This is also called "no-effect.

3. the overlapping Dynamic Planning of sub-problems improves the search algorithm with exponential complexity into an algorithm with polynomial time. The key lies in solving redundancy, which is the fundamental purpose of the dynamic planning algorithm. Dynamic Planning is essentially a technology that uses space for time. in the implementation process, it has to store various states in the Process of generation. Therefore, its spatial complexity is greater than that of other algorithms. (From Baidu encyclopedia)