Applied Dynamic Programming for Optimization of Dynamical Systems

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Applied Dynamic Programming For Optimization Of Dynamical Systems

Applied Dynamic Programming. Elements of Dynamic Optimization. Dynamic Programming Pure and Applied Mathematics. Dynamic Programming.

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Dynamic programming. Applied shape optimization for fluids. Stability of Dynamical Systems. Stability of dynamical systems. Handbook of dynamical systems. Dynamical Systems. In both examples, we only calculate fib 2 one time, and then use it to calculate both fib 4 and fib 3 , instead of computing it every time either of them is evaluated. There are at least three possible approaches: brute force , backtracking , and dynamic programming. Dynamic programming makes it possible to count the number of solutions without visiting them all.

Imagine backtracking values for the first row — what information would we require about the remaining rows, in order to be able to accurately count the solutions obtained for each first row value? The function f to which memoization is applied maps vectors of n pairs of integers to the number of admissible boards solutions.

There is one pair for each column, and its two components indicate respectively the number of zeros and ones that have yet to be placed in that column.

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If any one of the results is negative, then the assignment is invalid and does not contribute to the set of solutions recursion stops. Links to the MAPLE implementation of the dynamic programming approach may be found among the external links. Let us say there was a checker that could start at any square on the first rank i.

Dynamic Programming : Solving Linear Programming Problem using Dynamic Programming Approach

That is, a checker on 1,3 can move to 2,2 , 2,3 or 2,4. This problem exhibits optimal substructure.

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That is, the solution to the entire problem relies on solutions to subproblems. Let us define a function q i, j as. Starting at rank n and descending to rank 1 , we compute the value of this function for all the squares at each successive rank. Picking the square that holds the minimum value at each rank gives us the shortest path between rank n and rank 1.

The function q i, j is equal to the minimum cost to get to any of the three squares below it since those are the only squares that can reach it plus c i, j.

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For instance:. The first line of this equation deals with a board modeled as squares indexed on 1 at the lowest bound and n at the highest bound. The second line specifies what happens at the last rank; providing a base case. The third line, the recursion, is the important part. It represents the A,B,C,D terms in the example. In the following pseudocode, n is the size of the board, c i, j is the cost function, and min returns the minimum of a number of values:.

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This function only computes the path cost, not the actual path. We discuss the actual path below. This, like the Fibonacci-numbers example, is horribly slow because it too exhibits the overlapping sub-problems attribute. That is, it recomputes the same path costs over and over. However, we can compute it much faster in a bottom-up fashion if we store path costs in a two-dimensional array q[i, j] rather than using a function. This avoids recomputation; all the values needed for array q[i, j] are computed ahead of time only once. Precomputed values for i,j are simply looked-up whenever needed.

We also need to know what the actual shortest path is. To do this, we use another array p[i, j] ; a predecessor array. This array records the path to any square s. The predecessor of s is modeled as an offset relative to the index in q[i, j] of the precomputed path cost of s. To reconstruct the complete path, we lookup the predecessor of s , then the predecessor of that square, then the predecessor of that square, and so on recursively, until we reach the starting square.

Consider the following code:.

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In genetics , sequence alignment is an important application where dynamic programming is essential. Each operation has an associated cost, and the goal is to find the sequence of edits with the lowest total cost. The problem can be stated naturally as a recursion, a sequence A is optimally edited into a sequence B by either:.

The partial alignments can be tabulated in a matrix, where cell i,j contains the cost of the optimal alignment of A[ The cost in cell i,j can be calculated by adding the cost of the relevant operations to the cost of its neighboring cells, and selecting the optimum. Different variants exist, see Smith—Waterman algorithm and Needleman—Wunsch algorithm. The Tower of Hanoi or Towers of Hanoi is a mathematical game or puzzle. It consists of three rods, and a number of disks of different sizes which can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top, thus making a conical shape.

The objective of the puzzle is to move the entire stack to another rod, obeying the following rules:. The dynamic programming solution consists of solving the functional equation. Then it can be shown that [14]. An interactive online facility is available for experimentation with this model as well as with other versions of this puzzle e. However, there is an even faster solution that involves a different parametrization of the problem:.

Matrix chain multiplication is a well-known example that demonstrates utility of dynamic programming. For example, engineering applications often have to multiply a chain of matrices. Therefore, our task is to multiply matrices A 1 , A 2 ,.

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As we know from basic linear algebra, matrix multiplication is not commutative, but is associative; and we can multiply only two matrices at a time. So, we can multiply this chain of matrices in many different ways, for example:. There are numerous ways to multiply this chain of matrices. They will all produce the same final result, however they will take more or less time to compute, based on which particular matrices are multiplied. For example, let us multiply matrices A, B and C.

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Obviously, the second way is faster, and we should multiply the matrices using that arrangement of parenthesis. Therefore, our conclusion is that the order of parenthesis matters, and that our task is to find the optimal order of parenthesis.

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  • At this point, we have several choices, one of which is to design a dynamic programming algorithm that will split the problem into overlapping problems and calculate the optimal arrangement of parenthesis. The dynamic programming solution is presented below. Let's call m[i,j] the minimum number of scalar multiplications needed to multiply a chain of matrices from matrix i to matrix j i. A nonlinear optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over the prediction horizon and the control energy over a shorter control horizon.

    The problem is solved on line using a specially designed genetic algorithm, which has a number of advantages over conventional nonlinear optimization techniques. The method can be used with any type of fuzzy model and is particularly useful when a direct fuzzy controller cannot be designed due to the complexity of the process and the difficulty in developing fuzzy control rules. Production planning and inventory control: New methodologies based on control theory have been developed. An adaptation method for the online identification of lead time is incorporated in production-inventory control systems.

    Based on the lead time estimate, the tuning parameters are updated in real time to improve the efficiency of the system. Combination of the adaptive scheme with a proportional control law is able to eliminate the inventory drift that appears when the actual lead time is not known in advance or when it varies with time.

    An adaptive MPC configuration has also been developed for the identification and control of production-inventory systems.

    The time varying dynamic behavior of the production process is approximated by an adaptive Finite Impulse Response FIR model. The adapted model along with a smoothed estimation of the future customer demand, are used to predict inventory levels over the optimization horizon. The proposed scheme is able to eliminate the inventory drift and suppress the bullwhip effect. We have developed software tools for obtaining optimal production plans for the food, petrochemical and pulp and paper industries.

    Chemoinformatics and bioinformatics: Development of mathematical relationships linking chemical structure and pharmacological activity in a quantitative manner for a series of compounds. Standard statistical tools as well as advanced machine learning methodologies neural networks, kernel methods, evolutionary algorithms have been employed.

    Applied Dynamic Programming for Optimization of Dynamical Systems Applied Dynamic Programming for Optimization of Dynamical Systems
    Applied Dynamic Programming for Optimization of Dynamical Systems Applied Dynamic Programming for Optimization of Dynamical Systems
    Applied Dynamic Programming for Optimization of Dynamical Systems Applied Dynamic Programming for Optimization of Dynamical Systems
    Applied Dynamic Programming for Optimization of Dynamical Systems Applied Dynamic Programming for Optimization of Dynamical Systems
    Applied Dynamic Programming for Optimization of Dynamical Systems Applied Dynamic Programming for Optimization of Dynamical Systems
    Applied Dynamic Programming for Optimization of Dynamical Systems Applied Dynamic Programming for Optimization of Dynamical Systems

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