Dynamic programming (DP) is a fast, elegant method for solving many one-dimensional optimisation problems but, unfortunately, most problems in image analysis, such as restoration and warping, are two-dimensional. We consider three generalisations of DP. The first is iterated dynamic programming (IDP), where DP is used to recursively solve each of a sequence of one-dimensional problems in turn, ...

Following Karp's discrete Dynamic Programming (DP) approach, this work extends the sequential model for monadic DP to the parallel case. We propose general parallel DP algorithms for pipeline and ring networks. The study of the optimality of these algorithms leads us to the introduction of new classes of multistage automata. However, the important class of Polyadic Dynamic Problems is out of th...

Dynamic programming (DP) has long been used to solve optimisation problems. Though sometimes suffering from the curse of dimensionality, DP has a dowry of integrality. Integer programming (IP) often suffers interminable branch and bound, but brings the gift of flexibility. DP tends to be hard to program, but IP allows us to easily write models that are hard to solve. Until recently, DP and IP h...

The detection and tracking of small weak maneuvering radar targets in complex electromagnetic environments is still a difficult problem to effectively solve. To address this problem, paper proposes dynamic programming tracking-before-detection method based on long short-term memory (LSTM) network value function(VL-DP-TBD). With the help estimated posterior probability provided by designed LSTM ...

The technique of Dynamic Programming (DP) could be described “recursion turned upside-down”. However, it is not usually used as an alternative to recursion. Rather, dynamic programming is used (if possible) for cases when a recurrence for an algorithmic problem will not run in polynomial-time if it is implemented recursively. So in fact Dynamic Programming is a morepowerful technique than basic...

Dynamic Programming: In this lecture we begin our coverage of an important algorithm design technique, called dynamic programming (or DP for short). The technique is among the most powerful for designing algorithms for optimization problems. Dynamic programming is a powerful technique for solving optimization problems that have certain well-defined clean structural properties. (The meaning of t...

The Markov decision process (MDP) based methodology is implemented in order to establish the optimal schedule which minimizes the cost. Formulation of MDP problem is presented using the information about the current state of pipe, improvement cost, failure cost and pipe deterioration model. The objective function and detailed algorithm of dynamic programming (DP) are modified due to the difficu...

In this paper we develop a dynamic programming algorithm to compute the exact posterior probabilities of ancestor relations in Bayesian networks. Previous dynamic programming (DP) algorithm by (Parviainen and Koivisto, 2011) evaluates all possible ancestor relations in time O(n3) and space O(3). However, their algorithm assumes an order-modular prior over DAGs that does not respect Markov equiv...

Ambiguity in dynamic programming arises from two independent sources, the non-uniqueness of optimal solutions and the particular recursion scheme by which the search space is evaluated. Ambiguity, unless explicitly considered, leads to unnecessarily complicated, in exible, and sometimes even incorrect dynamic programming algorithms. Building upon the recently developed algebraic approach to dyn...

various methods have been used to economically design wastewater and stormwater collection systems. in this article, discrete differential dynamic programming (dddp) which is a subsection of dynamic programming (dp) was used. for each pipe network, various alternatives such as depth and slope of pipe laying and various diameters could be considered to satisfy hydraulic and engineering constrain...