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Matches in DBpedia 2015-04 for { <http://dbpedia.org/resource/Dynamic_programming> ?p ?o }

• Dynamic_programming abstract "In mathematics, computer science, economics, and bioinformatics, dynamic programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems. It is applicable to problems exhibiting the properties of overlapping subproblems and optimal substructure (described below). When applicable, the method takes far less time than naive methods that don't take advantage of the subproblem overlap (like depth-first search).In order to solve a given problem, using a dynamic programming approach, we need to solve different parts of the problem (subproblems), then combine the solutions of the subproblems to reach an overall solution. Often when using a more naive method, many of the subproblems are generated and solved many times. The dynamic programming approach seeks to solve each subproblem only once, thus reducing the number of computations: once the solution to a given subproblem has been computed, it is stored or "memo-ized": the next time the same solution is needed, it is simply looked up. This approach is especially useful when the number of repeating subproblems grows exponentially as a function of the size of the input.Dynamic programming algorithms are used for optimization (for example, finding the shortest path between two points, or the fastest way to multiply many matrices). A dynamic programming algorithm will examine the previously solved subproblems and will combine their solutions to give the best solution for the given problem. The alternatives are many, such as using a greedy algorithm, which picks the locally optimal choice at each branch in the road. The locally optimal choice may be a poor choice for the overall solution. While a greedy algorithm does not guarantee an optimal solution, it is often faster to calculate. Fortunately, some greedy algorithms (such as minimum spanning trees) are proven to lead to the optimal solution.For example, let's say that you have to get from point A to point B as fast as possible, in a given city, during rush hour. A dynamic programming algorithm will look at finding the shortest paths to points close to A, and use those solutions to eventually find the shortest path to B. On the other hand, a greedy algorithm will start you driving immediately and will pick the road that looks the fastest at every intersection. As you can imagine, this strategy might not lead to the fastest arrival time, since you might take some "easy" streets and then find yourself hopelessly stuck in a traffic jam.Sometimes, applying memoization to a naive basic recursive solution already results in an optimal dynamic programming solution; however, many problems require more sophisticated dynamic programming algorithms. Some of these may be recursive as well but parametrized differently from the naive solution. Others can be more complicated and cannot be implemented as a recursive function with memoization. Examples of these are the two solutions to the Egg Dropping puzzle below.".
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• Dynamic_programming wikiPageExternalLink introduction-to-dynamic-programming.
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• Dynamic_programming wikiPageExternalLink 230.pdf.
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• Dynamic_programming wikiPageExternalLink 1526-5463-2002-50-01-0048.pdf.
• Dynamic_programming wikiPageExternalLink www.probp.com.
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• Dynamic_programming subject Category:Dynamic_programming.
• Dynamic_programming subject Category:Equations.
• Dynamic_programming subject Category:Mathematical_optimization.
• Dynamic_programming subject Category:Operations_research.
• Dynamic_programming subject Category:Optimal_control.
• Dynamic_programming subject Category:Optimization_algorithms_and_methods.
• Dynamic_programming subject Category:Systems_engineering.
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• Dynamic_programming comment "In mathematics, computer science, economics, and bioinformatics, dynamic programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems. It is applicable to problems exhibiting the properties of overlapping subproblems and optimal substructure (described below).".
• Dynamic_programming label "Dynamic programming".
• Dynamic_programming label "Dynamické programování".
• Dynamic_programming label "Dynamische Programmierung".
• Dynamic_programming label "Programació dinàmica".
• Dynamic_programming label "Programación dinámica".
• Dynamic_programming label "Programação dinâmica".
• Dynamic_programming label "Programmation dynamique".
• Dynamic_programming label "Programmazione dinamica".
• Dynamic_programming label "Programowanie dynamiczne".
• Dynamic_programming label "Динамическое программирование".
• Dynamic_programming label "動的計画法".
• Dynamic_programming label "동적 계획법".
• Dynamic_programming differentFrom Dynamic_programming_language.
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• Dynamic_programming sameAs Programmazione_dinamica.
• Dynamic_programming sameAs 動的計画法.
• Dynamic_programming sameAs 동적_계획법.
• Dynamic_programming sameAs Programowanie_dynamiczne.
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