what is the Hungarian algorithm?
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The Hungarian algorithm is a combinatorial optimization algorithm which solves assignment problems in polynomial time. The first version, known as the Hungarian method, was invented and published by Harold Kuhn in 1955. This was revised by James Munkres in 1957, and has been known since as the Hungarian algorithm, the Munkres assignment algorithm, or the Kuhn-Munkres algorithm.\n\nTheory\nThe algorithm developed by Kuhn was largely based on the earlier works of two other Hungarian mathematicians: Denes Kőnig and Jenő Egerváry. The great advantage of Kuhn’s method is that it is strongly polynomial. The main idea of the algorithm is that it combines two separate parts in Egervery’s proof (computing a deficient set and revising the current π) into one. The procedure of covering the prime zeros can be implemented in a flow network, and automated by means of the Ford-Fulkerson algorithm. The n×m matrix is transformed in a G = (U,V) bipartite graph, with I/O capacity equal to 1. Each arc joining the nodes of the flow network represents a zero in the cost matrix. After the maximum flow is obtained, the affected arcs represent the prime zeros. The minimum cut obtained by Ford-Fulkerson automates the process of covering the independent zeros. Each node that gets "cut" on the max-flow graph represents a covered row or line on the cost matrix. However, in the Hungarian method, the assignment can be easily found on the cost matrix, yet the expansion into a max-flow sub-problem is a useful method in more complex scenarios.\n\nModeling\nThe algorithm models an assignment problem as a n×m cost matrix, where each element represents the cost of assigning the nth worker to the mth job. By default, the algorithm performs minimization on the elements of the matrix; hence in the case of a price-minimization problem, it is sufficient to begin Gaussian elimination to make zeros appear (at least one zero per line and per column). However, in the case of a profit-maximization problem, the cost matrix needs to be modified so that minimization of its elements results in maximizing the original cost values. In an infinite-cost problem, the initial cost matrix can be re-modelled by subtracting every element of each line from the maximum value of the element that line (or column respectively). In a finite-cost problem, all the elements are subtracted from the maximum value of the whole matrix.\n\nfor more details see link:\nhttp://en.wikipedia.org/wiki/Hungarian_algorithm
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It is a strings proccessing alg which uses a failure function to improve the run time rather than bm alg\nrun time:m+n\nm:the length of the pattern\nn: the length of the text\nwe use tis algorithm to find the pat string in the text string..\nmore details \nmail me at ziko@math.com\ni can provid a pascal source for it
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This graph helps us to show what happens when a quantity y is inversely proportional to another quantity x.\n\nAgain, it shows us that a computer algorithm implementing y=(1/x) for the order of efficiency of the algorithm..
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google search of  prolog nim found a PROLOG implementation of the nim game at the following website.\n\nI look at it and it is in English, but I did not run it. I dont have PROLOG installed.
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Lattis, or Lattice. I'm not sure of the spelling.
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I am going to answer this assuming a literal question.\n\nIn other words, I assume that you are interested in the efficiency of an *algorithm*, not the speed of a given algorithm running on a given processor having been compiled by a given compiler.\n\nIn this context, you need to learn how to compute algorithm complexity using o-notation:\n\nhttp://en.wikipedia.org/wiki/Big_o_notation\n\nThis is not a simple concept to describe in a 5 minute discussion. Perhaps you are best off looking into some algorithms textbooks or taking an undergraduate course in algorithms.\n\nBasically what this technique does is study roughly how long an algorithm will take when the size of data approaches infinity. This gives you an idea what to expect. For instance, if you double the number of numbers going into an algorithm, does the amount of time double? Does it less than than double? Does it more than double? All three answers are possible, depending on the chosen algorithm.\n\nA good place to start to investigate this is to look at sorting techniques. They have been extensively analyzed for complexity, and there are a number of good sources.\n\nHope this helps.
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http://en.wikipedia.org/wiki/Booth%27s_algorithm\nBooth's multiplication algorithm will multiply two signed numbers in two's complement notation
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I know a little Java...\n\nIf you are trying to create a compression algorithm, then good luck with the Math, its BSc/Masters level.\n\nTo compress/decompress (once seperate algorithms are made) just a few methods are needed, the main to decide what to do, one for compress and one to unpack.\n\nSorry can't be more specific at this time, I'll look into it.
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Convex Hull: Quick Hull Algorithm\nhttp://nereida.deioc.ull.es/html/casiano/qhull.html\n\nVoronoi: Boundary Extraction\nhttp://cgm.cs.mcgill.ca/~godfried/teaching/projects.pr.98/sergei/project.html
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Details of the algorithm for conversion along with\nsource program written in C can be found at.\nhttp://www.codepedia.com/1/Art_Expressions_p1\n\nA good short article on this topic.\nhttp://www.cs.man.ac.uk/~pjj/cs2121/fix.html
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not that I know...you might have to wright your own algorithm!\n\nouch...have fun!\n\nbut check links!!
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Use any recursive algorithm.\n\nFor instance:\n\nf(x) = f(x) + 1
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docking\nDocking is most pertinent to the field of drug design—most drugs are small molecules, and using a computational approach allows researchers to quickly screen large databases of potential drugs (e.g., the ZINC database of compounds for virtual screening) against protein targets such as HIV reverse transcriptase\n\nalgorithm\nIn mathematics and computing, an algorithm is a procedure (a finite set of well-defined instructions) for accomplishing some task which, given an initial state, will terminate in a defined end-state. The computational complexity and efficient implementation of the algorithm are important in computing, and this depends on suitable data structures.\n\nInformally, the concept of an algorithm is often illustrated by the example of a recipe, although many algorithms are much more complex; algorithms often have steps that repeat (iterate) or require decisions (such as logic or comparison). In most higher level programs, algorithms act in complex patterns, each using smaller and smaller sub-methods which are built up to the program as a whole. In most languages, they are isomorphic to functions or methods.\n\nThe concept of an algorithm originated as a means of recording procedures for solving mathematical problems such as finding the common divisor of two numbers or multiplying two numbers. The concept was formalized in 1936 through Alan Turing's Turing machines and Alonzo Church's lambda calculus, which in turn formed the foundation of computer science.\n\nMost algorithms can be directly implemented by computer programs; any other algorithms can at least in theory be simulated by computer programs.
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Yes:  theory of computational systems.
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The problem with calculating running time of an algorithm is that there are so many variables which affect running time (far beyond just cpu speed and RAM size).  It is impossible to come up with a accurate calculation from these variables.\n\nBig O is useful because it calculates relative effeciency, regardless of hardware outside conditions, and size of test data.\n\nIn order to come up with actual numbers, the ONLY accurate way is to actually run the algorithm and time it.
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Its of or relating to the computer language, prolog, which is commonly used for AI programs.  Its a very flexible language.
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What do you want to solve for? x? y? or do you want an ordered pair (x,y) that works (integers maybe?)