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  • How to minimize total cost of shortest path tree

    - by Michael
    I have a directed acyclic graph with positive edge-weights. It has a single source and a set of targets (vertices furthest from the source). I find the shortest paths from the source to each target. Some of these paths overlap. What I want is a shortest path tree which minimizes the total sum of weights over all edges. For example, consider two of the targets. Given all edge weights equal, if they share a single shortest path for most of their length, then that is preferable to two mostly non-overlapping shortest paths (fewer edges in the tree equals lower overall cost). Another example: two paths are non-overlapping for a small part of their length, with high cost for the non-overlapping paths, but low cost for the long shared path (low combined cost). On the other hand, two paths are non-overlapping for most of their length, with low costs for the non-overlapping paths, but high cost for the short shared path (also, low combined cost). There are many combinations. I want to find solutions with the lowest overall cost, given all the shortest paths from source to target. Does this ring any bells with anyone? Can anyone point me to relevant algorithms or analogous applications? Cheers!

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  • Can Haskell's Parsec library be used to implement a recursive descent parser with backup?

    - by Thor Thurn
    I've been considering using Haskell's Parsec parsing library to parse a subset of Java as a recursive descent parser as an alternative to more traditional parser-generator solutions like Happy. Parsec seems very easy to use, and parse speed is definitely not a factor for me. I'm wondering, though, if it's possible to implement "backup" with Parsec, a technique which finds the correct production to use by trying each one in turn. For a simple example, consider the very start of the JLS Java grammar: Literal: IntegerLiteral FloatingPointLiteral I'd like a way to not have to figure out how I should order these two rules to get the parse to succeed. As it stands, a naive implementation like this: literal = do { x <- try (do { v <- integer; return (IntLiteral v)}) <|> (do { v <- float; return (FPLiteral v)}); return(Literal x) } Will not work... inputs like "15.2" will cause the integer parser to succeed first, and then the whole thing will choke on the "." symbol. In this case, of course, it's obvious that you can solve the problem by re-ordering the two productions. In the general case, though, finding things like this is going to be a nightmare, and it's very likely that I'll miss some cases. Ideally, I'd like a way to have Parsec figure out stuff like this for me. Is this possible, or am I simply trying to do too much with the library? The Parsec documentation claims that it can "parse context-sensitive, infinite look-ahead grammars", so it seems like something like I should be able to do something here.

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  • Is information a subset of data?

    - by Jason Baker
    I apologize as I don't know whether this is more of a math question that belongs on mathoverflow or if it's a computer science question that belongs here. That said, I believe I understand the fundamental difference between data, information, and knowledge. My understanding is that information carries both data and meaning. One thing that I'm not clear on is whether information is data. Is information considered a special kind of data, or is it something completely different?

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  • Find all cycles in graph, redux

    - by Shadow
    Hi, I know there are a quite some answers existing on this question. However, I found none of them really bringing it to the point. Some argue that a cycle is (almost) the same as a strongly connected components (s. http://stackoverflow.com/questions/546655/finding-all-cycles-in-graph/549402#549402) , so one could use algorithms designed for that goal. Some argue that finding a cycle can be done via DFS and checking for back-edges (s. boost graph documentation on file dependencies). I now would like to have some suggestions on whether all cycles in a graph can be detected via DFS and checking for back-edges? My opinion is that it indeed could work that way as DFS-VISIT (s. pseudocode of DFS) freshly enters each node that was not yet visited. In that sense, each vertex exhibits a potential start of a cycle. Additionally, as DFS visits each edge once, each edge leading to the starting point of a cycle is also covered. Thus, by using DFS and back-edge checking it should indeed be possible to detect all cycles in a graph. Note that, if cycles with different numbers of participant nodes exist (e.g. triangles, rectangles etc.), additional work has to be done to discriminate the acutal "shape" of each cycle.

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  • Find all complete sub-graphs within a graph

    - by mvid
    Is there a known algorithm or method to find all complete sub-graphs within a graph? I have an undirected, unweighted graph and I need to find all subgraphs within it where each node in the subgraph is connected to each other node in the subgraph. Is there an existing algorithm for this?

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  • How do I compute the approximate entropy of a bit string?

    - by dreeves
    Is there a standard way to do this? Googling -- "approximate entropy" bits -- uncovers multiple academic papers but I'd like to just find a chunk of pseudocode defining the approximate entropy for a given bit string of arbitrary length. (In case this is easier said than done and it depends on the application, my application involves 16,320 bits of encrypted data (cyphertext). But encrypted as a puzzle and not meant to be impossible to crack. I thought I'd first check the entropy but couldn't easily find a good definition of such. So it seemed like a question that ought to be on StackOverflow! Ideas for where to begin with de-cyphering 16k random-seeming bits are also welcome...) See also this related question: http://stackoverflow.com/questions/510412/what-is-the-computer-science-definition-of-entropy

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  • What are logical and path queries

    - by NomeN
    I'm reading a paper which mentions that a language for refactoring has three specific requirements. functional features (like ML) logical queries (like Datalog) path queries (like Datalog) I know what they mean by functional features, but I'm not totally clear on the latter two and can't find a clear explanation either. Although I have a good idea after what I could find on the subjects, I need to be sure so here goes: Could the SO-community please clearly explain to me what logical queries and path queries are? Or at the very least what the people from the paper meant?

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  • Computationally simple Pseudo-Gaussian Distribution with varying mean and standard deviation?

    - by mstksg
    This picture from wikipedia has a nice example of the sort of functions I'd ideally like to generate http://en.wikipedia.org/wiki/File:Normal_Distribution_PDF.svg Right now I'm using the Irwin-Hall Distribution, which is more or less a Polynomial approximation of the Gaussian distribution...basically, you use uniform random number generator and iterate it x times, and take the average. The more iterations, the more like a Gaussian Distribution it is. It's pretty nice; however I'd like to be able to have one where I can vary the mean. For example, let's say I wanted a number between the range 0 and 10, but around 7. Like, the mean (if I repeated this function multiple times) would turn out to be 7, but the actual range is 0-10. Is there one I should look up, or should I work on doing some fancy maths with standard Gaussian Distributions?

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  • Gaining information from nodes of tree

    - by jainp
    I am working with the tree data structure and trying to come up with a way to calculate information I can gain from the nodes of the tree. I am wondering if there are any existing techniques which can assign higher numerical importance to a node which appears less frequently at lower level (Distance from the root of the tree) than the same nodes appearance at higher level and high frequency. To give an example, I want to give more significance to node Book, at level 2 appearing once, then at level 3 appearing thrice. Will appreciate any suggestions/pointers to techniques which achieve something similar. Thanks, Prateek

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  • curious ill conditioned numerical problem

    - by aaa
    hello. somebody today showed me this curious ill conditioned problem (apparently pretty famous), which looks relatively simple ƒ = (333.75 - a^2)b^6 + a^2 (11a^2 b^2 - 121b^4 - 2) + 5.5b^8 + a/(2^b) where a = 77617 and b = 33096 can you determine correct answer?

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  • What is an XYZ-complete problem?

    - by TheMachineCharmer
    EDIT: Diagram: http://www.cs.umass.edu/~immerman/complexity_theory.html There must be some meaning to the word "complete" its used every now and then. Look at the diagram. I tried reading previous posts about NP- My question is what does the word "COMPLETE" mean? Why is it there? What is its significance? N- Non-deterministic - makes sense' P- Polynomial - makes sense but the "COMPLETE" is still a mystery for me.

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  • count of distinct acyclic paths from A[a,b] to A[c,d]?

    - by Sorush Rabiee
    I'm writing a sokoban solver for fun and practice, it uses a simple algorithm (something like BFS with a bit of difference). now i want to estimate its running time ( O and omega). but need to know how to calculate count of acyclic paths from a vertex to another in a network. actually I want an expression that calculates count of valid paths, between two vertices of a m*n matrix of vertices. a valid path: visits each vertex 0 or one times. have no circuits for example this is a valid path: but this is not: What is needed is a method to find count of all acyclic paths between the two vertices a and b. comments on solving methods and tricks are welcomed.

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  • What exactly are administrative redexes after CPS conversion?

    - by eljenso
    In the context of Scheme and CPS conversion, I'm having a little trouble deciding what administrative redexes (lambdas) exactly are: all the lambda expressions that are introduced by the CPS conversion only the lambda expressions that are introduced by the CPS conversion but you wouldn't have written if you did the conversion "by hand" or through a smarter CPS-converter If possible, a good reference would be welcome.

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  • Create My own language with "Functional Programming Language"

    - by esehara
    I prefer Haskell. I already know How to create my own language with Procedural Language (for example: C, Java, Python, etc). But, I know How to create my own language with Functional Language (for example Haskell, Clojure and Scala). I've already read: Internet Resources Write Yourself a Scheme in 48 Hours Real World Haskell - Chapter 16.Using Persec Writing A Lisp Interpreter In Haskell Parsec, a fast combinator parser Implementing functional languages: a tutorial Books Introduction Functional Programming Using Haskell 2nd Edition -- Haskell StackOverflow (but with procedural language) Learning to write a compiler create my own programming language Source Libraries and tools/HJS -- Haskell Are there any other good sources? I wants to get more links,or sources.

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  • Finding the heaviest length-constrained path in a weighted Binary Tree

    - by Hristo
    UPDATE I worked out an algorithm that I think runs in O(n*k) running time. Below is the pseudo-code: routine heaviestKPath( T, k ) // create 2D matrix with n rows and k columns with each element = -8 // we make it size k+1 because the 0th column must be all 0s for a later // function to work properly and simplicity in our algorithm matrix = new array[ T.getVertexCount() ][ k + 1 ] (-8); // set all elements in the first column of this matrix = 0 matrix[ n ][ 0 ] = 0; // fill our matrix by traversing the tree traverseToFillMatrix( T.root, k ); // consider a path that would arc over a node globalMaxWeight = -8; findArcs( T.root, k ); return globalMaxWeight end routine // node = the current node; k = the path length; node.lc = node’s left child; // node.rc = node’s right child; node.idx = node’s index (row) in the matrix; // node.lc.wt/node.rc.wt = weight of the edge to left/right child; routine traverseToFillMatrix( node, k ) if (node == null) return; traverseToFillMatrix(node.lc, k ); // recurse left traverseToFillMatrix(node.rc, k ); // recurse right // in the case that a left/right child doesn’t exist, or both, // let’s assume the code is smart enough to handle these cases matrix[ node.idx ][ 1 ] = max( node.lc.wt, node.rc.wt ); for i = 2 to k { // max returns the heavier of the 2 paths matrix[node.idx][i] = max( matrix[node.lc.idx][i-1] + node.lc.wt, matrix[node.rc.idx][i-1] + node.rc.wt); } end routine // node = the current node, k = the path length routine findArcs( node, k ) if (node == null) return; nodeMax = matrix[node.idx][k]; longPath = path[node.idx][k]; i = 1; j = k-1; while ( i+j == k AND i < k ) { left = node.lc.wt + matrix[node.lc.idx][i-1]; right = node.rc.wt + matrix[node.rc.idx][j-1]; if ( left + right > nodeMax ) { nodeMax = left + right; } i++; j--; } // if this node’s max weight is larger than the global max weight, update if ( globalMaxWeight < nodeMax ) { globalMaxWeight = nodeMax; } findArcs( node.lc, k ); // recurse left findArcs( node.rc, k ); // recurse right end routine Let me know what you think. Feedback is welcome. I think have come up with two naive algorithms that find the heaviest length-constrained path in a weighted Binary Tree. Firstly, the description of the algorithm is as follows: given an n-vertex Binary Tree with weighted edges and some value k, find the heaviest path of length k. For both algorithms, I'll need a reference to all vertices so I'll just do a simple traversal of the Tree to have a reference to all vertices, with each vertex having a reference to its left, right, and parent nodes in the tree. Algorithm 1 For this algorithm, I'm basically planning on running DFS from each node in the Tree, with consideration to the fixed path length. In addition, since the path I'm looking for has the potential of going from left subtree to root to right subtree, I will have to consider 3 choices at each node. But this will result in a O(n*3^k) algorithm and I don't like that. Algorithm 2 I'm essentially thinking about using a modified version of Dijkstra's Algorithm in order to consider a fixed path length. Since I'm looking for heaviest and Dijkstra's Algorithm finds the lightest, I'm planning on negating all edge weights before starting the traversal. Actually... this doesn't make sense since I'd have to run Dijkstra's on each node and that doesn't seem very efficient much better than the above algorithm. So I guess my main questions are several. Firstly, do the algorithms I've described above solve the problem at hand? I'm not totally certain the Dijkstra's version will work as Dijkstra's is meant for positive edge values. Now, I am sure there exist more clever/efficient algorithms for this... what is a better algorithm? I've read about "Using spine decompositions to efficiently solve the length-constrained heaviest path problem for trees" but that is really complicated and I don't understand it at all. Are there other algorithms that tackle this problem, maybe not as efficiently as spine decomposition but easier to understand? Thanks.

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  • What does the q in a q-grammar stand for?

    - by Aru
    So I've been reading sites and the classic books on compilers, reading about s-grammar and q-grammars I wondered what the s and q stand for, I think the s stands for simple grammar. While the q...well, I have no idea. What does the q in a q-grammar stand for?

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  • What is a data structure for quickly finding non-empty intersections of a list of sets?

    - by Andrey Fedorov
    I have a set of N items, which are sets of integers, let's assume it's ordered and call it I[1..N]. Given a candidate set, I need to find the subset of I which have non-empty intersections with the candidate. So, for example, if: I = [{1,2}, {2,3}, {4,5}] I'm looking to define valid_items(items, candidate), such that: valid_items(I, {1}) == {1} valid_items(I, {2}) == {1, 2} valid_items(I, {3,4}) == {2, 3} I'm trying to optimize for one given set I and a variable candidate sets. Currently I am doing this by caching items_containing[n] = {the sets which contain n}. In the above example, that would be: items_containing = [{}, {1}, {1,2}, {2}, {3}, {3}] That is, 0 is contained in no items, 1 is contained in item 1, 2 is contained in itmes 1 and 2, 2 is contained in item 2, 3 is contained in item 2, and 4 and 5 are contained in item 3. That way, I can define valid_items(I, candidate) = union(items_containing[n] for n in candidate). Is there any more efficient data structure (of a reasonable size) for caching the result of this union? The obvious example of space 2^N is not acceptable, but N or N*log(N) would be.

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  • Git dirctet acyclic graph - children know their parents but not the other way around

    - by dayscott
    Git is implemented as a directed acyclic graph. Children know their parents but not the other way round. This makes sense because i can reach every commit only through a branch or a tag ( generally speaking through a reference). That's how i traverse the tree. What other reasons had the developers of Git to make "the children know their parents but not the other way around"?/ What are the key benefits of this?

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  • efficacy of register allocation algorithms!

    - by aksci
    i'm trying to do a research/project on register allocation using graph coloring where i am to test the efficiency of different optimizing register allocation algorithms in different scenarios. how do i start? what are the prerequisites and the grounds with which i can test them. what all algos can i use? thank you!

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  • Throughput measurements

    - by dotsid
    I wrote simple load testing tool for testing performance of Java modules. One problem I faced is algorithm of throughput measurements. Tests are executed in several thread (client configure how much times test should be repeated), and execution time is logged. So, when tests are finished we have following history: 4 test executions 2 threads 36ms overall time - idle * test execution 5ms 9ms 4ms 13ms T1 |-*****-*********-****-*************-| 3ms 6ms 7ms 11ms T2 |-***-******-*******-***********-----| <-----------------36ms---------------> For the moment I calculate throughput (per second) in a following way: 1000 / overallTime * threadCount. But there is problem. What if one thread will complete it's own tests more quickly (for whatever reason): 3ms 3ms 3ms 3ms T1 |-***-***-***-***----------------| 3ms 6ms 7ms 11ms T2 |-***-******-*******-***********-| <--------------32ms--------------> In this case actual throughput is much better because of measured throughput is bounded by the most slow thread. So, my question is how should I measure throughput of code execution in multithreaded environment.

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  • determine if intersection of a set with conjunction of two other sets is empty

    - by koen
    For any three given sets A, B and C: is there a way to determine (programmatically) whether there is an element of A that is part of the conjunction of B and C? example: A: all numbers greater than 3 B: all numbers lesser than 7 C: all numbers that equal 5 In this case there is an element in set A, being the number 5, that fits. I'm implementing this as specifications, so this numerical range is just an example. A, B, C could be anything.

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