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• How to investigate if opencl is possible for an algorithm

  - by Marnix

  I have a heavy-duty algorithm in C# that takes two large Bitmaps of about 10000x5000 and performs photo and ray collision operations on a 3D model to map photos on the 3D model. I would like to know if it is possible to convert such an algorithm to OpenCL to optimize parallel operations during the algorithm. But before asking you to go into the details of the algorithm, I would like to know how I can investigate if my algorithm is convertible to OpenCL. I am not experienced in OpenCL and I would like to know if it is worth it to get into it and learn how it works. Are there things I have to look for that will definitely not work on the graphics card? (for-loops, recursion) Update: My algorithm goes something like: foreach photo split the photo in 64x64 blocks foreach block cast a ray from the camera to the 3D model foreach triangle in 3D model perform raycheck

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• GRAPH PROBLEM: find an algorithm to determine the shortest path from one point to another in a recta

  - by newba

  I'm getting such an headache trying to elaborate an appropriate algorithm to go from a START position to a EXIT position in a maze. For what is worth, the maze is rectangular, maxsize 500x500 and, in theory, is resolvable by DFS with some branch and bound techniques ... 10 3 4 7 6 3 3 1 2 2 1 0 2 2 2 4 2 2 5 2 2 1 3 0 2 2 2 2 1 3 3 4 2 3 4 4 3 1 1 3 1 2 2 4 2 2 1 Output: 5 1 4 2 Explanation: Our agent looses energy every time he gives a step and he can only move UP, DOWN, LEFT and RIGHT. Also, if the agent arrives with a remaining energy of zero or less, he dies, so we print something like "Impossible". So, in the input 10 is the initial agent's energy, 3 4 is the START position (i.e. column 3, line 4) and we have a maze 7x6. Think this as a kind of labyrinth, in which I want to find the exit that gives the agent a better remaining energy (shortest path). In case there are paths which lead to the same remaining energy, we choose the one which has the small number of steps, of course. I need to know if a DFS to a maze 500x500 in the worst case is feasible with these limitations and how to do it, storing the remaining energy in each step and the number of steps taken so far. The output means the agent arrived with remaining energy= 5 to the exit pos 1 4 in 2 steps. If we look carefully, in this maze it's also possible to exit at pos 3 1 (column 3, row 1) with the same energy but with 3 steps, so we choose the better one. With these in mind, can someone help me some code or pseudo-code? I have troubles working this around with a 2D array and how to store the remaining energy, the path (or number of steps taken)....

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• Fast block placement algorithm, advice needed?

  - by James Morris

  I need to emulate the window placement strategy of the Fluxbox window manager. As a rough guide, visualize randomly sized windows filling up the screen one at a time, where the rough size of each results in an average of 80 windows on screen without any window overlapping another. It is important to note that windows will close and the space that closed windows previously occupied becomes available once more for the placement of new windows. The window placement strategy has three binary options: Windows build horizontal rows or vertical columns (potentially) Windows are placed from left to right or right to left Windows are placed from top to bottom or bottom to top Why is the algorithm a problem? It needs to operate to the deadlines of a real time thread in an audio application. At this moment I am only concerned with getting a fast algorithm, don't concern yourself over the implications of real time threads and all the hurdles in programming that that brings. So far I have two choices which I have built loose prototypes for: 1) A port of the Fluxbox placement algorithm into my code. The problem with this is, the client (my program) gets kicked out of the audio server (JACK) when I try placing the worst case scenario of 256 blocks using the algorithm. This algorithm performs over 14000 full (linear) scans of the list of blocks already placed when placing the 256th window. 2) My alternative approach. Only partially implemented, this approach uses a data structure for each area of rectangular free unused space (the list of windows can be entirely separate, and is not required for testing of this algorithm). The data structure acts as a node in a doubly linked list (with sorted insertion), as well as containing the coordinates of the top-left corner, and the width and height. Furthermore, each block data structure also contains four links which connect to each immediately adjacent (touching) block on each of the four sides. IMPORTANT RULE: Each block may only touch with one block per side. The problem with this approach is, it's very complex. I have implemented the straightforward cases where 1) space is removed from one corner of a block, 2) splitting neighbouring blocks so that the IMPORTANT RULE is adhered to. The less straightforward case, where the space to be removed can only be found within a column or row of boxes, is only partially implemented - if one of the blocks to be removed is an exact fit for width (ie column) or height (ie row) then problems occur. And don't even mention the fact this only checks columns one box wide, and rows one box tall. I've implemented this algorithm in C - the language I am using for this project (I've not used C++ for a few years and am uncomfortable using it after having focused all my attention to C development, it's a hobby). The implementation is 700+ lines of code (including plenty of blank lines, brace lines, comments etc). The implementation only works for the horizontal-rows + left-right + top-bottom placement strategy. So I've either got to add some way of making this +700 lines of code work for the other 7 placement strategy options, or I'm going to have to duplicate those +700 lines of code for the other seven options. Neither of these is attractive, the first, because the existing code is complex enough, the second, because of bloat. The algorithm is not even at a stage where I can use it in the real time worst case scenario, because of missing functionality, so I still don't know if it actually performs better or worse than the first approach. What else is there? I've skimmed over and discounted: Bin Packing algorithms: their emphasis on optimal fit does not match the requirements of this algorithm. Recursive Bisection Placement algorithms: sounds promising, but these are for circuit design. Their emphasis is optimal wire length. Both of these, especially the latter, all elements to be placed/packs are known before the algorithm begins. I need an algorithm which works accumulatively with what it is given to do when it is told to do it. What are your thoughts on this? How would you approach it? What other algorithms should I look at? Or even what concepts should I research seeing as I've not studied computer science/software engineering? Please ask questions in comments if further information is needed. [edit] If it makes any difference, the units for the coordinates will not be pixels. The units are unimportant, but the grid where windows/blocks/whatever can be placed will be 127 x 127 units.

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• Are evolutionary algorithms and neural networks used in the same problem domains?

  - by Joe Holloway

  I am trying to get a feel for the difference between the various classes of machine-learning algorithms. I understand that the implementations of evolutionary algorithms are quite different from the implementations of neural networks. However, they both seem to be geared at determining a correlation between inputs and outputs from a potentially noisy set of training/historical data. From a qualitative perspective, are there problem domains that are better targets for neural networks as opposed to evolutionary algorithms? I've skimmed some articles that suggest using them in a complementary fashion. Is there a decent example of a use case for that? Thanks

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• Genetic Programming in C#

  - by Mac

  I've been looking for some good genetic programming examples for C#. Anyone knows of good online/book resources? Wonder if there is a C# library out there for Evolutionary/Genetic programming?

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• Algorithm design, "randomising" timetable schedule in Python although open to other languages.

  - by S1syphus

  Before I start I should add I am a musician and not a native programmer, this was undertook to make my life easier. Here is the situation, at work I'm given a new csv file each which contains a list of sound files, their length, and the minimum total amount of time they must be played. I create a playlist of exactly 60 minutes, from this excel file. Each sample played the by the minimum number of instances, but spread out from each other; so there will never be a period where for where one sound is played twice in a row or in close proximity to itself. Secondly, if the minimum instances of each song has been used, and there is still time with in the 60 min, it needs to fill the remaining time using sounds till 60 minutes is reached, while adhering to above. The smallest duration possible is 15 seconds, and then multiples of 15 seconds. Here is what I came up with in python and the problems I'm having with it, and as one user said its buggy due to the random library used in it. So I'm guessing a total rethink is on the table, here is where I need your help. Whats is the best way to solve the issue, I have had a brief look at things like knapsack and bin packing algorithms, while both are relevant neither are appropriate and maybe a bit beyond me.

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• Diff Algorithm

  - by Daniel Magliola

  I've been looking like crazy for an explanation of a diff algorithm that works and is efficient. The closest I got is this link to RFC 3284 (from several Eric Sink blog posts), which describes in perfectly understandable terms the data format in which the diff results are stored. However, it has no mention whatsoever as to how a program would reach these results while doing a diff. I'm trying to research this out of personal curiosity, because I'm sure there must be tradeoffs when implementing a diff algorithm, which are pretty clear sometimes when you look at diffs and wonder "why did the diff program chose this as a change instead of that?"... Does anyone know where I can find a description of an efficient algorithm that'd end up outputting VCDIFF? By the way, if you happen to find a description of the actual algorithm used by SourceGear's DiffMerge, that'd be even better. NOTE: longest common subsequence doesn't seem to be the algorithm used by VCDIFF, it looks like they're doing something smarter, given the data format they use. Thanks!

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• problem with evolutionary algorithms degrading into simulated annealing: mutation too small?

  - by Schnalle

  i have a problem understanding evolutionary algorithms. i tried using this technique several times, but i always ran into the same problem: degeneration into simulated annealing. lets say my initial population, with fitness in brackets, is: A (7), B (9), C (14), D (19) after mating and mutation i have following children: AB (8.3), AC (12.2), AD (14.1), BC(11), BD (14.7), CD (17) after elimination of the weakest, we get A, AB, B, AC next turn, AB will mate again with a result around 8, pushing AC out. next turn, AB again, pushing B out (assuming mutation changes fitness mostly in the 1 range). now, after only a few turns the pool is populated with the originally fittest candidates (A, B) and mutations of those two (AB). this happens regardless of the size of the initial pool, it just takes a bit longer. say, with an initial population of 50 it takes 50 turns, then all others are eliminated, turning the whole setup in a more complicated simulated annealing. in the beginning i also mated canditates with themselves, worsening the problem. so, what do i miss? are my mutation rates simply too small and will it go away if i increase them? here's the project i'm using it for: http://stefan.schallerl.com/simuan-grid-grad/ yeah, the code is buggy and the interface sucks, but i'm too lazy to fix it right now - and be careful, it may lock up your browser. better use chrome, even thought firefox is not slower than chrome for once (probably the tracing for the image comparison pays off, yay!). if anyone is interested, the code can be found here. here i just dropped the ev-alg idea and went for simulated annealing. ps: i'm not even sure about simulated annealing - it is like evolutionary algorithms, just with a population size of one, right?

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• Algorithm to reduce a bitmap mask to a list of rectangles?

  - by mos

  Before I go spend an afternoon writing this myself, I thought I'd ask if there was an implementation already available --even just as a reference. The first image is an example of a bitmap mask that I would like to turn into a list of rectangles. A bad algorithm would return every set pixel as a 1x1 rectangle. A good algorithm would look like the second image, where it returns the coordinates of the orange and red rectangles. The fact that the rectangles overlap don't matter, just that there are only two returned. To summarize, the ideal result would be these two rectangles (x, y, w, h): [ { 3, 1, 2, 6 }, { 1, 3, 6, 2 } ]

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• Why hill climbing is called anytime algorithm?

  - by crucified soul

  From wikipedia, Anytime algorithm In computer science an anytime algorithm is an algorithm that can return a valid solution to a problem even if it's interrupted at any time before it ends. The algorithm is expected to find better and better solutions the more time it keeps running. Hill climbing Hill climbing can often produce a better result than other algorithms when the amount of time available to perform a search is limited, such as with real-time systems. It is an anytime algorithm: it can return a valid solution even if it's interrupted at any time before it ends. Hill climbing algorithm can stuck into local optima or ridge, after that even if it runs infinite time, the result won't be any better. Then, why hill climbing is called anytime algorithm?

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• Dijkstra’s algorithm and functions

  - by baris_a

  Hi guys, the question is: suppose I have an input function like sin(2-cos(3*A/B)^2.5)+0.756*(C*D+3-B) specified with a BNF, I will parse input using recursive descent algorithm, and then how can I use or change Dijkstra’s algorithm to handle this given function? After parsing this input function, I need to execute it with variable inputs, where Dijkstra’s algorithm should do the work. Thanks in advance. EDIT: May be I should ask also: What is the best practice or data structure to represent given function?

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• Efficient algorithm for finding largest eigenpair of small general complex matrix

  - by mklassen

  I am looking for an efficient algorithm to find the largest eigenpair of a small, general (non-square, non-sparse, non-symmetric), complex matrix, A, of size m x n. By small I mean m and n is typically between 4 and 64 and usually around 16, but with m not equal to n. This problem is straight forward to solve with the general LAPACK SVD algorithms, i.e. gesvd or gesdd. However, as I am solving millions of these problems and only require the largest eigenpair, I am looking for a more efficient algorithm. Additionally, in my application the eigenvectors will generally be similar for all cases. This lead me to investigate Arnoldi iteration based methods, but I have neither found a good library nor algorithm that applies to my small general complex matrix. Is there an appropriate algorithm and/or library?

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• Algorithm for Text Wrapping Within a Shape

  - by devongovett

  I am looking for an algorithm to wrap text within a non-rectangular shape, preferably based on the Knuth and Plass algorithm. The hardest part of this is that the lines may have different heights due to differing font sizes in the text. The image below is an example of what the algorithm should be able to generate. Thanks for any help!

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• Clustering [assessment] algorithm with distance matrix as an input

  - by Max

  Can anyone suggest some clustering algorithm which can work with distance matrix as an input? Or the algorithm which can assess the "goodness" of the clustering also based on the distance matrix? At this moment I'm using a modification of Kruskal's algorithm (http://en.wikipedia.org/wiki/Kruskal%27s_algorithm) to split data into two clusters. It has a problem though. When the data has no distinct clusters the algorithm will still create two clusters with one cluster containing one element and the other containing all the rest. In this case I would rather have one cluster containing all the elements and another one which is empty. Are there any algorithms which are capable of doing this type of clustering? Are there any algorithms which can estimate how well the clustering was done or even better how many clusters are there in the data? The algorithms should work only with distance(similarity) matrices as an input.

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• Dijkstra's algorithm: why does it work? (not how)

  - by BeeBand

  I understand what Dijkstra's algorithm is but I don't understand why it works. When selecting the next vertice to examine, why does Dijkstra's algorithm select the one with the smallest weight? Why not just select a vertex arbitrarily, since the algorithm visits all vertices anyway?

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• Algorithm for matching partially filled words

  - by adnanhb

  Hello All, I am writing a game which when given a partially filled word, searches a dictionary and returns all the matching words. To that effect, I am trying to find an algorithm that can be used for the said purpose. For example, given - - a -, the algorithm will search a dictionary for all the words which have length 4 and have 'a' as the third letter. Is there such an algorithm already? If not, can somebody given a rough idea of how to design such an algorithm? Thanks in Advance.

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• What are The opencv function useful for 2d skeleton estimation(ofRecursive centroids) [closed]

  - by kumar

  Possible Duplicate: Which is the best algorithm to “Estimate and Visulize 2d skeleton using Opencv” from the drawn contour Opencv functions or stepts for 2d skeleton estimation

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• isometric drawing order with larger than single tile images - drawing order algorithm?

  - by Roger Smith

  I have an isometric map over which I place various images. Most images will fit over a single tile, but some images are slightly larger. For example, I have a bed of size 2x3 tiles. This creates a problem when drawing my objects to the screen as I get some tiles erroneously overlapping other tiles. The two solutions that I know of are either splitting the image into 1x1 tile segments or implementing my own draw order algorithm, for example by assigning each image a number. The image with number 1 is drawn first, then 2, 3 etc. Does anyone have advice on what I should do? It seems to me like splitting an isometric image is very non obvious. How do you decide which parts of the image are 'in' a particular tile? I can't afford to split up all of my images manually either. The draw order algorithm seems like a nicer choice but I am not sure if it's going to be easy to implement. I can't solve, in my head, how to deal with situations whereby you change the index of one image, which causes a knock on effect to many other images. If anyone has an resources/tutorials on this I would be most grateful.

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• What algorithm to use to fill a KenKen square board with cages?

  - by JimmyBoh

  I am working on recreating KenKen, a popular math puzzle involving a blank grid that is divided into "cages". Each cage is just a collection of adjacent squares and has a clue which is generally a number and an operand, shown below: What type of algorithm would be best to fill the square with cages? Assume the maximum number of cells per cage would be 3 and the board is 4x4 in size, like in the example above.

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• Lawler's Algorithm Implementation Assistance

  - by Richard Knop

  Here is my implemenation of Lawler's algorithm in PHP (I know... but I'm used to it): <?php $jobs = array(1, 2, 3, 4, 5, 6); $jobsSubset = array(2, 5, 6); $n = count($jobs); $processingTimes = array(2, 3, 4, 3, 2, 1); $dueDates = array(3, 15, 9, 7, 11, 20); $optimalSchedule = array(); foreach ($jobs as $j) { $optimalSchedule[] = 0; } $dicreasedCardinality = array(); for ($i = $n; $i >= 1; $i--) { $x = 0; $max = 0; // loop through all jobs for ($j = 0; $j < $i; $j++) { // ignore if $j already is in the $dicreasedCardinality array if (false === in_array($j, $dicreasedCardinality)) { // if the job has no succesor in $jobsSubset if (false === isset($jobs[$j+1]) || false === in_array($jobs[$j+1], $jobsSubset)) { // here I find an array index of a job with the maximum due date // amongst jobs with no sucessor in $jobsSubset if ($x < $dueDates[$j]) { $x = $dueDates[$j]; $max = $j; } } } } // move the job at the end of $optimalSchedule $optimalSchedule[$i-1] = $jobs[$max]; // decrease the cardinality of $jobs $dicreasedCardinality[] = $max; } print_r($optimalSchedule); Now the above returns an optimal schedule like this: Array ( [0] => 1 [1] => 1 [2] => 1 [3] => 3 [4] => 2 [5] => 6 ) Which doesn't seem right to me. The problem might be with my implementation of the algorithm because I am not sure I understand it correctly. I used this source to implement it: http://www.google.com/books?id=aSiBs6PDm9AC&pg=PA166&dq=lawler%27s+algorithm+code&lr=&hl=sk&cd=4#v=onepage&q=&f=false The description there is a little confusing. For example, I didn't quite get how is the subset D defined (I guess it is arbitrary). Could anyone help me out with this? I have been trying to find some sources with simpler explanation of the algorithm but all sources I found were even more complicated (with math proofs and such) so I am stuck with the link above. Yes, this is a homework, if it wasn't obvious. I still have few weeks to crack this but I have spent few days already trying to get how exactly this algorithm works with no success so I don't think I will get any brighter during that time.

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• Shuffling algorithm with no "self-mapping"?

  - by OregonTrail

  To randomly shuffle an array, with no bias towards any particular permutation, there is the Knuth Fischer-Yeats algorithm. In Python: #!/usr/bin/env python import sys from random import randrange def KFYShuffle(items): i = len(items) - 1 while i > 0: j = randrange(i+1) # 0 <= j <= i items[j], items[i] = items[i], items[j] i = i - 1 return items print KFYShuffle(range(int(sys.argv[1]))) There is also Sattolo's algorithm, which produces random cycles. In Python: #!/usr/bin/env python import sys from random import randrange def SattoloShuffle(items): i = len(items) while i > 1: i = i - 1 j = randrange(i) # 0 <= j <= i-1 items[j], items[i] = items[i], items[j] return items print SattoloShuffle(range(int(sys.argv[1]))) I'm currently writing a simulation with the following specifications for a shuffling algorithm: The algorithm is unbiased. If a true random number generator was used, no permutation would be more likely than any other. No number ends up at its original index. The input to the shuffle will always be A[i] = i for i from 0 to N-1 Permutations are produced that are not cycles, but still meet specification 2. The cycles produced by Sattolo's algorithm meet specification 2, but not specification 1 or 3. I've been working at creating an algorithm that meets these specifications, what I came up with was equivalent to Sattolo's algorithm. Does anyone have an algorithm for this problem?

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• An implementation of Sharir's or Aurenhammer's deterministic algorithm for calculating the intersect

  - by RGrey

  The problem of finding the intersection/union of 'N' discs/circles on a flat plane was first proposed by M. I. Shamos in his 1978 thesis: Shamos, M. I. “Computational Geometry” Ph.D. thesis, Yale Univ., New Haven, CT 1978. Since then, in 1985, Micha Sharir presented an O(n log2n) time and O(n) space deterministic algorithm for the disc intersection/union problem (based on modified Voronoi diagrams): Sharir, M. Intersection and closest-pair problems for a set of planar discs. SIAM .J Comput. 14 (1985), pp. 448-468. In 1988, Franz Aurenhammer presented a more efficient O(n log n) time and O(n) space algorithm for circle intersection/union using power diagrams (generalizations of Voronoi diagrams): Aurenhammer, F. Improved algorithms for discs and balls using power diagrams. Journal of Algorithms 9 (1985), pp. 151-161. Earlier in 1983, Paul G. Spirakis also presented an O(n^2) time deterministic algorithm, and an O(n) probabilistic algorithm: Spirakis, P.G. Very Fast Algorithms for the Area of the Union of Many Circles. Rep. 98, Dept. Comput. Sci., Courant Institute, New York University, 1983. I've been searching for any implementations of the algorithms above, focusing on computational geometry packages, and I haven't found anything yet. As neither appear trivial to put into practice, it would be really neat if someone could point me in the right direction!

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• average case running time of linear search algorithm

  - by Brahadeesh

  Hi all. I am trying to derive the average case running time for deterministic linear search algorithm. The algorithm searches an element x in an unsorted array A in the order A[1], A[2], A[3]...A[n]. It stops when it finds the element x or proceeds until it reaches the end of the array. I searched on wikipedia and the answer given was (n+1)/(k+1) where k is the number of times x is present in the array. I approached in another way and am getting a different answer. Can anyone please give me the correct proof and also let me know whats wrong with my method? E(T)= 1*P(1) + 2*P(2) + 3*P(3) ....+ n*P(n) where P(i) is the probability that the algorithm runs for 'i' time (i.e. compares 'i' elements). P(i)= (n-i)C(k-1) * (n-k)! / n! Here, (n-i)C(k-1) is (n-i) Choose (k-1). As the algorithm has reached the ith step, the rest of k-1 x's must be in the last n-i elements. Hence (n-i)C(k-i). (n-k)! is the total number of ways of arranging the rest non x numbers, and n! is the total number of ways of arranging the n elements in the array. I am not getting (n+1)/(k+1) on simplifying.

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• Adaptive user interface/environment algorithm

  - by WowtaH

  Hi all, I'm working on an information system (in C#) that (while my users use it) gathers statistical data on what pieces of information (tables & records) each user is requesting the most, and what parts of the interface he/she uses most. I'm using this statistical data to make the application adaptive to the user's needs, both in the way the interface presents itself (eg: tab/pane-ordering) as in the way of using the frequently viewed information to (eg:) show higher in search results/suggestion-lists. What i'm looking for is an algorithm/formula to determine the current 'hotness'/relevance of these objects for a specific user. A simple 'hitcounter' for each object won't be sufficient because the user might view some information quite frequently for a period of time, and then moving on to the next, making the old information less relevant. So i think my algorithm also needs some sort of sliding/historical principle to account for the changing popularity of the objects in the application over time. So, the question is: Does anybody have some sort of algorithm that accounts for that 'popularity over time' ? Preferably with some explanation on the parameters :) Thanks! PS I've looked at other posts like http://stackoverflow.com/questions/32397/popularity-algorithm but i could't quite port it to my specific case. Any help is appreciated.

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• Algorithm for creating a school timetable.

  - by cand

  Hello all. I've been wondering if there are known solutions for algorithm of creating a school timetable. Basically, it's about optimizing "hour-dispersion" (both in teachers and classes case) for given class-subject-teacher associations. We can assume that we have sets of classes, lesson subjects and teachers associated with each other at the input and that timetable should fit between 8AM and 4PM. I guess that there is probably no accurate algorithm for that, but maybe someone knows a good approximation or hints for developing it. P.S. I know, there was http://stackoverflow.com/questions/1259686/school-timetable-generation-algorithm-closed , but unlike in that case, I'm not looking for actual implementation, rather for thoughts on how to do it or why it's impossible.

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