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  • optimized grid for rectangular items

    - by peterchen
    I have N rectangular items with an aspect ratio Aitem (X:Y). I have a rectangular display area with an aspect ratio Aview The items should be arranged in a table-like layout (i.e. r rows, c columns). what is the ideal grid rows x columns, so that individual items are largest? (rows * colums = N, of course - i.e. there may be "unused" grid places). A simple algorithm could iterate over rows = 1..N, calculate the required number of columns, and keep the row/column pair with the largest items. I wonder if there's a non-iterative algorithm, though (e.g. for Aitem = Aview = 1, rows / cols can be approximated by sqrt(N)).

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  • Divide and Conquer Algo to find maximum difference between two ordered elements

    - by instance
    Given an array arr[] of integers, find out the difference between any two elements such that larger element appears after the smaller number in arr[]. Examples: If array is [2, 3, 10, 6, 4, 8, 1, 7] then returned value should be 8 (Diff between 10 and 2). If array is [ 7, 9, 5, 6, 3, 2 ] then returned value should be 2 (Diff between 7 and 9) My Algorithm: I thought of using D&C algorithm. Explanation 2, 3, 10, 6, 4, 8, 1, 7 then 2,3,10,6 and 4,8,1,7 then 2,3 and 10,6 and 4,8 and 1,7 then 2 and 3 10 and 6 4 and 8 1 and 7 Here as these elements will remain in same order, i will get the maximum difference, here it's 6. Now i will move back to merege these arrays and again find the difference between minimum of first block and maximum of second block and keep doing this till end. I am not able to implement this in my code. can anyone please provide a pseudo code for this?

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  • Getting the most frequent items without counting every item

    - by DeadMonkeyWalkin
    Hi. I was wondering if there was an algorithm for counting "most frequent items" without having to keep a count of each item? For example, let's say I was a search engine and wanted to keep track of the 10 most popular searches. What I don't want to do is keep a counter of every query since there could be too many queries for me to count (and most them will be singletons). Is there a simple algorithm for this? Maybe something that is probabilistic? Thanks!

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  • Balanced Search Tree Query, Asymtotic Analysis..

    - by AGeek
    Hi, The situation is as follows:- We have n number and we have print them in sorted order. We have access to balanced dictionary data structure, which supports the operations serach, insert, delete, minimum, maximum each in O(log n) time. We want to retrieve the numbers in sorted order in O(n log n) time using only the insert and in-order traversal. The answer to this is:- Sort() initialize(t) while(not EOF) read(x) insert(x,t); Traverse(t); Now the query is if we read the elements in time "n" and then traverse the elements in "log n"(in-order traversal) time,, then the total time for this algorithm (n+logn)time, according to me.. Please explain the follow up of this algorithm for the time calculation.. How it will sort the list in O(nlogn) time?? Thanks.

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  • Construct A Polygon Out of Union of Many Polygons

    - by Ngu Soon Hui
    Supposed that I have many polygons, what is the best algorithm to construct a polygon--maybe with holes- out of the union of all those polygons? For my purpose, you can imagine each piece of a polygon as a jigsaw puzzle piece, when you complete them you will get a nice picture. But the catch is that a small portion <5% of the jigsaw is missing, and you are still require to form a picture as complete as possible; that's the polygon-- maybe with holes-- that I want to form. My naive approach is to take two polygons, union them, and take another polygon, union it with the union of the two polygons, and repeat this process until every single piece is union. Then I will run through the union polygon list and check whether there are still some polygons can be combined, and I will repeat this process until a satisfactory result is achieved. But this seems to be like an extremely naive approach. I just wonder is there any other better algorithm?

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  • Will Algorithm written in OCaml compiled from C be Faster than Algorithm written in Pure C code?

    - by Ole Jak
    So I have some cool Image Processing algorithm. I have written it in OCaml. It performs well. I now I can compile it as C code with such command ocamlc -output-obj -o foo.c foo.ml (I have a situation where I am not alowed to use OCaml compiler to bild my programm for my arcetecture, I can use only specialy modified gcc. so I will compile that programm with sometyhing like gcc -L/usr/lib/ocaml foo.c -lcamlrun -lm -lncurses and Itll run on my archetecture.) I want to know in general case will my OCaml code compiled into C run faster than algorithm implemented in pure C?

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  • (Python) algorithm to randomly select a key based on proportionality/weight

    - by LaundroMat
    Hi - I'm a bit at a loss as to how to find a clean algorithm for doing the following: Suppose I have a dict k: >>> k = {'A': 68, 'B': 62, 'C': 47, 'D': 16, 'E': 81} I now want to randomly select one of these keys, based on the 'weight' they have in the total (i.e. sum) amount of keys. >>> sum(k.values()) >>> 274 So that there's a >>> 68.0/274.0 >>> 0.24817518248175183 24.81% percent change that A is selected. How would you write an algorithm that takes care of this? In other words, that makes sure that on 10.000 random picks, A will be selected 2.481 times?

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  • How can I compute the average cost for this solution of the element uniqueness problem?

    - by Alceu Costa
    In the book Introduction to the Design & Analysis of Algorithms, the following solution is proposed to the element uniqueness problem: ALGORITHM UniqueElements(A[0 .. n-1]) // Determines whether all the elements in a given array are distinct // Input: An array A[0 .. n-1] // Output: Returns "true" if all the elements in A are distinct // and false otherwise. for i := 0 to n - 2 do for j := i + 1 to n - 1 do if A[i] = A[j] return false return true How can I compute the average cost (i.e. number of comparisons for a given n) for this algorithm? What is a reasonable assumption about the input?

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  • Autoclick security for a like button

    - by Ali Davut
    Hi everyone I want to develop a button like 'facebook like button'. I am going to use it on my website and thinking it to share as iframe like facebook but I cannot think its securty because someone can develop a script that can click on it automatically. I thought a solution using sessions but I couldn't make an algorithm completely. How can I disallow autoclicks and which solution is the best? It can be any language I just want algorithm. Thanks, have a nice day.

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  • Please explain how Trial Division works for Primality Test

    - by mister_dani
    I came across this algorithm for testing primality through trial division I fully understand this algorithm static boolean isPrime(int N) { if (N < 2) return false; for (int i = 2; i <= Math.sqrt(N); i++) if (N % i == 0) return false; return true; } It works just fine. But then I came across this other one which works just as good but I do not fully understand the logic behind it. static boolean isPrime(int N) { if (N < 2) return false; for (int i = 2; i * i<N; i++) if (N % i == 0) return false; return true; } It seems like i *i < N behaves like i <= Math.sqrt(N). If so, why?

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  • Small questions on data structure

    - by John Graveston
    Hi, I'm trying to search the parent of a node with Kruskal's algorithm. My program works just fine, but I think I have heard of a method to improve the speed of the algorithm by reconstructing the tree while searching for the parent node and connecting it to the parent node. I'm pretty sure that I've heard of this somewhere, maybe in a lecture. Can anyone refresh my memory? And also, given a number of arrays, when searching for the minimum and the maximum value from a certain section of an array, what is the name of the tree that can calculate the minimum/maximum value from the array by making a binary tree that has the minimum/maximum value of each array in O(log N)?

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  • question on revrse array

    - by davit-datuashvili
    we know algorithm how reverse array of n integers for (int i=0;i<n/2;i++){ swap(a[i],a[n-1-i]): } is this method better according the speed of algorithm or not because swap using xor is more fast then in other method here is code public class swap{ public static void main(String[]args){ int a[]=new int[]{2,4,5,7,8,11,13,12,14,24}; System.out.println(" array at the begining:"); for (int i=0;i<a.length;i++){ System.out.println(a[i]); } for (int j=0;j<a.length/2;j++){ a[j]^=a[a.length-1-j]; a[a.length-1-j]^=a[j]; a[j]^=a[a.length-1-j]; } System.out.println("reversed array:"); for (int j=0;j<a.length;j++){ System.out.println(a[j]); } } } //result array at the begining: 2 4 5 7 8 11 13 12 14 24 reversed array: 24 14 12 13 11 8 7 5 4 2

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  • Compare two integer arrays with same length

    - by meta
    [Description] Given two integer arrays with the same length. Design an algorithm which can judge whether they're the same, the definition of "same" is that, if these two arrays are in sorted order, the elements in corresponding position should be the same. [Example] <1 2 3 4> = <3 1 2 4> <1 2 3 4> != <3 4 1 1> [Limitation] The algorithm should require constant extra space, and O(n) running time.

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  • file names based on file content

    - by Mark
    So iow, some algorithm to generate a unique, reasonable length filename based on binary file content. Two files that have the same binary content should have the same name. Obviously there would be limits to this, as presumably you couldn't have unique reasonable length filenames for each of a large set of large files only differing at a handful of bit positions. But presumably there is some heuristic, best approximation to this that for example exploits known attributes of typical image files. If I had the name of some algorithm that does this I can google it and find other approaches as well.

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  • question on reverse array

    - by davit-datuashvili
    we know algorithm how reverse array of n integers for (int i=0;i<n/2;i++){ swap(a[i],a[n-1-i]): } is this method better according the speed of algorithm or not because swap using xor is more fast then in other method here is code public class swap { public static void main(String[]args){ int a[]=new int[]{2,4,5,7,8,11,13,12,14,24}; System.out.println(" array at the begining:"); for (int i=0;i<a.length;i++){ System.out.println(a[i]); } for (int j=0;j<a.length/2;j++){ a[j]^=a[a.length-1-j]; a[a.length-1-j]^=a[j]; a[j]^=a[a.length-1-j]; } System.out.println("reversed array:"); for (int j=0;j<a.length;j++){ System.out.println(a[j]); } } } Result: array at the begining: 2 4 5 7 8 11 13 12 14 24 reversed array: 24 14 12 13 11 8 7 5 4 2

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  • C# Improved algorithm

    - by generixs
    I have been asked at interview (C# 3.0) to provide a logic to remove a list of items from a list. I responded int[] items={1,2,3,4}; List<int> newList = new List<int>() { 1, 2, 3, 4, 5, 56, 788, 9 }; newList.RemoveAll((int i) => { return items.Contains(i); }); 1) The interviewer replied that the algorithm i had employed will gradually take time if the items grow and asked me to give even better and faster one.What would be the efficient algorithm ? 2) How can i achieve the same using LINQ? 3) He asked me to provide an example for Two-Way-Closure? (General I am aware of closure, what is Two-Way-Closure?, I replied there is no such term exists,but he did not satisfy).

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  • How can I estimate the entropy of a password?

    - by Wug
    Having read various resources about password strength I'm trying to create an algorithm that will provide a rough estimation of how much entropy a password has. I'm trying to create an algorithm that's as comprehensive as possible. At this point I only have pseudocode, but the algorithm covers the following: password length repeated characters patterns (logical) different character spaces (LC, UC, Numeric, Special, Extended) dictionary attacks It does NOT cover the following, and SHOULD cover it WELL (though not perfectly): ordering (passwords can be strictly ordered by output of this algorithm) patterns (spatial) Can anyone provide some insight on what this algorithm might be weak to? Specifically, can anyone think of situations where feeding a password to the algorithm would OVERESTIMATE its strength? Underestimations are less of an issue. The algorithm: // the password to test password = ? length = length(password) // unique character counts from password (duplicates discarded) uqlca = number of unique lowercase alphabetic characters in password uquca = number of uppercase alphabetic characters uqd = number of unique digits uqsp = number of unique special characters (anything with a key on the keyboard) uqxc = number of unique special special characters (alt codes, extended-ascii stuff) // algorithm parameters, total sizes of alphabet spaces Nlca = total possible number of lowercase letters (26) Nuca = total uppercase letters (26) Nd = total digits (10) Nsp = total special characters (32 or something) Nxc = total extended ascii characters that dont fit into other categorys (idk, 50?) // algorithm parameters, pw strength growth rates as percentages (per character) flca = entropy growth factor for lowercase letters (.25 is probably a good value) fuca = EGF for uppercase letters (.4 is probably good) fd = EGF for digits (.4 is probably good) fsp = EGF for special chars (.5 is probably good) fxc = EGF for extended ascii chars (.75 is probably good) // repetition factors. few unique letters == low factor, many unique == high rflca = (1 - (1 - flca) ^ uqlca) rfuca = (1 - (1 - fuca) ^ uquca) rfd = (1 - (1 - fd ) ^ uqd ) rfsp = (1 - (1 - fsp ) ^ uqsp ) rfxc = (1 - (1 - fxc ) ^ uqxc ) // digit strengths strength = ( rflca * Nlca + rfuca * Nuca + rfd * Nd + rfsp * Nsp + rfxc * Nxc ) ^ length entropybits = log_base_2(strength) A few inputs and their desired and actual entropy_bits outputs: INPUT DESIRED ACTUAL aaa very pathetic 8.1 aaaaaaaaa pathetic 24.7 abcdefghi weak 31.2 H0ley$Mol3y_ strong 72.2 s^fU¬5ü;y34G< wtf 88.9 [a^36]* pathetic 97.2 [a^20]A[a^15]* strong 146.8 xkcd1** medium 79.3 xkcd2** wtf 160.5 * these 2 passwords use shortened notation, where [a^N] expands to N a's. ** xkcd1 = "Tr0ub4dor&3", xkcd2 = "correct horse battery staple" The algorithm does realize (correctly) that increasing the alphabet size (even by one digit) vastly strengthens long passwords, as shown by the difference in entropy_bits for the 6th and 7th passwords, which both consist of 36 a's, but the second's 21st a is capitalized. However, they do not account for the fact that having a password of 36 a's is not a good idea, it's easily broken with a weak password cracker (and anyone who watches you type it will see it) and the algorithm doesn't reflect that. It does, however, reflect the fact that xkcd1 is a weak password compared to xkcd2, despite having greater complexity density (is this even a thing?). How can I improve this algorithm? Addendum 1 Dictionary attacks and pattern based attacks seem to be the big thing, so I'll take a stab at addressing those. I could perform a comprehensive search through the password for words from a word list and replace words with tokens unique to the words they represent. Word-tokens would then be treated as characters and have their own weight system, and would add their own weights to the password. I'd need a few new algorithm parameters (I'll call them lw, Nw ~= 2^11, fw ~= .5, and rfw) and I'd factor the weight into the password as I would any of the other weights. This word search could be specially modified to match both lowercase and uppercase letters as well as common character substitutions, like that of E with 3. If I didn't add extra weight to such matched words, the algorithm would underestimate their strength by a bit or two per word, which is OK. Otherwise, a general rule would be, for each non-perfect character match, give the word a bonus bit. I could then perform simple pattern checks, such as searches for runs of repeated characters and derivative tests (take the difference between each character), which would identify patterns such as 'aaaaa' and '12345', and replace each detected pattern with a pattern token, unique to the pattern and length. The algorithmic parameters (specifically, entropy per pattern) could be generated on the fly based on the pattern. At this point, I'd take the length of the password. Each word token and pattern token would count as one character; each token would replace the characters they symbolically represented. I made up some sort of pattern notation, but it includes the pattern length l, the pattern order o, and the base element b. This information could be used to compute some arbitrary weight for each pattern. I'd do something better in actual code. Modified Example: Password: 1234kitty$$$$$herpderp Tokenized: 1 2 3 4 k i t t y $ $ $ $ $ h e r p d e r p Words Filtered: 1 2 3 4 @W5783 $ $ $ $ $ @W9001 @W9002 Patterns Filtered: @P[l=4,o=1,b='1'] @W5783 @P[l=5,o=0,b='$'] @W9001 @W9002 Breakdown: 3 small, unique words and 2 patterns Entropy: about 45 bits, as per modified algorithm Password: correcthorsebatterystaple Tokenized: c o r r e c t h o r s e b a t t e r y s t a p l e Words Filtered: @W6783 @W7923 @W1535 @W2285 Breakdown: 4 small, unique words and no patterns Entropy: 43 bits, as per modified algorithm The exact semantics of how entropy is calculated from patterns is up for discussion. I was thinking something like: entropy(b) * l * (o + 1) // o will be either zero or one The modified algorithm would find flaws with and reduce the strength of each password in the original table, with the exception of s^fU¬5ü;y34G<, which contains no words or patterns.

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  • How to generate a number in arbitrary range using random()={0..1} preserving uniformness and density?

    - by psihodelia
    Generate a random number in range [x..y] where x and y are any arbitrary floating point numbers. Use function random(), which returns a random floating point number in range [0..1] from P uniformly distributed numbers (call it "density"). Uniform distribution must be preserved and P must be scaled as well. I think, there is no easy solution for such problem. To simplify it a bit, I ask you how to generate a number in interval [-0.5 .. 0.5], then in [0 .. 2], then in [-2 .. 0], preserving uniformness and density? Thus, for [0 .. 2] it must generate a random number from P*2 uniformly distributed numbers. The obvious simple solution random() * (x - y) + y will generate not all possible numbers because of the lower density for all abs(x-y)>1.0 cases. Many possible values will be missed. Remember, that random() returns only a number from P possible numbers. Then, if you multiply such number by Q, it will give you only one of P possible values, scaled by Q, but you have to scale density P by Q as well.

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  • Building a new cluster for mathematical calculations (Win/Lin)

    - by Muhammad Farhan
    I would like to build a new cluster to perform heavy mathematical calculations in Matlab and Abaqus. One of my friend told me that distributed computing is way faster than parallel computing, which is very true after reading a bit on the internet. However, I have never clustered before. Current workstation I own: Dell Precision T5400 2 x Intel Xeon 2.5 GHz 16 GB RAM (2GB x 8) 1 x Western Digital 1TB HDD 7200 rpm 1 x nVidia Quadro FX4600 768MB GPU 1 x 870W PSU OS: Windows 7 Ultimate 64-bit 2nd WS: I can buy another WS similar configuration to the one I own I am not bothered about OS, I am willing to cluster with either Windows or Linux. However, my software are compatible with windows 64-bit only. Please help me setup a cluster. Thank you.

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  • Fault tolerance with a pair of tightly coupled services

    - by cogitor
    I have two tightly coupled services that can run on completely different nodes (e.g. ServiceA and ServiceB). If I start up another replicated copy of both these services for backup purposes (ServiceA-2 and ServiceB-2), what would be the best way of setting up a fault tolerant distributed system such that on a fault in any of the tightly coupled services ServiceA or ServiceB the whole communication should go through backup ServiceA-2 and ServiceB-2? Overall, all the communication should go either through both services or their backup replicas. |---- Service A | | Service B | | (backup branch - used only on fault in Service A or B) ---- Service A-2 | Service B-2 Note that in case that Service A goes down, data from Service B would be incorrect (and vice versa). Load balancing between the primary and backup branch is also not feasible.

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