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  • Which algorithms/data structures should I "recognize" and know by name?

    - by Earlz
    I'd like to consider myself a fairly experienced programmer. I've been programming for over 5 years now. My weak point though is terminology. I'm self-taught, so while I know how to program, I don't know some of the more formal aspects of computer science. So, what are practical algorithms/data structures that I could recognize and know by name? Note, I'm not asking for a book recommendation about implementing algorithms. I don't care about implementing them, I just want to be able to recognize when an algorithm/data structure would be a good solution to a problem. I'm asking more for a list of algorithms/data structures that I should "recognize". For instance, I know the solution to a problem like this: You manage a set of lockers labeled 0-999. People come to you to rent the locker and then come back to return the locker key. How would you build a piece of software to manage knowing which lockers are free and which are in used? The solution, would be a queue or stack. What I'm looking for are things like "in what situation should a B-Tree be used -- What search algorithm should be used here" etc. And maybe a quick introduction of how the more complex(but commonly used) data structures/algorithms work. I tried looking at Wikipedia's list of data structures and algorithms but I think that's a bit overkill. So I'm looking more for what are the essential things I should recognize?

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  • Would a professional, self taught programmer benefit from reading an algorithms book?

    - by user65483
    I'm a 100% self taught, professional programmer (I've worked at a few web startups and made a few independent games). I've read quite a few of the "essential" books (Clean Code, The Pragmatic Programmer, Code Complete, SICP, K&R). I'm considering reading Introduction to Algorithms. I've asked a few colleagues if reading it will improve my programming skills, and I got very mixed answers. A few said yes, a few said no, and a one said "only if you spend a lot of time implementing these algorithms" (I don't). So, I figured I'd ask Stack Exchange. Is it worth the time to read about algorithms if you're a professional programmer who seldom needs to use complex algorithms? For what it's worth, I have a strong mathematical background (have a 2 year degree in Mathematics; took Linear Algebra, Differential Equations, Calc I-III).

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  • Is there a 'design pattern' type listing of common algorithms?

    - by KevinM1
    Is there a 'design pattern' styled listing of common/popular algorithms anywhere? Specifically, something that has a similar format along the lines of: Algorithm Name: e.g., Quick Sort, Bubble Sort, etc. Problem: A description of the stereotypical problem the algorithm is supposed to address Description: Description of the solution Implementation: Code examples of the solution Big O Rating: Self-explanatory Similar Algorithms: Algorithms that address the same problem in different ways, or similar problems I really like the GoF design pattern listing style, and I think it would help me learn various algorithms better/easier if I could find a resource that was similar in terms of organization.

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  • Evolutionary Algorithms: Optimal Repopulation Breakdowns

    - by Brian MacKay
    It's really all in the title, but here's a breakdown for anyone who is interested in Evolutionary Algorithms: In an EA, the basic premise is that you randomly generate a certain number of organisms (which are really just sets of parameters), run them against a problem, and then let the top performers survive. You then repopulate with a combination of crossbreeds of the survivors, mutations of the survivors, and also a certain number of new random organisms. Do that several thousand times, and efficient organisms arise. Some people also do things like introduce multiple "islands" of organisms, which are seperate populations that are allowed to crossbreed once in awhile. So, my question is: what are the optimal repopulation percentages? I have been keeping the top 10% performers, and repopulating with 30% crossbreeds and 30% mutations. The remaining 30% is for new organisms. I have also tried out the multiple island theory, and I'm interested in your results on that as well. It is not lost on me that this is exactly the type of problem an EA could solve. Are you aware of anyone trying that? Thanks in advance!

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  • Novel fitness measure for evolutionary image matching simulation

    - by Nick Johnson
    I'm sure many people have already seen demos of using genetic algorithms to generate an image that matches a sample image. You start off with noise, and gradually it comes to resemble the target image more and more closely, until you have a more-or-less exact duplicate. All of the examples I've seen, however, use a fairly straightforward pixel-by-pixel comparison, resulting in a fairly predictable 'fade in' of the final image. What I'm looking for is something more novel: A fitness measure that comes closer to what we see as 'similar' than the naive approach. I don't have a specific result in mind - I'm just looking for something more 'interesting' than the default. Suggestions?

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  • Don Knuth and MMIXAL vs. Chuck Moore and Forth -- Algorithms and Ideal Machines -- was there cross-pollination / influence in their ideas / work?

    - by AKE
    Question: To what extent is it known (or believed) that Chuck Moore and Don Knuth had influence on each other's thoughts on ideal machines, or their work on algorithms? I'm interested in citations, interviews, articles, links, or any other sort of evidence. It could also be evidence of the form of A and B here suggest that Moore might have borrowed or influenced C and D from Knuth here, or vice versa. (Opinions are of course welcome, but references / links would be better!) Context: Until fairly recently, I have been primarily familiar with Knuth's work on algorithms and computing models, mostly through TAOCP but also through his interviews and other writings. However, the more I have been using Forth, the more I am struck by both the power of a stack-based machine model, and the way in which the spareness of the model makes fundamental algorithmic improvements more readily apparent. A lot of what Knuth has done in fundamental analysis of algorithms has, it seems to me, a very similar flavour, and I can easily imagine that in a parallel universe, Knuth might perhaps have chosen Forth as his computing model. That's the software / algorithms / programming side of things. When it comes to "ideal computing machines", Knuth in the 70s came up with the MIX computer model, and then, collaborating with designers of state-of-the-art RISC chips through the 90s, updated this with the modern MMIX model and its attendant assembly language MMIXAL. Meanwhile, Moore, having been using and refining Forth as a language, but using it on top of whatever processor happened to be in the computer he was programming, began to imagine a world in which the efficiency and value of stack-based programming were reflected in hardware. So he went on in the 80s to develop his own stack-based hardware chips, defining the term MISC (Minimal Instruction Set Computers) along the way, and ending up eventually with the first Forth chip, the MuP21. Both are brilliant men with keen insight into the art of programming and algorithms, and both work at the intersection between algorithms, programs, and bare metal hardware (i.e. hardware without the clutter of operating systems). Which leads me to the headlined question... Question:To what extent is it known (or believed) that Chuck Moore and Don Knuth had influence on each other's thoughts on ideal machines, or their work on algorithms?

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  • Genetic/Evolutionary algorithms and local minima/maxima problem

    - by el.gringogrande
    I have run across several posts and articles that suggests using things like simulated annealing to avoid the local minima/maxima problem. I don't understand why this would be necessary if you started out with a sufficiently large random population. Is it just another check to insure that the initial population was, in fact, sufficiently large and random? Or are those techniques just an alternative to producing a "good" initial population?

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  • Should I keep investing into data structures and algorithms?

    - by 4bu3li
    These days, I'm investing heavily in data structures and algorithms and trying to solve some programming puzzles. I'm trying to code and solve with Java and Clojure. Am I wasting my time? should I invest more in technologies and frameworks that I already know in order to gain deeper knowledge (the ins and the outs) and be able to code with them more quickly? By studying data structures and algorithms, am I going to become a better programmer or those subjects are only important during college years?

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  • Should I keep investing into data structures and algorithms?

    - by Chiron
    These days, I'm investing heavily in data structures and algorithms and trying to solve some programming puzzles. I'm trying to code and solve with Java and Clojure. Am I wasting my time? should I invest more in technologies and frameworks that I already know in order to gain deeper knowledge (the ins and the outs) and be able to code with them more quickly? By studying data structures and algorithms, am I going to become a better programmer or those subjects are only important during college years?

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  • Why is quicksort better than other sorting algorithms in practice?

    - by Raphael
    This is a repost of a question on cs.SE by Janoma. Full credits and spoils to him or cs.SE. In a standard algorithms course we are taught that quicksort is O(n log n) on average and O(n²) in the worst case. At the same time, other sorting algorithms are studied which are O(n log n) in the worst case (like mergesort and heapsort), and even linear time in the best case (like bubblesort) but with some additional needs of memory. After a quick glance at some more running times it is natural to say that quicksort should not be as efficient as others. Also, consider that students learn in basic programming courses that recursion is not really good in general because it could use too much memory, etc. Therefore (and even though this is not a real argument), this gives the idea that quicksort might not be really good because it is a recursive algorithm. Why, then, does quicksort outperform other sorting algorithms in practice? Does it have to do with the structure of real-world data? Does it have to do with the way memory works in computers? I know that some memories are way faster than others, but I don't know if that's the real reason for this counter-intuitive performance (when compared to theoretical estimates).

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  • Can anybody recommend an application for laying out algorithms on a mac? [closed]

    - by Scotty
    In my intro to software development class I'm using a program called raptor which really helps me when I'm mapping out semi-complex algorithms and programs. The thing that I like about raptor is that when I'm flowcharting an algorithm, I can run it like an actual program and raptor steps through the code block by block. Unfortunately, raptor is only available on windows and when I'm at home I spend most of my time on Mac OS X. Is there any programs available for mac that help flowchart and step through algoritms?

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  • Finding a problem in some task [closed]

    - by nagisa
    Recently I competed in nation wide programming contest finals. Not unexpectedly all problems were algorithmic. I lost (40 points out of 600. Winner got ~300). I know why I lost very well - I don't know how to find actual problem in those obfuscated tasks which are life-blood of every competition. I think that being self-taught and not well versed in algorithms got me too. As side effect of learning things myself I know how to search for information, however all I could find are couple questions about learning algorithms. For now I put Python Algorithms: Mastering Basic Algorithms in the Python Language and Analysis of Algorithms which I found in those questions to my "to read" list. That leaves my first problem of not knowing how to find a problem unsolved. Will that ability come with learning algorithms? Or does it need some special attention? Any suggestions are welcomed.

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  • Books on string algorithms

    - by Max
    There have been numerous posts on string algorithms: http://stackoverflow.com/questions/246961/algorithm-to-find-similar-text, http://stackoverflow.com/questions/451884/similar-string-algorithm, http://stackoverflow.com/questions/613133/efficient-string-matching-algorithm However, no general literature was mentioned. Could anyone recommend a book(s) that would thoroughly explore various string algorithms? The topic which is of special interest is approximate string matching [things like google-offered corrected search string variants :) ]. Thanks a lot for advice.

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  • C++ Tutorial: 10 New STL Algorithms That Will Make You A More Productive Developer

    Unquestionably, the most effective tool for a C++ programmer's productivity is the Standard library's rich collection of algorithms. In 2008, about 20 new algorithms were voted into the C++0x draft standard. These new algorithms let you among the rest copy n elements intuitively, perform set theory operations, and handle partitions conveniently. Find out how to use these algorithms to make your code more efficient and intuitive.

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  • Am I right about the differences between Floyd-Warshall, Dijkstra's and Bellman-Ford algorithms?

    - by Programming Noob
    I've been studying the three and I'm stating my inferences from them below. Could someone tell me if I have understood them accurately enough or not? Thank you. Dijkstra's algorithm is used only when you have a single source and you want to know the smallest path from one node to another, but fails in cases like this Floyd-Warshall's algorithm is used when any of all the nodes can be a source, so you want the shortest distance to reach any destination node from any source node. This only fails when there are negative cycles (this is the most important one. I mean, this is the one I'm least sure about:) 3.Bellman-Ford is used like Dijkstra's, when there is only one source. This can handle negative weights and its working is the same as Floyd-Warshall's except for one source, right? If you need to have a look, the corresponding algorithms are (courtesy Wikipedia): Bellman-Ford: procedure BellmanFord(list vertices, list edges, vertex source) // This implementation takes in a graph, represented as lists of vertices // and edges, and modifies the vertices so that their distance and // predecessor attributes store the shortest paths. // Step 1: initialize graph for each vertex v in vertices: if v is source then v.distance := 0 else v.distance := infinity v.predecessor := null // Step 2: relax edges repeatedly for i from 1 to size(vertices)-1: for each edge uv in edges: // uv is the edge from u to v u := uv.source v := uv.destination if u.distance + uv.weight < v.distance: v.distance := u.distance + uv.weight v.predecessor := u // Step 3: check for negative-weight cycles for each edge uv in edges: u := uv.source v := uv.destination if u.distance + uv.weight < v.distance: error "Graph contains a negative-weight cycle" Dijkstra: 1 function Dijkstra(Graph, source): 2 for each vertex v in Graph: // Initializations 3 dist[v] := infinity ; // Unknown distance function from 4 // source to v 5 previous[v] := undefined ; // Previous node in optimal path 6 // from source 7 8 dist[source] := 0 ; // Distance from source to source 9 Q := the set of all nodes in Graph ; // All nodes in the graph are 10 // unoptimized - thus are in Q 11 while Q is not empty: // The main loop 12 u := vertex in Q with smallest distance in dist[] ; // Start node in first case 13 if dist[u] = infinity: 14 break ; // all remaining vertices are 15 // inaccessible from source 16 17 remove u from Q ; 18 for each neighbor v of u: // where v has not yet been 19 removed from Q. 20 alt := dist[u] + dist_between(u, v) ; 21 if alt < dist[v]: // Relax (u,v,a) 22 dist[v] := alt ; 23 previous[v] := u ; 24 decrease-key v in Q; // Reorder v in the Queue 25 return dist; Floyd-Warshall: 1 /* Assume a function edgeCost(i,j) which returns the cost of the edge from i to j 2 (infinity if there is none). 3 Also assume that n is the number of vertices and edgeCost(i,i) = 0 4 */ 5 6 int path[][]; 7 /* A 2-dimensional matrix. At each step in the algorithm, path[i][j] is the shortest path 8 from i to j using intermediate vertices (1..k-1). Each path[i][j] is initialized to 9 edgeCost(i,j). 10 */ 11 12 procedure FloydWarshall () 13 for k := 1 to n 14 for i := 1 to n 15 for j := 1 to n 16 path[i][j] = min ( path[i][j], path[i][k]+path[k][j] );

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  • data structure algorithms for database searching

    - by Ygam
    I was used to the traditional way of doing database searching with the following using wildcards for term searches using where clause for specific data like addresses and names but at other times, I found these common methods to produce code that is so bloated, especially when it comes to complex searches. Are there algorithms out there that you use for complex database searching? I tried to look for some but had a hard time doing so. I stumbled accross the binary search but I can't find a use for it :(

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  • What algorithms can I use to detect if articles or posts are duplicates?

    - by michael
    I'm trying to detect if an article or forum post is a duplicate entry within the database. I've given this some thought, coming to the conclusion that someone who duplicate content will do so using one of the three (in descending difficult to detect): simple copy paste the whole text copy and paste parts of text merging it with their own copy an article from an external site and masquerade as their own Prepping Text For Analysis Basically any anomalies; the goal is to make the text as "pure" as possible. For more accurate results, the text is "standardized" by: Stripping duplicate white spaces and trimming leading and trailing. Newlines are standardized to \n. HTML tags are removed. Using a RegEx called Daring Fireball URLs are stripped. I use BB code in my application so that goes to. (ä)ccented and foreign (besides Enlgish) are converted to their non foreign form. I store information about each article in (1) statistics table and in (2) keywords table. (1) Statistics Table The following statistics are stored about the textual content (much like this post) text length letter count word count sentence count average words per sentence automated readability index gunning fog score For European languages Coleman-Liau and Automated Readability Index should be used as they do not use syllable counting, so should produce a reasonably accurate score. (2) Keywords Table The keywords are generated by excluding a huge list of stop words (common words), e.g., 'the', 'a', 'of', 'to', etc, etc. Sample Data text_length, 3963 letter_count, 3052 word_count, 684 sentence_count, 33 word_per_sentence, 21 gunning_fog, 11.5 auto_read_index, 9.9 keyword 1, killed keyword 2, officers keyword 3, police It should be noted that once an article gets updated all of the above statistics are regenerated and could be completely different values. How could I use the above information to detect if an article that's being published for the first time, is already existing within the database? I'm aware anything I'll design will not be perfect, the biggest risk being (1) Content that is not a duplicate will be flagged as duplicate (2) The system allows the duplicate content through. So the algorithm should generate a risk assessment number from 0 being no duplicate risk 5 being possible duplicate and 10 being duplicate. Anything above 5 then there's a good possibility that the content is duplicate. In this case the content could be flagged and linked to the article's that are possible duplicates and a human could decide whether to delete or allow. As I said before I'm storing keywords for the whole article, however I wonder if I could do the same on paragraph basis; this would also mean further separating my data in the DB but it would also make it easier for detecting (2) in my initial post. I'm thinking weighted average between the statistics, but in what order and what would be the consequences...

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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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  • What types of programming contest problems are there?

    - by Alex
    Basically, I want to make a great reference for use with programming contests that would have all of the algorithms that I can put together that I would need during a contest as well as sample useage for the code. I'm planning on making this into a sort of book that I could print off and take with me to competitions. I would like to do this rather than simply bringing other books (such as Algorithms books) because I think that I will learn a lot more by going over all of the algorithms myself as well as I would know exactly what I have in the book, making it more efficient to have and use. So, I've been doing research to determine what types of programming problems and algorithms are common on contests, and the only thing I can really find is this (which I have seen referenced a few times): Hal Burch conducted an analysis over spring break of 1999 and made an amazing discovery: there are only 16 types of programming contest problems! Furthermore, the top several comprise almost 80% of the problems seen at the IOI. Here they are: Dynamic Programming Greedy Complete Search Flood Fill Shortest Path Recursive Search Techniques Minimum Spanning Tree Knapsack Computational Geometry Network Flow Eulerian Path Two-Dimensional Convex Hull BigNums Heuristic Search Approximate Search Ad Hoc Problems The most challenging problems are Combination Problems which involve a loop (combinations, subsets, etc.) around one of the above algorithms - or even a loop of one algorithm with another inside it. These seem extraordinarily tricky to get right, even though conceptually they are ``obvious''. Now that's good and all, but that study was conducted in 1999, which was 13 years ago! One thing I know is that there are no BigNums problems any more (as Java has a BigInteger class, they have stopped making those problems). So, I'm wondering if anyone knows of any more recent studies of the types of problems that may be seen in a programming contest? Or what the most helpful algorithms on contests would be?

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  • Trust metrics and related algorithms

    - by Nick Gerakines
    I'm trying to learn more about trust metrics (including related algorithms) and how user voting, ranking and rating systems can be wired to stiffle abuse. I've read abstract articles and papers describing trust metrics but haven't seen any actual implementations. My goal is to create a system that allows users to vote on other users and the content of other users and with those votes and related meta-data, determine if those votes can be applied to a users level or popularity. Have you used or seen some sort of trust system within a social graph? How did it work and what were its areas of strength and weaknesses?

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  • Which web algorithms book to get ?

    - by fjxx
    I am currently undecided between which of the following web algorithms book to buy: 1) Algorithms of the Intelligent web by Marmanis 2) Collective Intelligence by Alag Both feature code in Java; Marmanis' book delves deeper into the core algorithms while Alag's book discusses more APIs including WEKA. I have already read Programming Collective Intelligence by Segaran and enjoyed it. Any comments on these books or any other recommendations are welcome.

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  • Which web algorithms book to get? [closed]

    - by fjxx
    I am currently undecided between which of the following web algorithms book to buy: 1) Algorithms of the Intelligent web by Marmanis 2) Collective Intelligence by Alag Both feature code in Java; Marmanis' book delves deeper into the core algorithms while Alag's book discusses more APIs including WEKA. I have already read Programming Collective Intelligence by Segaran and enjoyed it. Any comments on these books or any other recommendations are welcome.

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