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• Have I pushed the limits of my current VPS or is there room for optimization?

  - by JRameau

  I am currently on a mediatemple DV server (basic) 512mb dedicated ram, this is a CentOS based VPS with Plesk and Virtuozzo. My experience with it from day 1 has been bad and I only could sooth my server issues with several caching "Band-aids," but my sites are not as small as they were a year ago either so the issues have worsen. I have 3 Drupal installs running on separate (plesk) domains, 1 of those drupal installs is a multisite, that consists of 5-6 sites 2 of those sites are bringing in actual traffic. Those caching "Band-aids" I mentioned are APC, which seemed to help alot initially, and Drupal's Boost, which is considered a poorman's Varnish, it makes all my pages static for anonymous users. Last 30day combined estimate on Google Ananlytics: 90k visitors 260k pageviews. Issue: alot of downtime, I am continually checking if my sites are up, and lately I have been finding it down more than 3 times daily. Restarting Apache will bring it back up, for some time. I have google search every error message and looked up ways to optimize my DV server, and I am beyond stump what is my next move. Is this server bad, have I hit a impossibly low restriction such as the 12mb kernel memory barrier (kmemsize), is it on my end, do I need to optimize some more? *I have provided as much information as I can below, any help or suggestions given will be appreciated Common Error messages I see in the log: [error] (12)Cannot allocate memory: fork: Unable to fork new process [error] make_obcallback: could not import mod_python.apache.\n Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/mod_python/apache.py", line 21, in ? import traceback File "/usr/lib/python2.4/traceback.py", line 3, in ? import linecache ImportError: No module named linecache [error] python_handler: no interpreter callback found. [warn-phpd] mmap cache can't open /var/www/vhosts/***/httpdocs/*** - Too many open files in system (pid ***) [alert] Child 8125 returned a Fatal error... Apache is exiting! [emerg] (43)Identifier removed: couldn't grab the accept mutex [emerg] (22)Invalid argument: couldn't release the accept mutex cat /proc/user_beancounters: Version: 2.5 uid resource held maxheld barrier limit failcnt 41548: kmemsize 4582652 5306699 12288832 13517715 21105036 lockedpages 0 0 600 600 0 privvmpages 38151 42676 229036 249036 0 shmpages 16274 16274 17237 17237 2 dummy 0 0 0 0 0 numproc 43 46 300 300 0 physpages 27260 29528 0 2147483647 0 vmguarpages 0 0 131072 2147483647 0 oomguarpages 27270 29538 131072 2147483647 0 numtcpsock 21 29 300 300 0 numflock 8 8 480 528 0 numpty 1 1 30 30 0 numsiginfo 0 1 1024 1024 0 tcpsndbuf 648440 675272 2867477 4096277 1711499 tcprcvbuf 301620 359716 2867477 4096277 0 othersockbuf 4472 4472 1433738 2662538 0 dgramrcvbuf 0 0 1433738 1433738 0 numothersock 12 12 300 300 0 dcachesize 0 0 2684271 2764800 0 numfile 3447 3496 6300 6300 3872 dummy 0 0 0 0 0 dummy 0 0 0 0 0 dummy 0 0 0 0 0 numiptent 14 14 200 200 0 TOP: (In January the load avg was really high 3-10, I was able to bring it down where it is currently is by giving APC more memory play around with) top - 16:46:07 up 2:13, 1 user, load average: 0.34, 0.20, 0.20 Tasks: 40 total, 2 running, 37 sleeping, 0 stopped, 1 zombie Cpu(s): 0.3% us, 0.1% sy, 0.0% ni, 99.7% id, 0.0% wa, 0.0% hi, 0.0% si Mem: 916144k total, 156668k used, 759476k free, 0k buffers Swap: 0k total, 0k used, 0k free, 0k cached MySQLTuner: (after optimizing every table and repairing any table with overage I got the fragmented count down to 86) [--] Data in MyISAM tables: 285M (Tables: 1105) [!!] Total fragmented tables: 86 [--] Up for: 2h 44m 38s (409K q [41.421 qps], 6K conn, TX: 1B, RX: 174M) [--] Reads / Writes: 79% / 21% [--] Total buffers: 58.0M global + 2.7M per thread (100 max threads) [!!] Query cache prunes per day: 675307 [!!] Temporary tables created on disk: 35% (7K on disk / 20K total)

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• Optimization in Python - do's, don'ts and rules of thumb.

  - by JV

  Well I was reading this post and then I came across a code which was: jokes=range(1000000) domain=[(0,(len(jokes)*2)-i-1) for i in range(0,len(jokes)*2)] I thought wouldn't it be better to calculate the value of len(jokes) once outside the list comprehension? Well I tried it and timed three codes jv@Pioneer:~$ python -m timeit -s 'jokes=range(1000000);domain=[(0,(len(jokes)*2)-i-1) for i in range(0,len(jokes)*2)]' 10000000 loops, best of 3: 0.0352 usec per loop jv@Pioneer:~$ python -m timeit -s 'jokes=range(1000000);l=len(jokes);domain=[(0,(l*2)-i-1) for i in range(0,l*2)]' 10000000 loops, best of 3: 0.0343 usec per loop jv@Pioneer:~$ python -m timeit -s 'jokes=range(1000000);l=len(jokes)*2;domain=[(0,l-i-1) for i in range(0,l)]' 10000000 loops, best of 3: 0.0333 usec per loop Observing the marginal difference 2.55% between the first and the second made me think - is the first list comprehension domain=[(0,(len(jokes)*2)-i-1) for i in range(0,len(jokes)*2)] optimized internally by python? or is 2.55% a big enough optimization (given that the len(jokes)=1000000)? If this is - What are the other implicit/internal optimizations in Python ? What are the developer's rules of thumb for optimization in Python? Edit1: Since most of the answers are "don't optimize, do it later if its slow" and I got some tips and links from Triptych and Ali A for the do's. I will change the question a bit and request for don'ts. Can we have some experiences from people who faced the 'slowness', what was the problem and how it was corrected? Edit2: For those who haven't here is an interesting read Edit3: Incorrect usage of timeit in question please see dF's answer for correct usage and hence timings for the three codes.

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• Design Book–Fourth(last) Section (Physical Abstraction Optimization)

  - by drsql

  In this last section of the book, we will shift focus to the physical abstraction layer optimization. By this I mean the little bits and pieces of the design that is specifically there for performance and are actually part of the relational engine (read: the part of the SQL Server experience that ideally is hidden from you completely, but in 2010 reality it isn’t quite so yet.  This includes all of the data structures like database, files, etc; the optimizer; some coding, etc. In my mind, this...(read more)

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• Ant Colony Optimization de Marco Dorigo et Thomas Stützle, critique par Franck Dernoncourt

  Bonjour à tous, Voici ma critique du livre "Ant Colony Optimization". Les algorithmes de colonies de fourmis sont des algorithmes inspirés du comportement des fourmis et qui constituent une famille de métaheuristiques d'optimisation. Ils ont été appliqués à un grand nombre de problèmes d'optimisation combinatoire, allant de l'assignement quadratique au replis de protéine ou au routage de véhicules. Comme beaucoup de métaheuristiques, l'algorithme de base a été adapté aux problèmes dynamiques, en variables réelles, aux problèmes stochastiques, multi-objectifs ou aux implémentations parallèles, etc. Bref, c'est une métaheuristique incontournable pour toute pe...

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• Quality Backlinks - A Key to Search Engine Optimization

  Backlinks are the links which are going to your blogs, sites or articles. Backlinks are the most important and single very significant factors to give the page rank to your site or blogs.They are great technique and way to find a proper and a decent place in the goggle or any of the major search engines. There are many other aspects of SEO but quality back links are the most appropriate way to find a great way in terms of search engine optimization. Now it is the time to take a look at the components which are very important about backlinks.

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• How do I avoid "Developer's Bad Optimization Intuition"?

  - by Mona

  I saw on a article that put forth this statement: Developers love to optimize code and with good reason. It is so satisfying and fun. But knowing when to optimize is far more important. Unfortunately, developers generally have horrible intuition about where the performance problems in an application will actually be. How can a developer avoid this bad intuition? Are there good tools to find which parts of your code really need optimization (for Java)? Do you know of some articles, tips, or good reads on this subject?

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• All About Search Engine Position Optimization

  Search engine optimization or SEO is a method increasing the amount of traffic or hits to your website, which results in making your website rank high in search engine results. These results are produced whenever an individual types in a keyword or a set of keywords in a search query in search engines like Yahoo!, Google and the like. Being high on the list of search results matters a lot because it makes you more visible to the general public, especially to your target market. This differentiates you from your competitors who may rank low in the search results, or may not even appear in the results lists at all.

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• SQLAuthority News Microsoft SQL Server 2005/2008 Query Optimization & Performance Tuning Training

  Last 3 days to register for the courses. This is one time offer with big discount. The deadline for the course registration is 5th May, 2010. There are two different courses are offered by Solid Quality Mentors 1) Microsoft SQL Server 2005/2008 Query Optimization & Performance Tuning – Pinal Dave Date: May 12-14, 2010 Price: [...]...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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• Learning MySQL Query optimization

  - by recluze

  I've been doing web/desktop/server development for a while and have worked with many databases (mysql mostly). I've come to the point in my career when I need to have someone look at my queries because they're 'kind of slow'. I believe it's now time to start learning query optimization. While I know the basics of index and joins etc., I'm not familiar with how to use, say, the EXPLAIN output to improve performance of my queries. I have not been able to find any online material that starts with the basics and takes me to application. Getting a book is not an option right now so I'm looking for tips about how to proceed with this. I hope this question is general enough not to get closed.

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• Wpf. Chart optimization. More than million points

  - by Evgeny

  I have custom control - chart with size, for example, 300x300 pixels and more than one million points (maybe less) in it. And its clear that now he works very slowly. I am searching for algoritm which will show only few points with minimal visual difference. I have link to component which have functionallity exactly what i need (2 million points demo): http://www.mindscape.co.nz/demo/SilverlightElements/demopage.html#/ChartOverviewPage I will be grateful for any matherials, links or thoughts how to realize such functionallity.

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• SQL SERVER – Introduction to Extended Events – Finding Long Running Queries

  - by pinaldave

  The job of an SQL Consultant is very interesting as always. The month before, I was busy doing query optimization and performance tuning projects for our clients, and this month, I am busy delivering my performance in Microsoft SQL Server 2005/2008 Query Optimization and & Performance Tuning Course. I recently read white paper about Extended Event by SQL Server MVP Jonathan Kehayias. You can read the white paper here: Using SQL Server 2008 Extended Events. I also read another appealing chapter by Jonathan in the book, SQLAuthority Book Review – Professional SQL Server 2008 Internals and Troubleshooting. After reading these excellent notes by Jonathan, I decided to upgrade my course and include Extended Event as one of the modules. This week, I have delivered Extended Events session two times and attendees really liked the said course. They really think Extended Events is one of the most powerful tools available. Extended Events can do many things. I suggest that you read the white paper I mentioned to learn more about this tool. Instead of writing a long theory, I am going to write a very quick script for Extended Events. This event session captures all the longest running queries ever since the event session was started. One of the many advantages of the Extended Events is that it can be configured very easily and it is a robust method to collect necessary information in terms of troubleshooting. There are many targets where you can store the information, which include XML file target, which I really like. In the following Events, we are writing the details of the event at two locations: 1) Ringer Buffer; and 2) XML file. It is not necessary to write at both places, either of the two will do. -- Extended Event for finding *long running query* IF EXISTS(SELECT * FROM sys.server_event_sessions WHERE name='LongRunningQuery') DROP EVENT SESSION LongRunningQuery ON SERVER GO -- Create Event CREATE EVENT SESSION LongRunningQuery ON SERVER -- Add event to capture event ADD EVENT sqlserver.sql_statement_completed ( -- Add action - event property ACTION (sqlserver.sql_text, sqlserver.tsql_stack) -- Predicate - time 1000 milisecond WHERE sqlserver.sql_statement_completed.duration > 1000 ) -- Add target for capturing the data - XML File ADD TARGET package0.asynchronous_file_target( SET filename='c:\LongRunningQuery.xet', metadatafile='c:\LongRunningQuery.xem'), -- Add target for capturing the data - Ring Bugger ADD TARGET package0.ring_buffer (SET max_memory = 4096) WITH (max_dispatch_latency = 1 seconds) GO -- Enable Event ALTER EVENT SESSION LongRunningQuery ON SERVER STATE=START GO -- Run long query (longer than 1000 ms) SELECT * FROM AdventureWorks.Sales.SalesOrderDetail ORDER BY UnitPriceDiscount DESC GO -- Stop the event ALTER EVENT SESSION LongRunningQuery ON SERVER STATE=STOP GO -- Read the data from Ring Buffer SELECT CAST(dt.target_data AS XML) AS xmlLockData FROM sys.dm_xe_session_targets dt JOIN sys.dm_xe_sessions ds ON ds.Address = dt.event_session_address JOIN sys.server_event_sessions ss ON ds.Name = ss.Name WHERE dt.target_name = 'ring_buffer' AND ds.Name = 'LongRunningQuery' GO -- Read the data from XML File SELECT event_data_XML.value('(event/data[1])[1]','VARCHAR(100)') AS Database_ID, event_data_XML.value('(event/data[2])[1]','INT') AS OBJECT_ID, event_data_XML.value('(event/data[3])[1]','INT') AS object_type, event_data_XML.value('(event/data[4])[1]','INT') AS cpu, event_data_XML.value('(event/data[5])[1]','INT') AS duration, event_data_XML.value('(event/data[6])[1]','INT') AS reads, event_data_XML.value('(event/data[7])[1]','INT') AS writes, event_data_XML.value('(event/action[1])[1]','VARCHAR(512)') AS sql_text, event_data_XML.value('(event/action[2])[1]','VARCHAR(512)') AS tsql_stack, CAST(event_data_XML.value('(event/action[2])[1]','VARCHAR(512)') AS XML).value('(frame/@handle)[1]','VARCHAR(50)') AS handle FROM ( SELECT CAST(event_data AS XML) event_data_XML, * FROM sys.fn_xe_file_target_read_file ('c:\LongRunningQuery*.xet', 'c:\LongRunningQuery*.xem', NULL, NULL)) T GO -- Clean up. Drop the event DROP EVENT SESSION LongRunningQuery ON SERVER GO Just run the above query, afterwards you will find following result set. This result set contains the query that was running over 1000 ms. In our example, I used the XML file, and it does not reset when SQL services or computers restarts (if you are using DMV, it will reset when SQL services restarts). This event session can be very helpful for troubleshooting. Let me know if you want me to write more about Extended Events. I am totally fascinated with this feature, so I’m planning to acquire more knowledge about it so I can determine its other usages. Reference : Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, SQL, SQL Authority, SQL Optimization, SQL Performance, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Training, SQLServer, T SQL, Technology Tagged: SQL Extended Events

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• Query optimization using composite indexes

  - by xmarch

  Many times, during the process of creating a new Coherence application, developers do not pay attention to the way cache queries are constructed; they only check that these queries comply with functional specs. Later, performance testing shows that these perform poorly and it is then when developers start working on improvements until the non-functional performance requirements are met. This post describes the optimization process of a real-life scenario, where using a composite attribute index has brought a radical improvement in query execution times.  The execution times went down from 4 seconds to 2 milliseconds! E-commerce solution based on Oracle ATG – Endeca In the context of a new e-commerce solution based on Oracle ATG – Endeca, Oracle Coherence has been used to calculate and store SKU prices. In this architecture, a Coherence cache stores the final SKU prices used for Endeca baseline indexing. Each SKU price is calculated from a base SKU price and a series of calculations based on information from corporate global discounts. Corporate global discounts information is stored in an auxiliary Coherence cache with over 800.000 entries. In particular, to obtain each price the process needs to execute six queries over the global discount cache. After the implementation was finished, we discovered that the most expensive steps in the price calculation discount process were the global discounts cache query. This query has 10 parameters and is executed 6 times for each SKU price calculation. The steps taken to optimise this query are described below; Starting point Initial query was: String filter = "levelId = :iLevelId AND  salesCompanyId = :iSalesCompanyId AND salesChannelId = :iSalesChannelId "+ "AND departmentId = :iDepartmentId AND familyId = :iFamilyId AND brand = :iBrand AND manufacturer = :iManufacturer "+ "AND areaId = :iAreaId AND endDate >=  :iEndDate AND startDate <= :iStartDate"; Map<String, Object> params = new HashMap<String, Object>(10); // Fill all parameters. params.put("iLevelId", xxxx); // Executing filter. Filter globalDiscountsFilter = QueryHelper.createFilter(filter, params); NamedCache globalDiscountsCache = CacheFactory.getCache(CacheConstants.GLOBAL_DISCOUNTS_CACHE_NAME); Set applicableDiscounts = globalDiscountsCache.entrySet(globalDiscountsFilter); With the small dataset used for development the cache queries performed very well. However, when carrying out performance testing with a real-world sample size of 800,000 entries, each query execution was taking more than 4 seconds. First round of optimizations The first optimisation step was the creation of separate Coherence index for each of the 10 attributes used by the filter. This avoided object deserialization while executing the query. Each index was created as follows: globalDiscountsCache.addIndex(new ReflectionExtractor("getXXX" ) , false, null); After adding these indexes the query execution time was reduced to between 450 ms and 1s. However, these execution times were still not good enough.  Second round of optimizations In this optimisation phase a Coherence query explain plan was used to identify how many entires each index reduced the results set by, along with the cost in ms of executing that part of the query. Though the explain plan showed that all the indexes for the query were being used, it also showed that the ordering of the query parameters was "sub-optimal".  Parameters associated to object attributes with high-cardinality should appear at the beginning of the filter, or more specifically, the attributes that filters out the highest of number records should be placed at the beginning. But examining corporate global discount data we realized that depending on the values of the parameters used in the query the “good” order for the attributes was different. In particular, if the attributes brand and family had specific values it was more optimal to have a different query changing the order of the attributes. Ultimately, we ended up with three different optimal variants of the query that were used in its relevant cases: String filter = "brand = :iBrand AND familyId = :iFamilyId AND departmentId = :iDepartmentId AND levelId = :iLevelId "+ "AND manufacturer = :iManufacturer AND endDate >= :iEndDate AND salesCompanyId = :iSalesCompanyId "+ "AND areaId = :iAreaId AND salesChannelId = :iSalesChannelId AND startDate <= :iStartDate"; String filter = "familyId = :iFamilyId AND departmentId = :iDepartmentId AND levelId = :iLevelId AND brand = :iBrand "+ "AND manufacturer = :iManufacturer AND endDate >=  :iEndDate AND salesCompanyId = :iSalesCompanyId "+ "AND areaId = :iAreaId  AND salesChannelId = :iSalesChannelId AND startDate <= :iStartDate"; String filter = "brand = :iBrand AND departmentId = :iDepartmentId AND familyId = :iFamilyId AND levelId = :iLevelId "+ "AND manufacturer = :iManufacturer AND endDate >= :iEndDate AND salesCompanyId = :iSalesCompanyId "+ "AND areaId = :iAreaId AND salesChannelId = :iSalesChannelId AND startDate <= :iStartDate"; Using the appropriate query depending on the value of brand and family parameters the query execution time dropped to between 100 ms and 150 ms. But these these execution times were still not good enough and the solution was cumbersome. Third and last round of optimizations The third and final optimization was to introduce a composite index. However, this did mean that it was not possible to use the Coherence Query Language (CohQL), as composite indexes are not currently supporte in CohQL. As the original query had 8 parameters using EqualsFilter, 1 using GreaterEqualsFilter and 1 using LessEqualsFilter, the composite index was built for the 8 attributes using EqualsFilter. The final query had an EqualsFilter for the multiple extractor, a GreaterEqualsFilter and a LessEqualsFilter for the 2 remaining attributes.  All individual indexes were dropped except the ones being used for LessEqualsFilter and GreaterEqualsFilter. We were now running in an scenario with an 8-attributes composite filter and 2 single attribute filters. The composite index created was as follows: ValueExtractor[] ve = { new ReflectionExtractor("getSalesChannelId" ), new ReflectionExtractor("getLevelId" ),    new ReflectionExtractor("getAreaId" ), new ReflectionExtractor("getDepartmentId" ),    new ReflectionExtractor("getFamilyId" ), new ReflectionExtractor("getManufacturer" ),    new ReflectionExtractor("getBrand" ), new ReflectionExtractor("getSalesCompanyId" )}; MultiExtractor me = new MultiExtractor(ve); NamedCache globalDiscountsCache = CacheFactory.getCache(CacheConstants.GLOBAL_DISCOUNTS_CACHE_NAME); globalDiscountsCache.addIndex(me, false, null); And the final query was: ValueExtractor[] ve = { new ReflectionExtractor("getSalesChannelId" ), new ReflectionExtractor("getLevelId" ),    new ReflectionExtractor("getAreaId" ), new ReflectionExtractor("getDepartmentId" ),    new ReflectionExtractor("getFamilyId" ), new ReflectionExtractor("getManufacturer" ),    new ReflectionExtractor("getBrand" ), new ReflectionExtractor("getSalesCompanyId" )}; MultiExtractor me = new MultiExtractor(ve); // Fill composite parameters.String SalesCompanyId = xxxx;...AndFilter composite = new AndFilter(new EqualsFilter(me,                   Arrays.asList(iSalesChannelId, iLevelId, iAreaId, iDepartmentId, iFamilyId, iManufacturer, iBrand, SalesCompanyId)),                                     new GreaterEqualsFilter(new ReflectionExtractor("getEndDate" ), iEndDate)); AndFilter finalFilter = new AndFilter(composite, new LessEqualsFilter(new ReflectionExtractor("getStartDate" ), iStartDate)); NamedCache globalDiscountsCache = CacheFactory.getCache(CacheConstants.GLOBAL_DISCOUNTS_CACHE_NAME); Set applicableDiscounts = globalDiscountsCache.entrySet(finalFilter);      Using this composite index the query improved dramatically and the execution time dropped to between 2 ms and  4 ms.  These execution times completely met the non-functional performance requirements . It should be noticed than when using the composite index the order of the attributes inside the ValueExtractor was not relevant.

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• gcc optimization? bug? and its practial implication to project

  - by kumar_m_kiran

  Hi All, My questions are divided into three parts Question 1 Consider the below code, #include <iostream> using namespace std; int main( int argc, char *argv[]) { const int v = 50; int i = 0X7FFFFFFF; cout<<(i + v)<<endl; if ( i + v < i ) { cout<<"Number is negative"<<endl; } else { cout<<"Number is positive"<<endl; } return 0; } No specific compiler optimisation options are used or the O's flag is used. It is basic compilation command g++ -o test main.cpp is used to form the executable. The seemingly very simple code, has odd behaviour in SUSE 64 bit OS, gcc version 4.1.2. The expected output is "Number is negative", instead only in SUSE 64 bit OS, the output would be "Number is positive". After some amount of analysis and doing a 'disass' of the code, I find that the compiler optimises in the below format - Since i is same on both sides of comparison, it cannot be changed in the same expression, remove 'i' from the equation. Now, the comparison leads to if ( v < 0 ), where v is a constant positive, So during compilation itself, the else part cout function address is added to the register. No cmp/jmp instructions can be found. I see that the behaviour is only in gcc 4.1.2 SUSE 10. When tried in AIX 5.1/5.3 and HP IA64, the result is as expected. Is the above optimisation valid? Or, is using the overflow mechanism for int not a valid use case? Question 2 Now when I change the conditional statement from if (i + v < i) to if ( (i + v) < i ) even then, the behaviour is same, this atleast I would personally disagree, since additional braces are provided, I expect the compiler to create a temporary built-in type variable and them compare, thus nullify the optimisation. Question 3 Suppose I have a huge code base, an I migrate my compiler version, such bug/optimisation can cause havoc in my system behaviour. Ofcourse from business perspective, it is very ineffective to test all lines of code again just because of compiler upgradation. I think for all practical purpose, these kinds of error are very difficult to catch (during upgradation) and invariably will be leaked to production site. Can anyone suggest any possible way to ensure to ensure that these kind of bug/optimization does not have any impact on my existing system/code base? PS : When the const for v is removed from the code, then optimization is not done by the compiler. I believe, it is perfectly fine to use overflow mechanism to find if the variable is from MAX - 50 value (in my case).

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• What does it mean interprocedural optimization using the intel compiler?

  - by debugger

  Hello all, Anybody knows how this kind of optimization works ? how it work ? and in which case should one use it ? Thanks

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• Bash: evaluate a mathematical term?

  - by Heoa

  echo 3+3 How can I evaluate such expressions in Bash, in this case to 6?

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• Fixing VBSCRIPT inaccurate mathematical results due to rounding

  - by jay

  Try running this in a .VBS file MsgBox(545.14-544.94) You get a neat little answer of 0.199999999999932! This rounding issue also occurs unfortunately in Sin(2 * pi) since VB can only ever see the (user defined) variable pi as accurate as 3.14159265358979. Is rounding it manually (and loosing accuracy) the only way to improve the result? What is the most effective way of dealing with this kind of problem?

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• Array: mathematical sequence

  - by VaioIsBorn

  An array of integers A[i] (i 1) is defined in the following way: an element A[k] ( k 1) is the smallest number greater than A[k-1] such that the sum of its digits is equal to the sum of the digits of the number 4* A[k-1] . You need to write a program that calculates the N th number in this array based on the given first element A[1] . INPUT: In one line of standard input there are two numbers seperated with a single space: A[1] (1 <= A[1] <= 100) and N (1 <= N <= 10000). OUTPUT: The standard output should only contain a single integer A[N] , the Nth number of the defined sequence. Input: 7 4 Output: 79 Explanation: Elements of the array are as follows: 7, 19, 49, 79... and the 4th element is solution. I tried solving this by coding a separate function that for a given number A[k] calculates the sum of it's digits and finds the smallest number greater than A[k-1] as it says in the problem, but with no success. The first testing failed because of a memory limit, the second testing failed because of a time limit, and now i don't have any possible idea how to solve this. One friend suggested recursion, but i don't know how to set that. Anyone who can help me in any way please write, also suggest some ideas about using recursion/DP for solving this problem. Thanks.

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• Can you explain(mathematical calculations) gesture example (Levenshtein )?

  - by androidbase Praveen

  I got the example in this link. I can't understand the addmove, costLeven and meatureGesture methods. Can you explain it step by step?

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• Evaluating a string as a mathematical expression in javascript

  - by wheresrhys

  If I have a string '1+1' is there a way, other than eval(string) to get the numerical value of it i.e. I want to turn '1+1' into 2.

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• Mathematical attack on the Digital Signature Algorithm

  - by drelihan

  Does anybody know the mathematics behind an attack on DSA where modulus p has p-1 made up of only small factors. In reality, this would not happen as the key generator would guarantee that this is not so. There is much information on the web on generating good input paramters for DSA so that it is hard to crack but no information on how you find X if modulus p has p-1 made up of only small factors.

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• Are mathematical Algorithms protected by copyright?

  - by analogy

  I wish to implement an algorithm which i read in a journal paper in my software (commercial). I want to know if this is allowed or not. The algorithm in question is described in http://arxiv.org/abs/0709.2938 It is a very simple algorithm and a number of implementations exist in python (http://igraph.sourceforge.net/) and java. One of them is in gpl another which i got from a different researcher and had no license attached. There are significant differences in two implementations, e.g. second one uses threads and multiple cores. It is possible to rewrite/ (not translate) the algorithm. So can I use it in my software or on a server for commercial purpose. Thanks UPDATE: I am completely aware of copyright on the text of paper, it was published in phys rev E. I am concerned with use of the algorithm, in commercial software. Also the publication means that unless the patent has been already filed. The method has been disclosed publicly hence barring patent in future. Also the GPL implementation is not by authors themselves but comes from a third party. Finally i am not using the GPL implementation but creating my own using C++.

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• An efficient way to compute mathematical constant e

  - by Cinnamon

  Hello, The standard representation of constant e as the sum of the infinite series is very inefficient for computation, because of many division operations. So are there any alternative ways to compute the constant efficiently? Thanks!

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• Mysql - Grouping the result based on a mathematical operation and SUM() function

  - by SpikETidE

  Hi all... I'm having the following two tables... Table : room_type type_id type_name no_of_rooms max_guests rate 1 Type 1 15 2 1254 2 Type 2 10 1 3025 Table : reservation reservation_id start_date end_date room_type booked_rooms 1 2010-04-12 2010-04-15 1 8 2 2010-04-12 2010-04-15 1 2 Now... I have this query SELECT type_id, type_name FROM room_type WHERE id NOT IN (SELECT room_type FROM reservation WHERE start_date >= '$start_date' AND end_date <= '$end_date') What the query does is it selects the rooms that are not booked between the start date and end date. Also, as you can see from the reservation table, we also have 'number of rooms booked between the two dates' factor also... I need to add this 'no.of booked rooms between the two dates' factor also in to the query... The query should return the type of rooms for which at least one room is free between the two dates. I worked out the logic but just can't represent it as a query....! How will you do this...? Thanks for your suggestions..!

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• parsing of mathematical expressions

  - by gcc

  (in c90) (linux) input: sqrt(2 - sin(3*A/B)^2.5) + 0.5*(C*~(D) + 3.11 +B) a b /*there are values for a,b,c,d */ c d input: cos(2 - asin(3*A/B)^2.5) +cos(0.5*(C*~(D)) + 3.11 +B) a b /*there are values for a,b,c,d */ c d input: sqrt(2 - sin(3*A/B)^2.5)/(0.5*(C*~(D)) + sin(3.11) +ln(B)) /*max lenght of formula is 250 characters*/ a b /*there are values for a,b,c,d */ c /*each variable with set of floating numbers*/ d As you can see infix formula in the input depends on user. My program will take a formula and n-tuples value. Then it calculate the results for each value of a,b,c and d. If you wonder I am saying ;outcome of program is graph. /sometimes,I think i will take input and store in string. then another idea is arise " I should store formula in the struct" but i don't know how I can construct the code on the base of structure./ really, I don't know way how to store the formula in program code so that I can do my job. can you show me? /* a,b,c,d is letters cos,sin,sqrt,ln is function*/

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• Less Mathematical Approaches to Machine Learning?

  - by Ed

  Out of curiosity, I've been reading up a bit on the field of Machine Learning, and I'm surprised at the amount of computation and mathematics involved. One book I'm reading through uses advanced concepts such as Ring Theory and PDEs (note: the only thing I know about PDEs is that they use that funny looking character). This strikes me as odd considering that mathematics itself is a hard thing to "learn." Are there any branches of Machine Learning that use different approaches? I would think that a approaches relying more on logic, memory, construction of unfounded assumptions, and over-generalizations would be a better way to go, since that seems more like the way animals think. Animals don't (explicitly) calculate probabilities and statistics; at least as far as I know.

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