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  • Optimize included files and uses in Delphi

    - by Roland Bengtsson
    I try to increase performance of Delphi 2007 and Codeinsight. In the application there are 483 files added in the DPR file. I don't know if it is imagination but I feel that I got better performance from Codeinsight by simply readd all files in the DPR. I also think (correct me if I'm wrong) that all files that are included in a uses section also should be included in the DPR file for best performance. My question is, does it exists a tool that scan the whole project and give a list what files are missing in the DPR file and what files can be removed? Would also be nice to have a list of uses that can be removed in the PAS files. Regards

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  • how to optimize an oracle query that has to_char in where clause for date

    - by panorama12
    I have a table that contains about 49403459 records. I want to query the table on a date range. say 04/10/2010 to 04/10/2010. However, the dates are stored in the table as format 10-APR-10 10.15.06.000000 AM (time stamp). As a result. When I do: SELECT bunch,of,stuff,create_date FROM myTable WHERE TO_CHAR (create_date,'MM/DD/YYYY)' >= '04/10/2010' AND TO_CHAR (create_date, 'MM/DD/YYYY' <= '04/10/2010' I get 529 rows but in 255.59 seconds! which is because I guess I am doing to_char on EACH record. However, When I do SELECT bunch,of,stuff,create_date FROM myTable WHERE create_date >= to_date('04/10/2010','MM/DD/YYYY') AND create_date <= to_date('04/10/2010','MM/DD/YYYY') then I get 0 results in 0.14 seconds. How can I make this query fast and still get valid (529) results?? At this point I can not change indexes. Right now I think index is created on create_date column

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  • Any way to optimize this MySQL query?

    - by manyxcxi
    My table looks like this: `MyDB`.`Details` ( `id` bigint(20) NOT NULL, `run_id` int(11) NOT NULL, `element_name` varchar(255) NOT NULL, `value` text, `line_order` int(11) default NULL, `column_order` int(11) default NULL ); I have the following SELECT statement in a stored procedure SELECT RULE ,TITLE ,SUM(IF(t.PASSED='Y',1,0)) AS PASS ,SUM(IF(t.PASSED='N',1,0)) AS FAIL FROM ( SELECT a.line_order ,MAX(CASE WHEN a.element_name = 'PASSED' THEN a.`value` END) AS PASSED ,MAX(CASE WHEN a.element_name = 'RULE' THEN a.`value` END) AS RULE ,MAX(CASE WHEN a.element_name = 'TITLE' THEN a.`value` END) AS TITLE FROM Details a WHERE run_id = runId GROUP BY line_order ) t GROUP BY RULE, TITLE; *runId is an input parameter to the stored procedure. This query takes about 14 seconds to run. The table has 214856 rows, and the particular run_id I am filtering on has 162204 records. It's not on a super high power machine, but I feel like I could be doing this more efficiently. My main goal is to summarize by Rule and Title and show Pass and Fail count columns.

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  • Optimize master-detail insert statements

    - by Dave Jarvis
    Quest After a day of running (against nearly 1 GB of data), a set of statements are tumbling down to 40 inserts per second. I am looking to increase that by an order of magnitude or two. SQL Code The code to insert the information comes in two parts: a master record and detail records. The master record: INSERT INTO MONTH_REF (DISTRICT_ID, STATION_ID, CATEGORY_ID, YEAR, MONTH) VALUES ('101', '0066', '010', 1984, 07); The detail records: INSERT INTO DAILY (MONTH_REF_ID, AMOUNT, DAILY_FLAG_ID, DAY) VALUES ((SELECT ID FROM MONTH_REF M WHERE M.DISTRICT_ID = '101' AND M.STATION_ID = '0066' AND M.CAT EGORY_ID = '010' AND M.YEAR = 1984 AND M.MONTH = 07), 0, ' ', 1); INSERT INTO DAILY (MONTH_REF_ID, AMOUNT, DAILY_FLAG_ID, DAY) VALUES ((SELECT ID FROM MONTH_REF M WHERE M.DISTRICT_ID = '101' AND M.STATION_ID = '0066' AND M.CAT EGORY_ID = '010' AND M.YEAR = 1984 AND M.MONTH = 07), 0.5, ' ', 2); INSERT INTO DAILY (MONTH_REF_ID, AMOUNT, DAILY_FLAG_ID, DAY) VALUES ((SELECT ID FROM MONTH_REF M WHERE M.DISTRICT_ID = '101' AND M.STATION_ID = '0066' AND M.CAT EGORY_ID = '010' AND M.YEAR = 1984 AND M.MONTH = 07), 0, 'T', 3); Proposed Solution INSERT INTO MONTH_REF (DISTRICT_ID, STATION_ID, CATEGORY_ID, YEAR, MONTH) VALUES ('101', '0066', '010', 1984, 07); SET @month_ref_id := (SELECT LAST_INSERT_ID()); INSERT INTO DAILY (MONTH_REF_ID, AMOUNT, DAILY_FLAG_ID, DAY) VALUES (@month_ref_id, 0, ' ', 1); INSERT INTO DAILY (MONTH_REF_ID, AMOUNT, DAILY_FLAG_ID, DAY) VALUES (@month_ref_id, 0.5, ' ', 2); INSERT INTO DAILY (MONTH_REF_ID, AMOUNT, DAILY_FLAG_ID, DAY) VALUES (@month_ref_id, 0, 'T', 3); Constraints The MONTH_REF table has an AUTO_INCREMENT primary key and is indexed on it. The DAILY table has no index and no primary key. A primary key can be added to the DAILY table, if it would help. Question Is there a more efficient way to execute the (billion or so) insert statements than the proposed solution? Thank you!

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  • How Optimize sql query make it faster

    - by user502083
    Hello every one : I have a very simple small database, 2 of tables are: Node (Node_ID, Node_name, Node_Date) : Node_ID is primary key Citation (Origin_Id, Target_Id) : PRIMARY KEY (Origin_Id, Target_Id) each is FK in Node Now I write a query that first find all citations that their Origin_Id has a specific date and then I want to know what are the target dates of these records. I'm using sqlite in python the Node table has 3000 record and Citation has 9000 records, and my query is like this in a function: def cited_years_list(self, date): c=self.cur try: c.execute("""select n.Node_Date,count(*) from Node n INNER JOIN (select c.Origin_Id AS Origin_Id, c.Target_Id AS Target_Id, n.Node_Date AS Date from CITATION c INNER JOIN NODE n ON c.Origin_Id=n.Node_Id where CAST(n.Node_Date as INT)={0}) VW ON VW.Target_Id=n.Node_Id GROUP BY n.Node_Date;""".format(date)) cited_years=c.fetchall() self.conn.commit() print('Cited Years are : \n ',str(cited_years)) except Exception as e: print('Cited Years retrival failed ',e) return cited_years Then I call this function for some specific years, But it's crazy slowwwwwwwww :( (around 1 min for a specific year) Although my query works fine, it is slow. would you please give me a suggestion to make it faster? I'd appreciate any idea about optimizing this query :) I also should mention that I have indices on Origin_Id and Target_Id, so the inner join should be pretty fast, but it's not!!!

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  • How to optimize this Python code?

    - by RandomVector
    def maxVote(nLabels): count = {} maxList = [] maxCount = 0 for nLabel in nLabels: if nLabel in count: count[nLabel] += 1 else: count[nLabel] = 1 #Check if the count is max if count[nLabel] > maxCount: maxCount = count[nLabel] maxList = [nLabel,] elif count[nLabel]==maxCount: maxList.append(nLabel) return random.choice(maxList) nLabels contains a list of integers. The above function returns the integer with highest frequency, if more than one have same frequency then a randomly selected integer from them is returned. E.g. maxVote([1,3,4,5,5,5,3,12,11]) is 5

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  • what is the best way to optimize my json on an asp.net-mvc site

    - by ooo
    i am currently using jqgrid on an asp.net mvc site and we have a pretty slow network (internal application) and it seems to be taking the grid a long time to load (the issue is both network as well as parsing, rendering) I am trying to determine how to minimized what i send over to the client to make it as fast as possible. Here is a simplified view of my controller action to load data into the grid: [AcceptVerbs(HttpVerbs.Get)] public ActionResult GridData1(GridData args) { var paginatedData = applications.GridPaginate(args.page ?? 1, args.rows ?? 10, i => new { i.Id, Name = "<div class='showDescription' id= '" + i.id+ "'>" + i.Name + "</div>", MyValue = GetImageUrl(_map, i.value, "star"), ExternalId = string.Format("<a href=\"{0}\" target=\"_blank\">{1}</a>", Url.Action("Link", "Order", new { id = i.id }), i.Id), i.Target, i.Owner, EndDate = i.EndDate, Updated = "<div class='showView' aitId= '" + i.AitId + "'>" + GetImage(i.EndDateColumn, "star") + "</div>", }) return Json(paginatedData); } So i am building up a json data (i have about 200 records of the above) and sending it back to the GUI to put in the jqgrid. The one thing i can thihk of is Repeated data. In some of the json fields i am appending HTML on top of the raw "data". This is the same HTML on every record. It seems like it would be more efficient if i could just send the data and "append" the HTML around it on the client side. Is this possible? Then i would just be sending the actual data over the wire and have the client side add on the rest of the HTML tags (the divs, etc) be put together. Also, if there are any other suggestions on how i can minimize the size of my messages, that would be great. I guess at some point these solution will increase the client side load but it may be worth it to cut down on network traffic.

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  • optimize python code

    - by user283405
    i have code that uses BeautifulSoup library for parsing. But it is very slow. The code is written in such a way that threads cannot be used. Can anyone help me about this? I am using beautifulsoup library for parsing and than save in DB. if i comment the save statement, than still it takes time so there is no problem with database. def parse(self,text): soup = BeautifulSoup(text) arr = soup.findAll('tbody') for i in range(0,len(arr)-1): data=Data() soup2 = BeautifulSoup(str(arr[i])) arr2 = soup2.findAll('td') c=0 for j in arr2: if str(j).find("<a href=") > 0: data.sourceURL = self.getAttributeValue(str(j),'<a href="') else: if c == 2: data.Hits=j.renderContents() #and few others... #... c = c+1 data.save() Any suggestions? Note: I already ask this question here but that was closed due to incomplete information.

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  • How to optimize frame rate in Flash/Actionscript?

    - by LillyWolf
    I'm building an application in Actionscript using Flash assets, and my frame rate becomes very low (~7 fps) when I attempt to render 20+ assets on the screen, even though most of those assets are stopped movie clips. I've tried setting .cacheAsBitmap to true, which helps a bit, but not enough. What else can I do to get the frame rate up? I've noticed that some movie clips seem to impact it more than others, but I'm not sure how to alter them to make them easier to render. Thanks!

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  • Please help fix and optimize this query

    - by user607217
    I am working on a system to find potential duplicates in our customers table (SQL 2005). I am using the built-in SOUNDEX value that our software computes when customers are added/updated, but I also implemented the double metaphone algorithm for better matching. This is the most-nested query I have created, and I can't help but think there is a better way to do it and I'd like to learn. In the inner-most query I am joining the customer table to the metaphone table I created, then finding customers that have identical pKey (primary phonetic key). I take that, union that with customers that have matching soundex values, and then proceed to score those matches with various text similarity functions. This is currently working, but I would also like to add a union of customers whose aKey (alternate phonetic key) match. This would be identical to "QUERY A" except to substitute on (c1Akey = c2Akey) for the join. However, when I attempt to include that, I get errors when I try to execute my query. Here is the code: --Create aggregate ranking select c1Name, c2Name, nDiff, c1Addr, c2Addr, aDiff, c1SSN, c2SSN, sDiff, c1DOB, c2DOB, dDiff, nDiff+aDiff+dDiff+sDiff as Score ,(sDiff+dDiff)*1.5 + (nDiff+dDiff)*1.5 + (nDiff+sDiff)*1.5 + aDiff *.5 + nDiff *.5 as [Rank] FROM ( --Create match scores for different fields SELECT c1Name, c2Name, c1Addr, c2Addr, c1SSN, c2SSN, c1LTD, c2LTD, c1DOB, c2DOB, dbo.Jaro(c1name, c2name) AS nDiff, dbo.JaroWinkler(c1addr, c2addr) AS aDiff, CASE WHEN c1dob = '1901-01-01' OR c2dob = '1901-01-01' OR c1dob = '1800-01-01' OR c2dob = '1800-01-01' THEN .5 ELSE dbo.SmithWaterman(c1dob, c2dob) END AS dDiff, CASE WHEN c1ssn = '000-00-0000' OR c2ssn = '000-00-0000' THEN .5 ELSE dbo.Jaro(c1ssn, c2ssn) END AS sDiff FROM -- Generate list of possible matches based on multiple phonetic matching fields ( select * from -- List of similar names from pKey field of ##Metaphone table --QUERY A BEGIN (select customers.custno as c1Custno, name as c1Name, haddr as c1Addr, ssn as c1SSN, lasttripdate as c1LTD, dob as c1DOB, soundex as c1Soundex, pkey as c1Pkey, akey as c1Akey from Customers WITH (nolock) join ##Metaphone on customers.custno = ##Metaphone.custno) as c1 JOIN (select customers.custno as c2Custno, name as c2Name, haddr as c2Addr, ssn as c2SSN, lasttripdate as c2LTD, dob as c2DOB, soundex as c2Soundex, pkey as c2Pkey, akey as c2Akey from Customers with (nolock) join ##Metaphone on customers.custno = ##Metaphone.custno) as c2 on (c1Pkey = c2Pkey) and (c1Custno < c2Custno) WHERE (c1Name <> 'PARENT, GUARDIAN') and c1soundex != c2soundex --QUERY A END union --List of similar names from pregenerated SOUNDEX field (select t1.custno, t1.name, t1.haddr, t1.ssn, t1.lasttripdate, t1.dob, t1.[soundex], 0, 0, t2.custno, t2.name, t2.haddr, t2.ssn, t2.lasttripdate, t2.dob, t2.[soundex], 0, 0 from Customers t1 WITH (nolock) join customers t2 with (nolock) on t1.[soundex] = t2.[soundex] and t1.custno < t2.custno where (t1.name <> 'PARENT, GUARDIAN')) ) as a ) as b where (sDiff+dDiff)*1.5 + (nDiff+dDiff)*1.5 + (nDiff+sDiff)*1.5 + aDiff *.5 + nDiff *.5 >= 7.5 order by [rank] desc, score desc Previously, I was using joins such as on c1.pkey = c2.pkey or c1.akey = c2.akey or c1.soundex = c2.soundex but the performance was horrendous, and using unions seems to be working a lot better. Out of 103K customers, tt is currently generating a list of 8.5M potential matches (based on the phonetic codes) in 2.25 minutes, and then taking another 2 to score, rank and filter those down to about 3000. So I am happy with the performance, I just can't help but think there is a better way to structure this, and I need help adding the extra union condition. Thanks!

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  • How to optimize shopping carts for minimal prices?

    - by tangens
    I have a list of items I want to buy. The items are offered by different shops and different prices. The shops have individual delivery costs. I'm looking for an optimal shopping strategy (and a java library supporting it) to purchase all of the items with a minimal total price. Example: Item1 is offered at Shop1 for $100, at Shop2 for $111. Item2 is offered at Shop1 for $90, at Shop2 for $85. Delivery cost of Shop1: $10 if total order < $150; $0 otherwise Delivery cost of Shop2: $5 if total order < $50; $0 otherwise If I buy Item1 and Item2 at Shop1 the total cost is $100 + $90 +$0 = $190. If I buy Item1 and Item2 at Shop2 the total cost is $111 + $85 +$0 = $196. If I buy Item1 at Shop1 and Item2 at Shop2 the total cost is $100 + $10 + $85 + $0 = 195. I get the minimal price if I order Item1 at Shop1 and Item2 at Shop2: $195 Question I need some hints which algorithms may help me to solve optimization problems of this kind for number of items about 100 and number of shops about 20. I already looked at apache-math and its optimization package, but I have no idea what algorithm to look for.

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  • How Can I optimize this RewriteEngine Code?

    - by Lucki Mile
    I have server overload, server admin said that this issue is caused from htaccess file This is the code: RewriteEngine On RewriteBase /here/ RewriteRule ^top/?$ index.php?mode=top [QSA] RewriteRule ^top/video/?$ index.php?mode=top&cat=vids [QSA] RewriteRule ^top/picture/?$ /index.php?mode=top&cat=pics [QSA] RewriteRule ^random$ index.php?mode=random [QSA] RewriteRule ^random/video/?$ index.php?mode=random&cat=vids [QSA] RewriteRule ^random/picture/?$ index.php?mode=random&cat=pics [QSA] RewriteRule ^new/?$ index.php [QSA] RewriteRule ^new/video/?$ index.php?mode=&cat=vids [QSA] RewriteRule ^new/picture/?$ index.php?mode=&cat=pics [QSA] RewriteRule ^video/([0-9]+)_(.*)$ item.php?cat=vids&id=$1 [QSA] RewriteRule ^picture/([0-9]+)_(.*)$ item.php?cat=pics&id=$1 [QSA] ErrorDocument 404 /item.php

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  • C++ Optimize if/else condition

    - by Heye
    I have a single line of code, that consumes 25% - 30% of the runtime of my application. It is a less-than comparator for an std::set (the set is implemented with a Red-Black-Tree). It is called about 180 Million times within 52 seconds. struct Entry { const float _cost; const long _id; // some other vars Entry(float cost, float id) : _cost(cost), _id(id) { } }; template<class T> struct lt_entry: public binary_function <T, T, bool> { bool operator()(const T &l, const T &r) const { // Most readable shape if(l._cost != r._cost) { return r._cost < l._cost; } else { return l._id < r._id; } } }; The entries should be sorted by cost and if the cost is the same by their id. I have many insertions for each extraction of the minimum. I thought about using Fibonacci-Heaps, but I have been told that they are theoretically nice, but suffer from high constants and are pretty complicated to implement. And since insert is in O(log(n)) the runtime increase is nearly constant with large n. So I think its okay to stick to the set. To improve performance I tried to express it in different shapes: return l._cost < r._cost || r._cost > l._cost || l._id < r._id; return l._cost < r._cost || (l._cost == r._cost && l._id < r._id); Even this: typedef union { float _f; int _i; } flint; //... flint diff; diff._f = (l._cost - r._cost); return (diff._i && diff._i >> 31) || l._id < r._id; But the compiler seems to be smart enough already, because I haven't been able to improve the runtime. I also thought about SSE but this problem is really not very applicable for SSE... The assembly looks somewhat like this: movss (%rbx),%xmm1 mov $0x1,%r8d movss 0x20(%rdx),%xmm0 ucomiss %xmm1,%xmm0 ja 0x410600 <_ZNSt8_Rb_tree[..]+96> ucomiss %xmm0,%xmm1 jp 0x4105fd <_ZNSt8_Rb_[..]_+93> jne 0x4105fd <_ZNSt8_Rb_[..]_+93> mov 0x28(%rdx),%rax cmp %rax,0x8(%rbx) jb 0x410600 <_ZNSt8_Rb_[..]_+96> xor %r8d,%r8d I have a very tiny bit experience with assembly language, but not really much. I thought it would be the best (only?) point to squeeze out some performance, but is it really worth the effort? Can you see any shortcuts that could save some cycles? The platform the code will run on is an ubuntu 12 with gcc 4.6 (-stl=c++0x) on a many-core intel machine. Only libraries available are boost, openmp and tbb. I am really stuck on this one, it seems so simple, but takes that much time. I have been crunching my head since days thinking how I could improve this line... Can you give me a suggestion how to improve this part, or is it already at its best?

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  • Can I optimize this at all?

    - by Moshe
    I'm working on an iOS app and I'm using the following code for one of my tables to return the number of rows in a particular section: return [[kSettings arrayForKey:@"views"] count]; Is there any other way to write that line of code so that it is more memory efficient? EDIT: kSettings = NSUserDefaults standardUserDefaults. Is there any way to rewrite my line of code so that whatever memory it occupies is released sooner than it is released now?

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  • How to optimize paging for large in memory database

    - by snakefoot
    I have an application where the entire database is implemented in memory using a stl-map for each table in the database. Each item in the stl-map is a complex object with references to other items in the other stl-maps. The application works with a large amount of data, so it uses more than 500 MByte RAM. Clients are able to contact the application and get a filtered version of the entire database. This is done by running through the entire database, and finding items relevant for the client. When the application have been running for an hour or so, then Windows 2003 SP2 starts to page out parts of the RAM for the application (Eventhough there is 16 GByte RAM on the machine). After the application have been partly paged out then a client logon takes a long time (10 mins) because it now generates a page fault for each pointer lookup in the stl-map. I can see it is possible to tell Windows to lock memory in RAM, but this is generally only recommended for device drivers, and only for "small" amounts of memory. I guess a poor mans solution could be to loop through the entire memory database, and thus tell Windows we are still interested in keeping the datamodel in RAM. I guess another poor mans solution could be to disable the pagefile completely on Windows. I guess the expensive solution would be a SQL database, and then rewrite the entire application to use a database layer. Then hopefully the database system will have implemented means to for fast access. Are there other more elegant solutions ?

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  • How to optimize dynamic programming?

    - by Chan
    Problem A number is called lucky if the sum of its digits, as well as the sum of the squares of its digits is a prime number. How many numbers between A and B are lucky? Input: The first line contains the number of test cases T. Each of the next T lines contains two integers, A and B. Output: Output T lines, one for each case containing the required answer for the corresponding case. Constraints: 1 <= T <= 10000 1 <= A <= B <= 10^18 Sample Input: 2 1 20 120 130 Sample Output: 4 1 Explanation: For the first case, the lucky numbers are 11, 12, 14, 16. For the second case, the only lucky number is 120. The problem is quite simple if we use brute force, however the running time is so critical that my program failed most test cases. My current idea is to use dynamic programming by storing the previous sum in a temporary array, so for example: sum_digits(10) = 1 -> sum_digits(11) = sum_digits(10) + 1 The same idea is applied for sum square but with counter equals to odd numbers. Unfortunately, it still failed 9 of 10 test cases which makes me think there must be a better way to solve it. Any idea would be greatly appreciated. #include <iostream> #include <vector> #include <string> #include <algorithm> #include <unordered_map> #include <unordered_set> #include <cmath> #include <cassert> #include <bitset> using namespace std; bool prime_table[1540] = { 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 }; unsigned num_digits(long long i) { return i > 0 ? (long) log10 ((double) i) + 1 : 1; } void get_sum_and_sum_square_digits(long long n, int& sum, int& sum_square) { sum = 0; sum_square = 0; int digit; while (n) { digit = n % 10; sum += digit; sum_square += digit * digit; n /= 10; } } void init_digits(long long n, long long previous_sum[], const int size = 18) { int current_no_digits = num_digits(n); int digit; for (int i = 0; i < current_no_digits; ++i) { digit = n % 10; previous_sum[i] = digit; n /= 10; } for (int i = current_no_digits; i <= size; ++i) { previous_sum[i] = 0; } } void display_previous(long long previous[]) { for (int i = 0; i < 18; ++i) { cout << previous[i] << ","; } } int count_lucky_number(long long A, long long B) { long long n = A; long long end = B; int sum = 0; int sum_square = 0; int lucky_counter = 0; get_sum_and_sum_square_digits(n, sum, sum_square); long long sum_counter = sum; long long sum_square_counter = sum_square; if (prime_table[sum_counter] && prime_table[sum_square_counter]) { lucky_counter++; } long long previous_sum[19] = {1}; init_digits(n, previous_sum); while (n < end) { n++; if (n % 100000000000000000 == 0) { previous_sum[17]++; sum_counter = previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[16] = 0; previous_sum[15] = 0; previous_sum[14] = 0; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000000000000 == 0) { previous_sum[16]++; sum_counter = previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[15] = 0; previous_sum[14] = 0; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000000000000 == 0) { previous_sum[15]++; sum_counter = previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[14] = 0; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000000000000 == 0) { previous_sum[14]++; sum_counter = previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[13] = 0; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000000000 == 0) { previous_sum[13]++; sum_counter = previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[12] = 0; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000000000 == 0) { previous_sum[12]++; sum_counter = previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[11] = 0; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000000000 == 0) { previous_sum[11]++; sum_counter = previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[10] = 0; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000000 == 0) { previous_sum[10]++; sum_counter = previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[9] = 0; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000000 == 0) { previous_sum[9]++; sum_counter = previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[8] = 0; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000000 == 0) { previous_sum[8]++; sum_counter = previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[7] = 0; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000000 == 0) { previous_sum[7]++; sum_counter = previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[6] = 0; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000000 == 0) { previous_sum[6]++; sum_counter = previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[5] = 0; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100000 == 0) { previous_sum[5]++; sum_counter = previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[4] = 0; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10000 == 0) { previous_sum[4]++; sum_counter = previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[3] = 0; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 1000 == 0) { previous_sum[3]++; sum_counter = previous_sum[3] + previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[3] * previous_sum[3] + previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[2] = 0; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 100 == 0) { previous_sum[2]++; sum_counter = previous_sum[2] + previous_sum[3] + previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[2] * previous_sum[2] + previous_sum[3] * previous_sum[3] + previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[1] = 0; previous_sum[0] = 0; } else if (n % 10 == 0) { previous_sum[1]++; sum_counter = previous_sum[1] + previous_sum[2] + previous_sum[3] + previous_sum[4] + previous_sum[5] + previous_sum[6] + previous_sum[7] + previous_sum[8] + previous_sum[9] + previous_sum[10] + previous_sum[11] + previous_sum[12] + previous_sum[13] + previous_sum[14] + previous_sum[15] + previous_sum[16] + previous_sum[17] + previous_sum[18]; sum_square_counter = previous_sum[1] * previous_sum[1] + previous_sum[2] * previous_sum[2] + previous_sum[3] * previous_sum[3] + previous_sum[4] * previous_sum[4] + previous_sum[5] * previous_sum[5] + previous_sum[6] * previous_sum[6] + previous_sum[7] * previous_sum[7] + previous_sum[8] * previous_sum[8] + previous_sum[9] * previous_sum[9] + previous_sum[10] * previous_sum[10] + previous_sum[11] * previous_sum[11] + previous_sum[12] * previous_sum[12] + previous_sum[13] * previous_sum[13] + previous_sum[14] * previous_sum[14] + previous_sum[15] * previous_sum[15] + previous_sum[16] * previous_sum[16] + previous_sum[17] * previous_sum[17] + previous_sum[18] * previous_sum[18]; previous_sum[0] = 0; } else { sum_counter++; sum_square_counter += ((n - 1) % 10) * 2 + 1; } // get_sum_and_sum_square_digits(n, sum, sum_square); // assert(sum == sum_counter && sum_square == sum_square_counter); if (prime_table[sum_counter] && prime_table[sum_square_counter]) { lucky_counter++; } } return lucky_counter; } void inout_lucky_numbers() { int n; cin >> n; long long a; long long b; while (n--) { cin >> a >> b; cout << count_lucky_number(a, b) << endl; } } int main() { inout_lucky_numbers(); return 0; }

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  • Is it possible to optimize maven dependencies automatically?

    - by AlexR
    I am working on a big project that consists of about 40 sub-projects with very not optimized dependencies. There are declared dependencies that are not in use as well as used but undeclared dependencies. The second case is possible when dependency is added via other dependency. I want to remove redundant and add required dependencies. I ran mvn dependency:analyze and got a long list of warnings I have to fix now. I wonder whether there is maven plugin or any other utility that can update my pom.xml files automatically. I tried to do it manually but it takes a lot of time. It seems it will take a couple of days of copy/paste to complete the task. In worse case I can write such script myself but probably ready stuff exists? Here is how mvn dependency:analyze reports dependency warnings: [WARNING] Used undeclared dependencies found: [WARNING] org.apache.httpcomponents:httpcore:jar:4.1:compile [WARNING] Unused declared dependencies found: [WARNING] commons-lang:commons-lang:jar:2.4:compile [WARNING] org.json:json:jar:20090211:compile

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  • How to optimize the login option in android?

    - by Praween k
    HI, I want to create Login option in my application , so that once a person gets login that device creates token which is saved over server. From next time whenever he/she operates the application, directly goes to next label by checking that token keyvalue pair over server.IT requires login page only when that keyvalue pair is deleted from the server. Can anyone help me from this.I will be very grateful to you. Looking for reply. Regards, Praween

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  • Image Gurus: Optimize my Python PNG transparency function

    - by ozone
    I need to replace all the white(ish) pixels in a PNG image with alpha transparency. I'm using Python in AppEngine and so do not have access to libraries like PIL, imagemagick etc. AppEngine does have an image library, but is pitched mainly at image resizing. I found the excellent little pyPNG module and managed to knock up a little function that does what I need: make_transparent.py pseudo-code for the main loop would be something like: for each pixel: if pixel looks "quite white": set pixel values to transparent otherwise: keep existing pixel values and (assuming 8bit values) "quite white" would be: where each r,g,b value is greater than "240" AND each r,g,b value is within "20" of each other This is the first time I've worked with raw pixel data in this way, and although works, it also performs extremely poorly. It seems like there must be a more efficient way of processing the data without iterating over each pixel in this manner? (Matrices?) I was hoping someone with more experience in dealing with these things might be able to point out some of my more obvious mistakes/improvements in my algorithm. Thanks!

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  • optimize a string.Format + replace.

    - by acidzombie24
    I have this function. The visual studio profile marked the line with string.Format as hot and were i spend much of my time. How can i write this loop more efficiently? public string EscapeNoPredicate(string sz) { var s = new StringBuilder(sz); s.Replace(sepStr, sepStr + sepStr); foreach (char v in IllegalChars) { string s2 = string.Format("{0}{1:X2}", seperator, (Int16)v); s.Replace(v.ToString(), s2); } return s.ToString(); }

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  • How to Optimize Combined Graphical Operations?

    - by Sunny
    Hi, Here is a Scenario, A series of operations that I will call for painting, QPainter p(this); 1). p.fillRect(0,0,320,240, RED_COLOR) 2) p.drawLine(0,0,100,100, BLUE_COLOR) 3) p.fillRect(0,0,320,240, YELLOW_COLOR) Now I want that painter should not draw first FillRect Function. It should not draw line. It should only perform last operation. Is there any way to achive this optimization in Qt. Is this type of drawing/painting optimizations are supported by any library?

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  • How can I optimize this loop?

    - by Moshe
    I've got a piece of code that returns a super-long string that represents "search results". Each result is represented by a double HTML break symbol. For example: Result1<br><br>Result 2<br><br>Result3 I've got the following loop that takes each result and puts it into an array, stripping out the break indicator, "kBreakIndicator" (<br><br>). The problem is that this lopp takes way too long to execute. With a few results it's fine, but once you hit a hundred results, it's about 20-30 seconds slower. It's unacceptable performance. What can I do to improve performance? Here's my code: content is the original NSString. NSMutableArray *results = [[NSMutableArray alloc] init]; //Loop through the string of results and take each result and put it into an array while(![content isEqualToString:@""]){ NSRange rangeOfResult = [content rangeOfString:kBreakIndicator]; NSString *temp = (rangeOfResult.location != NSNotFound) ? [content substringToIndex:rangeOfResult.location] : nil; if (temp) { [results addObject:temp]; content = [[[content stringByReplacingOccurrencesOfString:[NSString stringWithFormat:@"%@%@", temp, kBreakIndicator] withString:@""] mutableCopy] autorelease]; }else{ [results addObject:[content description]]; content = [[@"" mutableCopy] autorelease]; } } //Do something with the results array. [results release];

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  • Tree iterator, can you optimize this any further?

    - by Ron
    As a follow up to my original question about a small piece of this code I decided to ask a follow up to see if you can do better then what we came up with so far. The code below iterates over a binary tree (left/right = child/next ). I do believe there is room for one less conditional in here (the down boolean). The fastest answer wins! The cnt statement can be multiple statements so lets make sure this appears only once The child() and next() member functions are about 30x as slow as the hasChild() and hasNext() operations. Keep it iterative <-- dropped this requirement as the recursive solution presented was faster. This is C++ code visit order of the nodes must stay as they are in the example below. ( hit parents first then the children then the 'next' nodes). BaseNodePtr is a boost::shared_ptr as thus assignments are slow, avoid any temporary BaseNodePtr variables. Currently this code takes 5897ms to visit 62200000 nodes in a test tree, calling this function 200,000 times. void processTree (BaseNodePtr current, unsigned int & cnt ) { bool down = true; while ( true ) { if ( down ) { while (true) { cnt++; // this can/will be multiple statesments if (!current->hasChild()) break; current = current->child(); } } if ( current->hasNext() ) { down = true; current = current->next(); } else { down = false; current = current->parent(); if (!current) return; // done. } } }

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