How do I calculate the runtime complexity of this implementation of longest common prefix algorithm?










0















Below is my code:



/**
* @param string strs
* @return string
*/
var longestCommonPrefix = function(strs)
if(strs.length ===0) return '';
let prefix = strs[0];
for(let i=0; i< strs.length; i++)
while(strs[i].indexOf(prefix) !=0)
prefix = prefix.substring(0, prefix.length -1);
if(!prefix) return '';



return prefix;
;


Here for loop runs O(n) times and while loop within checks whether prefix matches the elements in for loop iteration - is the while loop O(n) times too? Please provide insight










share|improve this question


























    0















    Below is my code:



    /**
    * @param string strs
    * @return string
    */
    var longestCommonPrefix = function(strs)
    if(strs.length ===0) return '';
    let prefix = strs[0];
    for(let i=0; i< strs.length; i++)
    while(strs[i].indexOf(prefix) !=0)
    prefix = prefix.substring(0, prefix.length -1);
    if(!prefix) return '';



    return prefix;
    ;


    Here for loop runs O(n) times and while loop within checks whether prefix matches the elements in for loop iteration - is the while loop O(n) times too? Please provide insight










    share|improve this question
























      0












      0








      0








      Below is my code:



      /**
      * @param string strs
      * @return string
      */
      var longestCommonPrefix = function(strs)
      if(strs.length ===0) return '';
      let prefix = strs[0];
      for(let i=0; i< strs.length; i++)
      while(strs[i].indexOf(prefix) !=0)
      prefix = prefix.substring(0, prefix.length -1);
      if(!prefix) return '';



      return prefix;
      ;


      Here for loop runs O(n) times and while loop within checks whether prefix matches the elements in for loop iteration - is the while loop O(n) times too? Please provide insight










      share|improve this question














      Below is my code:



      /**
      * @param string strs
      * @return string
      */
      var longestCommonPrefix = function(strs)
      if(strs.length ===0) return '';
      let prefix = strs[0];
      for(let i=0; i< strs.length; i++)
      while(strs[i].indexOf(prefix) !=0)
      prefix = prefix.substring(0, prefix.length -1);
      if(!prefix) return '';



      return prefix;
      ;


      Here for loop runs O(n) times and while loop within checks whether prefix matches the elements in for loop iteration - is the while loop O(n) times too? Please provide insight







      algorithm runtime time-complexity






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 15 '18 at 16:07









      novemberskynovembersky

      342614




      342614






















          1 Answer
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          active

          oldest

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          1














          Let n be the number of strings in strs, and let the average (or longest) string length be m. Your algorithm with line numbers is as follows.



          1. var longestCommonPrefix = function(strs) 
          2. if(strs.length ===0) return '';
          3. let prefix = strs[0];
          4. for(let i=0; i< strs.length; i++)
          5. while(strs[i].indexOf(prefix) !=0)
          6. prefix = prefix.substring(0, prefix.length - 1);
          7. if(!prefix) return '';
          8.
          9.
          10. return prefix;
          11. ;


          We only need estimate the number of times the for loop (line 4) and the while loop (line 5) are executed. As you noted that the for loop is executed n times, since n = strs.length. However, you do have a return statement within the while loop, so execution might never reach line 10.



          Now, for the while loop. As described, this loop is executed the size of the prefix (m) and in each iteration the indexOf operation takes O(m^2) in the worst case (see next paragraph). Thus, the time complexity of your algorithm as a function of the number of strings (n) is O(nm^2).



          We could also look at the time complexity as a function of m, the number of characters in each string. How long does it take to compare two strings? Java's implementation in this case will take O(mk), where m and k are the lengths of the two strings respectively (see this answer to this question) and the String class code for details.






          share|improve this answer

























          • thanks a lot @rrufai but I got another doubt after reading one of the comments in the link shared above. Per Andreas, O(m^2) is worst case and least likely, cos pattern matching will stop as soon as first mismatch occurs and amortized, it is O(1.1 * n), which is O(n). So do we calculate the average case or the worst one?

            – novembersky
            Nov 16 '18 at 1:33






          • 1





            @novembersky: Usually, for big O analysis, we go with the worst-case. In fact, it's sometimes called worst-case analysis. I hope this helps.

            – rrufai
            Nov 16 '18 at 1:57










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          1 Answer
          1






          active

          oldest

          votes








          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          1














          Let n be the number of strings in strs, and let the average (or longest) string length be m. Your algorithm with line numbers is as follows.



          1. var longestCommonPrefix = function(strs) 
          2. if(strs.length ===0) return '';
          3. let prefix = strs[0];
          4. for(let i=0; i< strs.length; i++)
          5. while(strs[i].indexOf(prefix) !=0)
          6. prefix = prefix.substring(0, prefix.length - 1);
          7. if(!prefix) return '';
          8.
          9.
          10. return prefix;
          11. ;


          We only need estimate the number of times the for loop (line 4) and the while loop (line 5) are executed. As you noted that the for loop is executed n times, since n = strs.length. However, you do have a return statement within the while loop, so execution might never reach line 10.



          Now, for the while loop. As described, this loop is executed the size of the prefix (m) and in each iteration the indexOf operation takes O(m^2) in the worst case (see next paragraph). Thus, the time complexity of your algorithm as a function of the number of strings (n) is O(nm^2).



          We could also look at the time complexity as a function of m, the number of characters in each string. How long does it take to compare two strings? Java's implementation in this case will take O(mk), where m and k are the lengths of the two strings respectively (see this answer to this question) and the String class code for details.






          share|improve this answer

























          • thanks a lot @rrufai but I got another doubt after reading one of the comments in the link shared above. Per Andreas, O(m^2) is worst case and least likely, cos pattern matching will stop as soon as first mismatch occurs and amortized, it is O(1.1 * n), which is O(n). So do we calculate the average case or the worst one?

            – novembersky
            Nov 16 '18 at 1:33






          • 1





            @novembersky: Usually, for big O analysis, we go with the worst-case. In fact, it's sometimes called worst-case analysis. I hope this helps.

            – rrufai
            Nov 16 '18 at 1:57















          1














          Let n be the number of strings in strs, and let the average (or longest) string length be m. Your algorithm with line numbers is as follows.



          1. var longestCommonPrefix = function(strs) 
          2. if(strs.length ===0) return '';
          3. let prefix = strs[0];
          4. for(let i=0; i< strs.length; i++)
          5. while(strs[i].indexOf(prefix) !=0)
          6. prefix = prefix.substring(0, prefix.length - 1);
          7. if(!prefix) return '';
          8.
          9.
          10. return prefix;
          11. ;


          We only need estimate the number of times the for loop (line 4) and the while loop (line 5) are executed. As you noted that the for loop is executed n times, since n = strs.length. However, you do have a return statement within the while loop, so execution might never reach line 10.



          Now, for the while loop. As described, this loop is executed the size of the prefix (m) and in each iteration the indexOf operation takes O(m^2) in the worst case (see next paragraph). Thus, the time complexity of your algorithm as a function of the number of strings (n) is O(nm^2).



          We could also look at the time complexity as a function of m, the number of characters in each string. How long does it take to compare two strings? Java's implementation in this case will take O(mk), where m and k are the lengths of the two strings respectively (see this answer to this question) and the String class code for details.






          share|improve this answer

























          • thanks a lot @rrufai but I got another doubt after reading one of the comments in the link shared above. Per Andreas, O(m^2) is worst case and least likely, cos pattern matching will stop as soon as first mismatch occurs and amortized, it is O(1.1 * n), which is O(n). So do we calculate the average case or the worst one?

            – novembersky
            Nov 16 '18 at 1:33






          • 1





            @novembersky: Usually, for big O analysis, we go with the worst-case. In fact, it's sometimes called worst-case analysis. I hope this helps.

            – rrufai
            Nov 16 '18 at 1:57













          1












          1








          1







          Let n be the number of strings in strs, and let the average (or longest) string length be m. Your algorithm with line numbers is as follows.



          1. var longestCommonPrefix = function(strs) 
          2. if(strs.length ===0) return '';
          3. let prefix = strs[0];
          4. for(let i=0; i< strs.length; i++)
          5. while(strs[i].indexOf(prefix) !=0)
          6. prefix = prefix.substring(0, prefix.length - 1);
          7. if(!prefix) return '';
          8.
          9.
          10. return prefix;
          11. ;


          We only need estimate the number of times the for loop (line 4) and the while loop (line 5) are executed. As you noted that the for loop is executed n times, since n = strs.length. However, you do have a return statement within the while loop, so execution might never reach line 10.



          Now, for the while loop. As described, this loop is executed the size of the prefix (m) and in each iteration the indexOf operation takes O(m^2) in the worst case (see next paragraph). Thus, the time complexity of your algorithm as a function of the number of strings (n) is O(nm^2).



          We could also look at the time complexity as a function of m, the number of characters in each string. How long does it take to compare two strings? Java's implementation in this case will take O(mk), where m and k are the lengths of the two strings respectively (see this answer to this question) and the String class code for details.






          share|improve this answer















          Let n be the number of strings in strs, and let the average (or longest) string length be m. Your algorithm with line numbers is as follows.



          1. var longestCommonPrefix = function(strs) 
          2. if(strs.length ===0) return '';
          3. let prefix = strs[0];
          4. for(let i=0; i< strs.length; i++)
          5. while(strs[i].indexOf(prefix) !=0)
          6. prefix = prefix.substring(0, prefix.length - 1);
          7. if(!prefix) return '';
          8.
          9.
          10. return prefix;
          11. ;


          We only need estimate the number of times the for loop (line 4) and the while loop (line 5) are executed. As you noted that the for loop is executed n times, since n = strs.length. However, you do have a return statement within the while loop, so execution might never reach line 10.



          Now, for the while loop. As described, this loop is executed the size of the prefix (m) and in each iteration the indexOf operation takes O(m^2) in the worst case (see next paragraph). Thus, the time complexity of your algorithm as a function of the number of strings (n) is O(nm^2).



          We could also look at the time complexity as a function of m, the number of characters in each string. How long does it take to compare two strings? Java's implementation in this case will take O(mk), where m and k are the lengths of the two strings respectively (see this answer to this question) and the String class code for details.







          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 15 '18 at 22:21

























          answered Nov 15 '18 at 22:10









          rrufairrufai

          1,3551125




          1,3551125












          • thanks a lot @rrufai but I got another doubt after reading one of the comments in the link shared above. Per Andreas, O(m^2) is worst case and least likely, cos pattern matching will stop as soon as first mismatch occurs and amortized, it is O(1.1 * n), which is O(n). So do we calculate the average case or the worst one?

            – novembersky
            Nov 16 '18 at 1:33






          • 1





            @novembersky: Usually, for big O analysis, we go with the worst-case. In fact, it's sometimes called worst-case analysis. I hope this helps.

            – rrufai
            Nov 16 '18 at 1:57

















          • thanks a lot @rrufai but I got another doubt after reading one of the comments in the link shared above. Per Andreas, O(m^2) is worst case and least likely, cos pattern matching will stop as soon as first mismatch occurs and amortized, it is O(1.1 * n), which is O(n). So do we calculate the average case or the worst one?

            – novembersky
            Nov 16 '18 at 1:33






          • 1





            @novembersky: Usually, for big O analysis, we go with the worst-case. In fact, it's sometimes called worst-case analysis. I hope this helps.

            – rrufai
            Nov 16 '18 at 1:57
















          thanks a lot @rrufai but I got another doubt after reading one of the comments in the link shared above. Per Andreas, O(m^2) is worst case and least likely, cos pattern matching will stop as soon as first mismatch occurs and amortized, it is O(1.1 * n), which is O(n). So do we calculate the average case or the worst one?

          – novembersky
          Nov 16 '18 at 1:33





          thanks a lot @rrufai but I got another doubt after reading one of the comments in the link shared above. Per Andreas, O(m^2) is worst case and least likely, cos pattern matching will stop as soon as first mismatch occurs and amortized, it is O(1.1 * n), which is O(n). So do we calculate the average case or the worst one?

          – novembersky
          Nov 16 '18 at 1:33




          1




          1





          @novembersky: Usually, for big O analysis, we go with the worst-case. In fact, it's sometimes called worst-case analysis. I hope this helps.

          – rrufai
          Nov 16 '18 at 1:57





          @novembersky: Usually, for big O analysis, we go with the worst-case. In fact, it's sometimes called worst-case analysis. I hope this helps.

          – rrufai
          Nov 16 '18 at 1:57



















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