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  2. Master theorem (analysis of algorithms) - Wikipedia

    en.wikipedia.org/wiki/Master_theorem_(analysis...

    ISBN 0-262-03293-7. Sections 4.3 (The master method) and 4.4 (Proof of the master theorem), pp. 73–90. Michael T. Goodrich and Roberto Tamassia. Algorithm Design: Foundation, Analysis, and Internet Examples. Wiley, 2002. ISBN 0-471-38365-1. The master theorem (including the version of Case 2 included here, which is stronger than the one from ...

  3. Quicksort - Wikipedia

    en.wikipedia.org/wiki/Quicksort

    The master theorem for divide-and-conquer recurrences tells us that T(n) = O(n log n). The outline of a formal proof of the O(n log n) expected time complexity follows. Assume that there are no duplicates as duplicates could be handled with linear time pre- and post-processing, or considered cases easier than the analyzed.

  4. Karatsuba algorithm - Wikipedia

    en.wikipedia.org/wiki/Karatsuba_algorithm

    In a computer with a full 32-bit by 32-bit multiplier, for example, one could choose B = 2 31 and store each digit as a separate 32-bit binary word. Then the sums x 1 + x 0 and y 1 + y 0 will not need an extra binary word for storing the carry-over digit (as in carry-save adder ), and the Karatsuba recursion can be applied until the numbers to ...

  5. Master theorem - Wikipedia

    en.wikipedia.org/wiki/Master_theorem

    In mathematics, a theorem that covers a variety of cases is sometimes called a master theorem. Some theorems called master theorems in their fields include: Master theorem (analysis of algorithms), analyzing the asymptotic behavior of divide-and-conquer algorithms; Ramanujan's master theorem, providing an analytic expression for the Mellin ...

  6. Merge sort - Wikipedia

    en.wikipedia.org/wiki/Merge_sort

    The closed form follows from the master theorem for divide-and-conquer recurrences. The number of comparisons made by merge sort in the worst case is given by the sorting numbers. These numbers are equal to or slightly smaller than (n ⌈lg n⌉ − 2 ⌈lg n⌉ + 1), which is between (n lg n − n + 1) and (n lg n + n + O(lg n)). [6]

  7. Analysis of algorithms - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_algorithms

    4 for i = 1 to n 5 for j = 1 to i 6 print i * j 7 print "Done!" A given computer will take a discrete amount of time to execute each of the instructions involved with carrying out this algorithm. Say that the actions carried out in step 1 are considered to consume time at most T 1 , step 2 uses time at most T 2 , and so forth.

  8. AOL

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    The search engine that helps you find exactly what you're looking for. Find the most relevant information, video, images, and answers from all across the Web.

  9. Akra–Bazzi method - Wikipedia

    en.wikipedia.org/wiki/Akra–Bazzi_method

    In computer science, the Akra–Bazzi method, or Akra–Bazzi theorem, is used to analyze the asymptotic behavior of the mathematical recurrences that appear in the analysis of divide and conquer algorithms where the sub-problems have substantially different sizes.