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spaceComplexity.md

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// what is time complexity ? // same time complexity // time complexity !== time taken // same pattern becaulse of the // time is grow as the input is grow : (Time complexity) // if its linear () Alt text Linear Search 0(1) < O(logN) < O(n) < O(2 ^n)

What we consider when thinking about the time complexity:

Worst case time complexity

  1. Always look for the worst time complexity
  2. Always Look of complexity for large pool of data
  3. Alt text
  4. O(n ^3 + log N) Always ignore less dominite terms

Big ON Notation

word Defination O(n^3) : upper bound will not exceed this

Big Omega Notation

opposite of big notation

basically it lower bond (Means it will take minimum time)

Little O notation

  • this is also giving uper bound Loose up

space complexity Alt text Auxilary space

Recursive Alogrithm (Space complexity) (Height of the recursion tree) (Path of the tree) Complexity Only call that are interlinked will be in the stack at same time

O(n)

Divide and Conquer : requerence