Top 20 coding interview problems asked in Google with by Lin Quan

By Lin Quan

Must Have for Google Aspirants !!!

This ebook is written for assisting humans arrange for Google Coding Interview. It includes best 20 programming difficulties commonly asked @Google with exact worked-out ideas either in pseudo-code and C++(and C++11).

  1. Matching Nuts and Bolts Optimally
  2. Searching two-dimensional taken care of array
  3. Lowest universal Ancestor(LCA) Problem
  4. Max Sub-Array Problem
  5. Compute subsequent better Number
  6. 2D Binary Search
  7. String Edit Distance
  8. Searching in Dimensional Sequence
  9. Select Kth Smallest Element
  10. Searching in probably Empty Dimensional Sequence
  11. The superstar Problem
  12. Switch and Bulb Problem
  13. Interpolation Search
  14. The Majority Problem
  15. The Plateau Problem
  16. Segment Problems
  17. Efficient Permutation
  18. The Non-Crooks Problem
  19. Median seek Problem
  20. Missing Integer Problem

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Top 20 coding interview problems asked in Google with solutions: Algorithmic Approach

Should have for Google Aspirants ! !! This ebook is written for aiding humans organize for Google Coding Interview. It comprises most sensible 20 programming difficulties commonly asked @Google with targeted worked-out recommendations either in pseudo-code and C++(and C++11). Matching Nuts and Bolts Optimally looking out two-dimensional taken care of array Lowest universal Ancestor(LCA) challenge Max Sub-Array challenge Compute subsequent larger quantity 2nd Binary seek String Edit Distance looking out in Dimensional series opt for Kth Smallest aspect looking in in all probability Empty Dimensional series the fame challenge swap and Bulb challenge Interpolation seek the bulk challenge The Plateau challenge section difficulties effective Permutation The Non-Crooks challenge Median seek challenge lacking Integer challenge

Extra info for Top 20 coding interview problems asked in Google with solutions: Algorithmic Approach

Sample text

The algorithm for matching nuts and bolts works as follows: ☛ Find a c-approximate median s of the n given nuts (constant c will be determined later). This requires O(n) nut-and-bolt comparisons. ☛ Find the bolt b corresponding to s. ☛ Compare all nuts to b and all bolts to s. This gives two piles of nuts (and bolts as well), one with the nuts (bolts) smaller than s and one with the nuts (bolts) bigger than s. ☛ Run the algorithm recursively on the two piles of the smaller nuts and bolts and the two piles of the bigger nuts and bolts.

Algorithm 5: Selecting a c-approximate median of X with O(n) complexity 1: function Get-c-Approximate-Median(X) 2: n ← 3: l ← 1 4: r ← 2n 5: i ← 0 6: while |Xi|≥ C do 7: Y i ← nut-and-bolt-ϵ-halve 8: B ← Back-Track 9: Z ← Find-Misplaced-Elements 10: if i is odd then 11: r ← 12: else 13: l ← 14: end if 15: i ← i + 1 16: Xi ← Y i-1[l,r] ∪ Z ∪ B 17: end while 18: return Xi 19: end function Algorithm 6: Back-Tracking 1: function Back-Tracking(Y, i, Z) 2: if i ≤ 2 then 3: return ∅ 4: end if 5: if i is even then 6: for any members of Z that are in the right half of Y find all members of all of the left fringes that are supported exclusively by these members of Z or other active elements.

That is, we look at the average running time for each input, and then choose the maximum over all inputs of a certain size: Tworst-caseexpected(n) = max|X|=nE[T(X)]. It’s important to note here that we are making no assumptions about the probability distribution of possible inputs. All the randomness is inside the algorithm, where we can control it. Suppose we want to find the nut that matches a particular bolt. The obvious algorithm can be: test every nut until we find a match. This requires exactly n - 1 tests in the worst case.

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