From f700d74ba14ebb5d591656168040c4c2688d123a Mon Sep 17 00:00:00 2001 From: Riya Bansal <35702912+riyabansal98@users.noreply.github.com> Date: Sat, 12 Oct 2019 16:20:51 +0530 Subject: [PATCH] Fix: Minor indentation Changes --- Recursion and Backtracking/1.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/Recursion and Backtracking/1.md b/Recursion and Backtracking/1.md index eafff29..0ebe175 100644 --- a/Recursion and Backtracking/1.md +++ b/Recursion and Backtracking/1.md @@ -23,7 +23,7 @@ __Intuition__ The main idea is to represent a problem in terms of one or more smaller problems, and add one or more base conditions that stop the recursion. _For example_, we compute factorial n if we know factorial of (n-1). The base case for factorial would be n = 0. We return 1 when n = 0. --- -- + Power ----- @@ -35,7 +35,7 @@ def pow(n, k): if k == 0: return 1 return n*pow(n, k - 1) ``` -__Time Complexity__: $O(n)$ +__Time Complexity__: O(n) __Optimised solution:__ ```python @@ -54,7 +54,7 @@ To allow reuse of answers. -__Time Complexity__ (assuming all multiplications are O(1))? $O(\log_2 k)$ +__Time Complexity__ (assuming all multiplications are O(1))? O(\log_2 k)$O(\log_{2}k)$ Break it into 3 parts? k//3 and take care of mod1 and mod2. @@ -71,7 +71,7 @@ The idea is to consider two cases for every element. (i) Consider current element as part of current subset. (ii) Do not consider current element as part of current subset. -Number of subsets? $2^n$ +Number of subsets? $2^{n}$ Explain that we want combinations, and not permutations. [1, 4] = [4, 1] @@ -164,7 +164,7 @@ def subsets(A, i, aux, p): no_take = subsets(A, i+1, aux, False) ``` -__Time Complexity__: $O(2^n)$ +__Time Complexity__: $O(2^n)$
__Space Complexity__: $O(n^2)$, because we're creating new aux arrays. -- -- @@ -228,7 +228,7 @@ def subsetSum2(A,N,cur_sum, i, target): no_take = subsetSum2(A,N,cur_sum, i+1, target) return take + no_take ``` -__Time Complexity__ : $O(2 ** (Target/MinElement))$ +__Time Complexity__ : $O(2 ** (Target/MinElement))$
__Space Complexity__: $O(Target/Min Element)$