#import "template.typ": *

// Take a look at the file `template.typ` in the file panel
// to customize this template and discover how it works.
#show: project.with(
  title: "CS3230",
  authors: (
    "Yadunand Prem",
  ),
)


= Lecture 1
== Fibonacci
```python
def fib(n):
  if n == 0: return 0
  elif n == 1: return 1
  else: return fib(n-1) + fib(n-2)
```
- `T(0) = 2` (if, return)
- `T(1) = 3` (if, elif, return)
- `T(n) = T(n-1) + T(n-2) + 7` (if, elif, else, +, fib, fib, return)

== Notations
=== $O-$notation
- _Upper Bound_, that is function grows no faster than $c g(n)$
- $f in O(g)$ if there is $c > 0$ and $n_0 > 0$ such that $forall n >= n_0: 0 <= f(n) <= c g(n)$
  - The intuition is that for a large enough $n$, there is a #link(<orders>)[function] $g$ and constant $c$, such that $f(n)$ is always lesser than $g$.
=== $Omega-$notation
- _Lower Bound_, that is function grows at least as fast as $c g(n)$
- $f in O(g)$ if there is $c > 0$ and $n_0 > 0$ such that $forall n >= n_0: 0 <= c g(n) <= f(n)$
=== $Theta-$notation
- Both upper and lower bounded by $c g(n)$
- $f in O(g)$ if there is $c_1,c_2 > 0$ and $n_0 > 0$ such that $forall n >= n_0: 0 <= c_1 g(n) <= f(n) <= c_2 g(n)$

=== $o$-notation
- Strict upper bound, $0 <= f(n) < c g(n)$
=== $omega$-notation
- Strict lower bound, $0 <= c g(n) < f(n)$

#[
#set par(justify: true, leading: 0.5em)
=== Orders of Common Functions <orders>
- $O(1)$
- $O(log log n)$
- $O(log n)$
- $O((log n)^c)$
- $O(n^c), 0 < c < 1$
- $O(n)$
- $O(n log^* n)$
- $O(n log n) = O(log n!)$
- $O(n^2)$
- $O(n^c)$
- $O(c^n)$
- $O(n!)$
]

=== Limits
- $limits(lim)_(n arrow.r infinity)(f(n)/g(n)) = 0 arrow.r.double f(n) = o(g(n))$
  - By defn of limits, $limits(lim)_(n arrow.r infinity)(f(n)/g(n)) = 0$ means: 
    - $forall epsilon > 0, exists n_0 > 0$, s.t. $forall n >= n_0, f(n)/g(n) < epsilon$
    - Hence, $f(n) < c * g(n)$
- $limits(lim)_(n arrow.r infinity)(f(n)/g(n)) < infinity arrow.r.double f(n) = O(g(n))$
- 0 < $limits(lim)_(n arrow.r infinity)(f(n)/g(n)) < infinity arrow.r.double f(n) = Theta(g(n))$
- $limits(lim)_(n arrow.r infinity)(f(n)/g(n)) > 0 arrow.r.double f(n) = Omega(g(n))$
- $limits(lim)_(n arrow.r infinity)(f(n)/g(n)) = infinity arrow.r.double f(n) = omega(g(n))$

= Lecture 2
== Merge Sort
- MERGE-SORT $A[1..n]$
  1. If $n = 1$, done
  2. Recursively sort $A[1..ceil(n/2)]$ and $A[ceil(n/2)+1..n]$
  3. Merge the 2 sorted lists
- $T(n) = $
  - $Theta(1)$ if $n = 1$
  - $2T(n/2) + Theta-(n)$ if n > 1

== Solving Recurrances
=== Telescoping Method
- For a sequence $sum^(n-1)_(k=0) (a_k - a_k+1) = vec(delim: #none,a_0-a_1,a_1-a_2,a_(n-1)-a_n) = a_0 - a_n$
- E.g. $T(n) = 2T(n/2)+n$
  - $T(n)/n = T(n/2)/(n/2) + 1$
  - $T(n)/n=T(1)/1+log n$
  - $T(n) = n log n$
- General Solution
  - $T(n) = a T(n/b)+f(n)$
  - $T(n)/g(n) = T(n/b)/g(n/b) + h(n)$
  - And then sum up occurances of $h(n)$. 
=== Recursion Tree
- Draw the tree, where each node is the $f(n)$ value.
- Figure out the height of the tree and number of leaves
=== Master Theorem
- Put recurrance in the form $ T(n) = a T(n/b) + f(n) $
- $a >= 1, b > 1, f$ is asymptotically positive
- Compare $f(n)$ and $n^(log_b a)$

Cases
+ $f(n) = O(n^(log_b a - epsilon)), epsilon > 0$ (If $epsilon = 0$, case 2)
  - $f(n)$ grows polynomically slower than $n^(log_b a)$
  - $therefore T(n) = Theta(n^(log_b a))$
+ $f(n) = Theta(n^(log_b a)log^(k)n), k >= 0$
  - $f(n)$ and $n^(log_b a)$ grow similar rates
  - $therefore T(n) = Theta(n^(log_b a) log^(k+1)n)$
+ $f(n) = Omega(n^(log_b a + epsilon)), epsilon > 0$
  - $f(n)$ satisfies $a f(n/b) <= c f(n), c < 1$
  - The regularity condition satisifes that sum of subproblems $< f(n)$
  - $f(n)$ grows polynomically faster than $n^(log_b a)$
  - $therefore T(n) = Theta(f(n))$

=== Substitution
- For Upper bound: $T(n) <= c f(n)$
- For Tight bound: $T(n) <= c_2n^2 - c_1n$
Solve $T(n) = 4T(n/2) + n$
- Guess $T(n) = O(n^3)$
  - Constant $c$ s.t. $T(n) <= c n^3, n >= n_0$
- By induction
  - $c = max{2, q}, n_0=1$
  - Base Case ($n = 1$): $T(1) = q <= c (1)^3$
  - Recursive Case ($n > 1$):
    - Strong induction, assume $T(k) <= c k^3, n > k >= 1$
    - $T(n) = 4T(n/2) + n <= 4c(n/2)^3 + n = (c/2)n^3 + n <= c n^3$
= Tutorial 2
== Telescoping
- $T(n) = 4T(n/4) + n/(log n)$
- $T(n)/n - T(n/4)/(n/4) = 1/(log n)$
- Let $a_i = T(4^i)/4^i, i = log_4 n, a_i - a_(i-1) = 1/(2i)$
- $sum^(i-1)_(m=0)(a_(m+1)-a_m) = 1/(2i) +1/(2(i-1)) + ... + 1/2 = 1/2(H_i)$ ($H_i$ is harmonic)
- $a_i - a_0 = O(log i)$
- $T(4^i) = O(4^i log i), T(n) = O(n log log n)$
= Lecture 3
== Correctness of Iterative Algos using Invariants
- Initialization: Invariant is true before first iteration of loop
- Maintenance: Invariant is true at start of loop iteration, it is true at start of the _next iteration_ (Induction)
- Termination: Algo at the end gives correct answer
=== Insertion Sort
- Invariant 1: $A[1..i-1]$ are sorted values of $B[1..i-1]$
- Invariant 2: 
== Recursive Algorithms
- Usually use mathematical induction on _size of problem_
=== Binary Search
- Induction on $"length" n = "ub" - "lb" + 1$
- Base Case
  - ```python if n <= 0: return False```
- Induction Step: if $n > 0$, `ub >= lb`
  - Assume algo works for all values `ub-lb+1 < n`
  - `x == A["mid"]: return True`, answer returned correctly
  - `x > A[mid]`, then x is in the array iff it is in `A[mid + 1..ub]`, as $A$ is sorted. Thus by induction answer must be `BinarySearch(A, mid+1, ub, x)`
  - `x < A[mid]`, then x is in the array iff it is in `A[lb..mid-1]`, as $A$ is sorted. Thus by induction answer must be `BinarySearch(A, lb, mid-1, x)`
  - Thus, answer returned is correct

== Divide and Conquer
+ Divide problem into smaller subproblems
+ Solve subproblems recursively (conquer)
+ Combine / Use subproblem to get solution to full problem

- $T(n) = a T(n/b) + f(n)$
  - $a$ subproblems
  - Each subproblem is size of atmost $n/b$
  - $f(n)$ is time needed to _divide_ problem into subproblems + time to get solution from subproblems _(combine)_
=== Merge Sort
+ `MergeSort(A[lb..ub])` 
+ `If ub = lb` return
+ `If ub > lb` $"mid" = ("ub"+"lb")/2$ ($O(1)$)
+ `MergeSort(A[lb,mid]), MergeSort(A[mid+1,ub])` (2 Subproblems, each $ceil(n/2)$)
+ Merge 2 sorted list (Combining: $Theta(n)$)

$T(n) = 2T(n/2) + O(n)$
Complexity: $Theta(n * log n)$

=== Powering
- $F(a, n) = F(a, floor(n/2))^2$ if $n$ is even
- $F(a, n) = F(a, floor(n/2))^2 * F(a, 1)$ if $n$ is odd

$T(n) = T(n/2) + O(1)$

=== Fibonacci


= Tutorial 3
= Lecture 4
= Tutorial 4
= Lecture 5
= Tutorial 5
= Lecture 6
= Tutorial 6