ICMC-USP: A C repository from ropalma

English

Roadmap Software Engineering

Algorithms and Data Structs

Most Recommended Book
- Algoritmos Teoria e Prática - Thomas H. Cormen
YouTube
- Linguagem C Programação Descomplicada 💀
Sites
- GeeksForGeeks
- AssemblyProgressivo.net
Code Training
KhanAcademy
- Computer Science
- Notação assintótica
Recursion 💀
- O que é recursão? Teste de Mesa com Fatorial - Softblue
Data Structs Visualization
- VisualGo
- University of San Francisco - CS
Work Hard, Have Fun & Make Things 😄 ❤️ 👶
Big O
- A beginner's guide to Big O notation
- A coffee-break introduction to time complexity of algorithms
- O(1) Constant Time
  - Best Case Possible
  - If an algorithm has a constant time, it means that it always takes the same amount time to produce the output.
  - Example: array.pop()
- Logarithms O(log n)
  - Preferible in most of times
  - Logarithms are the inverse of exponentiation.
  - Example: Classic Binary search algorithm -> divide-to-conquer
- Linear time O(n)
  - Preferible in most of times
  - If an algorithm has a linear time, it means that the running time of an algorithm grows as the input size grows.
  - Example: array.forEach() sum all values
- Linear Logarithms O(n log n)
  - Acceptable
```
x = n
while ( x > 0 ) {
  y = x
  while ( y > 0 ) {
     y = y / 2
  }
  x = x - 1
}
```
  - Example: classic merge sort -> divide-to-conquer
- Quadratic time O(n²) 💀
  - Nice To Avoid
  - The running time of an algorithm is directly proportional to the square of the size of the input.
  - Example: sum of matrizes
```
for (var outer = 0; outer < elements.Count; outer++){
  for (var inner = 0; inner < elements.Count; inner++){
    ...
  }
}
```
- Exponential Time O(2^n) 💀 💀
  - Worst Case Possible, always try to avoid
  - Denotes an algorithm whose growth doubles with each additon to the input data set. The growth curve of an O(2N) function is exponential - starting off very shallow, then rising meteorically
  - Example: recursive calculation of Fibonacci numbers
```
int Fibonacci(int number){
    if (number <= 1) return number;
    return Fibonacci(number - 2) + Fibonacci(number - 1);
}
```