algorithms

Big O

A generalize way to categorize an algorithm time and space based on input.

• Growth based on input
• Drop constants
• Consider the worst case

Big O complexity

• O(1)
• O(logn)
• O(n)
• O(nlogn)
• O(n^2)
• O(n!)

Examples

O(n)

function sumCharCodes(n) {
  let sum = 0;

  for (let i = 0; i < n.length; ++i) {
    sum += n.charCodeAt(i);
  }

  for (let i = 0; i < n.length; ++i) {
    sum += n.charCodeAt(i);
  }

  return sum;
}

O(n^2)

function sumCharCodes(n) {
  let sum = 0;

  for (let i = 0; i < n.length; ++i) {
    for (let j = 0; j < n.length; ++j) {
      sum += charCode;
    }
  }

  return sum;
}

O(n^3)

function sumCharCodes(n) {
  let sum = 0;

  for (let i = 0; i < n.length; ++i) {
    for (let j = 0; j < n.length; ++j) {
      for (let k = 0; k < n.length; ++k) {
        sum += charCode;
      }
    }
  }

  return sum;
}

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