Analyze algorithm efficiency with time and space complexity. Big O describes worst-case behavior as input grows: O(1), O(log n), O(n), O(n²), O(2ⁿ).

• Algorithm efficiency with time
• Space complexity

// O(1)
function getFirst(arr) { return arr[0]; }

// O(n)
function sum(arr) { 
  let s = 0; 
// ...

• Big O analysis understood

• Nested loops → multiply complexities: O(n) × O(n) = O(n²)
• Binary search: O(log n) by halving search space each iteration
• Space complexity: O(1) for variables, O(n) for copying array, O(log n) for recursion stack

Analyze algorithm efficiency with time and space complexity. Big O describes worst-case behavior as input grows: O(1), O(log n), O(n), O(n²), O(2ⁿ).

• Algorithm efficiency with time
• Space complexity

// O(1)
function getFirst(arr) { return arr[0]; }

// O(n)
function sum(arr) { 
  let s = 0; 
// ...

• Big O analysis understood

• Nested loops → multiply complexities: O(n) × O(n) = O(n²)
• Binary search: O(log n) by halving search space each iteration
• Space complexity: O(1) for variables, O(n) for copying array, O(log n) for recursion stack