Finding the Maximum and Its Index

Finding the biggest value takes one pass because you only need to remember one thing: the best value seen so far. Start by assuming the first element is the maximum, then walk forward comparing each new element to your current best. Any time the new element is bigger, it replaces the running maximum. By the time you reach the end, whatever you're holding is guaranteed to be the largest value in the array, because every other element lost a comparison at some point. Finding the second biggest is harder because losing once doesn't disqualify a value from being second place overall — you'd need to track two candidates and handle the bookkeeping when the true max changes mid-pass.

Often you don't just want the maximum value, you want to know where it lives. The fix is simple: instead of storing the value itself, store its index. Initialize maxIndex to 0, and compare array[maxIndex] against array[i] as you loop. When array[i] is bigger, update maxIndex to i. At the end, array[maxIndex] gives you the value and maxIndex gives you the position — tracking the index gets you both for free, since you can always look the value up.

This pattern is the backbone of linear search, selection sort, and later recursive traversals in this unit — any algorithm that needs to find an extreme value or its location reuses this same one-pass comparison loop. Minimum works identically, just flip the comparison from greater-than to less-than.