Algorithm

Searching

Searching finds a value or position in data: linear search, binary search, lower_bound and upper_bound, binary search on the answer, and hash lookups.

Searching is the task of finding a value, or the position where some condition first holds, inside a collection of data. Linear search checks elements one by one and works on any data, while binary search halves a sorted range at every step and needs only about 20 comparisons for a million elements. For exact-match lookups, a hash table answers in constant time on average.

Searching matters because almost every program looks things up: database indexes, autocomplete, log analysis and routing tables all rely on it. Binary search also goes beyond arrays. Whenever a yes/no condition flips only once over a range, you can binary search the answer itself, which turns many optimization problems in coding interviews into short, reliable code.

Start by writing linear search and a closed-interval binary search yourself, then practice the half-open lower_bound and upper_bound forms until the boundary handling feels natural. Trace small arrays by hand, test your code against Python's bisect module with random inputs, and finally work through optimization problems that call for binary search on the answer and ternary search.