Binary Search

A BST keeps smaller values on the left and larger on the right, so each comparison halves the search.

From each matching start cell, DFS into the four neighbours, marking cells visited and unmarking on backtrack, until the whole word is traced.

Binary-search the candidate root; keep the largest candidate whose square does not exceed the input.

Make each value the root, recurse to build all left and all right subtree shapes, and pair them under the root.

Binary-search the answer itself, using a yes-or-no feasibility check that is monotonic.

Keep each key's history sorted by time and binary-search for the latest entry at or before the query timestamp to answer point-in-time lookups fast.

A fully sorted matrix is just a sorted array folded into rows, so one binary search with index mapping finds any value in logarithmic time.

Binary search halves a sorted range at each step, finding the target in logarithmic time.

Iterative binary search tightens left and right pointers in a loop, halving the sorted range each step with no recursion.

Linear search checks every element and needs no order; binary search halves a sorted range and is far faster.

Run two binary searches, one that leans left on a match and one that leans right, to bracket the duplicate range.

Binary search, and when it ends without a match, low is exactly where the target should be inserted.

Binary-search the daily capacity; feasibility is a greedy day-count simulation that is monotonic.

Every node must lie strictly between a lower and upper bound inherited from its ancestors, and those bounds tighten as you descend.