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Build an intuitive mental model of core data structures and algorithms using relatable, real-world analogies. Topics range from ...

The cost of every graph algorithm operation in one place — big-O, and the traps that bite in practice. We build the complexity table ...

The bugs people actually hit with graph algorithms — shown, explained, and fixed. A gallery of real mistakes: what they look like, ...

The invariants of graph algorithms: the facts that must stay true after every operation. We state each invariant plainly and show ...

The invariants of graph traversal: the facts that must stay true after every operation. We state each invariant plainly and show how ...

Three ideas hold this chapter together: relaxation, spanning trees and topological order. Before any single algorithm, we name the ...

Graph Algorithms in action: each core operation walked step by step — the idea, the code, and the cost. We take the operations ...

When to reach for sorting and searching — and when not to. The design tradeoffs, side by side. We compare sorting and ...

When to reach for graph algorithms — and when not to. The design tradeoffs, side by side. We compare graph algorithms against ...

A tight recap of sorting and searching: the mental model, the operations, and the costs — everything worth keeping. The essential ...

A tight recap of range query structures: the mental model, the operations, and the costs — everything worth keeping. The essential ...

Searching on tries — finding an element, and how many places you have to look. This lesson shows exactly what happens, step ...

The Trie abstract data type: describing the type by what it promises, not how it's built. We separate the interface (the operations ...

The cost of every graph representation operation in one place — big-O, and the traps that bite in practice. We build the complexity ...

Heap Sort, end to end: how it sorts, why it's correct, and what it costs. We watch the algorithm run on a real array, then write it in C ...

When to reach for tries — and when not to. The design tradeoffs, side by side. We compare tries against the alternatives so you ...

A plain, precise definition of sorting — and the one idea everything else follows from. We define it exactly, picture it, and pull out ...

The invariants of graph representations: the facts that must stay true after every operation. We state each invariant plainly and ...

A tight recap of graph traversal: the mental model, the operations, and the costs — everything worth keeping. The essential ...

How graph algorithms actually live in memory — the byte-level layout behind the abstraction. We map the abstract picture onto ...