You have a CS exam in two days and the textbook chapter on greedy algorithms is forty pages of dense proofs. Or you are staring at a LeetCode problem, you know it involves making some kind of "best choice at each step," and you cannot quite see why that works — or when it does not.

This TLDR guide cuts straight to what you need. In under twenty pages, you will understand what a greedy algorithm actually is, the two structural conditions (the greedy-choice property and optimal substructure) that tell you when greedy is safe to use, and how to apply the classic exchange argument to convince yourself — or an instructor — that a greedy solution is correct.

The book walks through the problems every CS student encounters: interval scheduling, the fractional knapsack, and Huffman coding, each with full numerical examples and clear derivations of the right sorting key. It also shows you exactly where greedy fails — the 0/1 knapsack and the coin-change trap are worked in detail — so you stop making the mistake of reaching for greedy by default. The final section surveys where these ideas appear in graph algorithms, real schedulers, and network compression, and previews dynamic programming as the natural next step.

Designed for high school students in AP Computer Science, early college students in CS 101 or discrete math, and anyone prepping for a technical interview who needs to understand algorithm design techniques for students clearly and quickly.

Pick it up, read it in one sitting, and walk into your next exam or interview with the concept locked in.

• Define a greedy algorithm and identify the locally-best-choice pattern
• Recognize the greedy-choice property and optimal substructure
• Solve classic greedy problems: interval scheduling, coin change, fractional knapsack, Huffman coding
• Distinguish problems where greedy works from problems where it fails (and why 0/1 knapsack needs DP)
• Prove correctness of a greedy algorithm using an exchange argument
• Analyze the time complexity of greedy algorithms, typically dominated by sorting

1. 1. What Is a Greedy Algorithm?
   Introduces the locally-best-choice strategy with a concrete change-making example and contrasts greedy with brute force and dynamic programming.
2. 2. When Greedy Works: The Greedy-Choice Property and Optimal Substructure
   Explains the two structural conditions a problem must satisfy for greedy to produce an optimal solution, and walks through the exchange argument used to prove correctness.
3. 3. Classic Greedy Problems: Interval Scheduling and Fractional Knapsack
   Works through two canonical greedy problems, deriving the right sorting key for each and walking through full numerical examples.
4. 4. Huffman Coding: Greedy on a Priority Queue
   Builds an optimal prefix-free code by repeatedly merging the two least-frequent symbols, illustrating greedy with a more sophisticated data structure.
5. 5. When Greedy Fails: 0/1 Knapsack and the Coin Change Trap
   Shows concrete problems where the greedy approach gives a wrong answer, explains why, and points to dynamic programming as the fix.
6. 6. Where Greedy Shows Up: Applications and What's Next
   Surveys greedy algorithms in graph problems, scheduling, and real systems, and previews the next topics — dynamic programming and approximation algorithms.