Algorithm Design Paradigms I

reading · we frame, you read MIT or the canonical taught · we author, no canonical fits ↺ spirals back to earlier lessons

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reading · 35 min + ~207 min canonical

The Paradigm Lens: Brute Force, Greedy, Divide-and-Conquer, and How to Recognise Which One Fits

You've spent the last 8 courses building algorithms one at a time. Quicksort, BFS, Dijkstra, quickselect — each handled separately, each with its own analysis. This course is diff…

primary MIT 6.006 — Greedy, Divide and Conquer lectures

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reading · 30 min + ~207 min canonical L1

Greedy Algorithms: The Pattern That Works When the Locally Best Stays Globally Best

A greedy algorithm makes the choice that looks best right now and never reconsiders. Sometimes this gives you the globally optimal answer. Sometimes it doesn't. The greedy paradig…

primary MIT 6.006 — Greedy, Divide and Conquer lectures

after L1 locked

reading · 35 min + ~207 min canonical L1

Proving Greedy Correct: Exchange Argument and Greedy-Stays-Ahead

The last lesson gave you the greedy menu — activity selection, Huffman coding, fractional knapsack — and noted that greedy sometimes works and sometimes doesn't. The question this…

primary MIT 6.006 — Greedy, Divide and Conquer lectures

after L2 locked

reading · 35 min + ~207 min canonical L1

Divide and Conquer: Master Theorem, Closest-Pair, and Strassen's Surprise

Divide and conquer is the paradigm whose recurrences you've been computing since Course 1.2 — T(n) = 2T(n/2) + O(n) for merge sort, T(n) = T(n-1) + O(1) for recursive factorial, T…

primary MIT 6.006 — Greedy, Divide and Conquer lectures

after L3 locked

reading · 25 min + ~207 min canonical L1

Decrease and Conquer: When You Shrink Instead of Splitting

Divide-and-conquer splits a problem into multiple subproblems. Decrease-and-conquer is its quieter cousin: shrink the problem by one piece — one element, one bit, one step — and r…

primary MIT 6.006 — Greedy, Divide and Conquer lectures

after L4 locked

exercise reading · 15 min L1

Exercises: Easy (Warm-up)

- [ ] Maximum Subarray (#53) — NC/LC — Kadane's algorithm (greedy) - [ ] Jump Game (#55) — NC/LC — Greedy: track farthest reachable index - [ ] Meeting Rooms (#252) — NC/LC — Sort…

after L5 locked

exercise reading · 30 min L1

Exercises: Medium (Core)

- [ ] Jump Game II (#45) — NC/LC — Greedy BFS: minimum jumps to reach end - [ ] Gas Station (#134) — NC/LC — Greedy: total surplus determines solvability - [ ] Hand of Straights (…

after L6 locked

exercise reading · 45 min L1

Exercises: Hard (Stretch)

- [ ] Insert Interval (#57) — NC/LC — Greedy merge with three-phase scan - [ ] Non-overlapping Intervals (#435) — NC/LC — Greedy: sort by end, count conflicts - [ ] Minimum Interv…

after L7 locked

exercise reading · 60 min L1

Mini-Project

- [ ] Implement a task scheduler in Ruby — given tasks with deadlines and penalties, use greedy (earliest deadline first and weighted scheduling) to minimize penalty; compare gree…

after L8 locked

L10

project reading · 300 min L1

Project: Greedy vs DP Showdown

Pick 3 problems solvable by both greedy and DP (activity selection, coin change with canonical denominations, fractional knapsack vs 0/1 knapsack). Implement both approaches in Ru…

after L9 locked

L11

assessment reading · 63 min L1

Self-Assessment: Algorithm Design Paradigms I

- [ ] What is a greedy algorithm? What two properties must a problem have for greedy to work? - [ ] Explain the difference between greedy and dynamic programming. Give an example …

after L10 locked

11 lessons. Read in order; spiral back when you need to. By the end you'll have used the core ideas twice — once on the abstract, once on something you'll meet at work next week.