USACO Guide
USACO Guide (Competitive Programming Initiative)
Free, community-maintained curriculum for the USA Computing Olympiad, organized from Bronze to Platinum with explanations, code in C++, Java, and Python, and curated practice problems. Learners build skill in greedy algorithms, binary search, dynamic programming, graph algorithms, and data structures for contest problem solving.
More resources on Algorithm Design Techniques
Algorithms, Part I
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Algorithms, Fourth Edition (upon which the course is based) or visit the website algs4.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.
Algorithms, Part II
This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. People who are interested in digging deeper into the content may wish to obtain the textbook Algorithms, Fourth Edition (upon which the course is based) or visit the website algs4.cs.princeton.edu for a wealth of additional material. This course does not offer a certificate upon completion.
cp-algorithms.com
Open-source translation of the Russian e-maxx algorithm compendium, with articles on number theory, combinatorics, graph algorithms, string processing, and geometry. Each entry pairs a derivation with tested C++ code you can adapt for contest problems.
LeetCode
Online judge with thousands of algorithm and data-structure problems sorted by difficulty, topic and company, plus timed contests, discussion threads and study plans. Regular practice builds fluency with common interview problem patterns and writing correct, efficient code under time pressure.
Dynamic Programming Introduction
Erik Demaine's lecture from MIT 6.006 Introduction to Algorithms (Fall 2011) introducing dynamic programming through Fibonacci numbers and shortest paths. Viewers learn to define subproblems, apply memoization, convert recursion to bottom-up computation, and reason about running time via the subproblem DAG.
Design and Analysis of Algorithms (MIT 6.046J)
Intermediate algorithms after 6.006: divide-and-conquer, randomization, dynamic programming, greedy algorithms, network flow, amortization, complexity and cryptography. 39 lecture videos, notes, problem sets and exams with solutions train learners to design efficient algorithms and prove their correctness and running time.