Module 4: Dynamic Programming & Optimization
This page aggregates the generated reference routes used by the learner-facing module.
- Semester:
semester-02-algorithms - App:
foundations
Read only if stuck
- CLRS: Part IV -- Advanced Design and Analysis Techniques -- Introduction
- CLRS: 14.1 Rod cutting (Part 1)
- CLRS: 14.1 Rod cutting (Part 2)
- CLRS: 14.1 Rod cutting (Part 3)
- CLRS: 14.3 Elements of Dynamic Programming (Part 1)
- CLRS: 14.3 Elements of Dynamic Programming (Part 2)
- CLRS: 14.3 Elements of Dynamic Programming (Part 3)
- Skiena: 8.1.1 Fibonacci Numbers by Recursion
- Skiena: 8.1.2 Fibonacci Numbers by Caching
- Grokking Algorithms: 9 Dynamic Programming (Part 1)
- Skiena: 8.2 Approximate String Matching
- Skiena: 8.2.2 Edit Distance by Dynamic Programming
- Skiena: 8.2.3 Reconstructing the Path
- Skiena: 8.2.4 Varieties of Edit Distance
- Skiena: 8.3 Longest Increasing Sequence
- Skiena: 8.5 The Partition Problem
- Skiena: 13.10 Knapsack Problem
- CLRS: 14.4 Longest Common Subsequence (Part 1)
- CLRS: 14.4 Longest Common Subsequence (Part 2)
- CLRS: 14.4 Longest Common Subsequence (Part 3)
- Competitive Programming: 3.5 Dynamic Programming
- Competitive Programming: 3.5.2 Classical Examples (LIS)
- Competitive Programming: 3.5.2 Classical Examples (Knapsack)
- Competitive Programming: 3.5.2 Classical Examples (Coin Change)
- CLRS: 14.2 Matrix Chain Multiplication (Part 1)
- CLRS: 14.2 Matrix Chain Multiplication (Part 2)
- CLRS: 14.2 Matrix Chain Multiplication (Part 3)
- CLRS: 14.5 Optimal Binary Search Trees (Part 1)
- CLRS: 14.5 Optimal Binary Search Trees (Part 2)
- Skiena: 8.6 Parsing Context-Free Grammars
- Skiena: 8.6.1 Minimum Weight Triangulation
- Competitive Programming: 9.20 Matrix Chain Multiplication
- Competitive Programming: 9.22 Max Weighted Independent Set
- Competitive Programming: 3.5.2 Classical Examples (Counting Paths)
- Grokking Algorithms: 9 Dynamic Programming (Part 2)
- Skiena: 8.7 Limitations of Dynamic Programming (TSP)
- Competitive Programming: 8.3 More Advanced DP Techniques
- Competitive Programming: 8.3.2 Compilation of Common DP Parameters
- Competitive Programming: 8.3.4 MLE: Consider Using Balanced BST as Memo Table
- Competitive Programming: 3.5.3 Non-classical Examples
- Competitive Programming: 6.5 String Processing With DP
- CLRS: 15.1 Activity-Selection Problem (Part 1)
- CLRS: 15.1 Activity-Selection Problem (Part 2)
- CLRS: 15.2 Elements of the Greedy Strategy (Part 1)
- CLRS: 15.2 Elements of the Greedy Strategy (Part 2)
- Grokking Algorithms: 8 Greedy Algorithms
- Competitive Programming: 3.4 Greedy
- Competitive Programming: 9.24 Min Path Cover on DAG
- Skiena: 10 How to Design Algorithms
- Skiena: 8.7 Limitations of DP (TSP)
- CLRS: 15.1 Activity Selection (Part 1)