Practical Algorithm Design
Solving real-world problems
Code Trail4 of 6
Master the implementation of crucial algorithms such as BFS, DFS, and Dynamic Programming. This trail bridges theoretical knowledge with practical application, enabling you to solve real-world problems efficiently.
6 Chapters
Concept Explanations & Warm up problems
Learn concepts with detailed explanations and illustrations. Wrap up with practice problems to apply what you've learned.
Challenge Problems
Apply what you've learned to various practice problems and identify any gaps in your understanding.
Test Problems
The final stage of each chapter consists of test problems that you must solve on your own, without solutions or discussion features. With a slightly higher difficulty level, these problems help you refine your skills.
1
Advanced Simulation
Learn how to perform exhaustive searches and implement complex operations like rotation and movement on two-dimensional grids.
Exhaustive Search
Shift
Bomb & Drop
Moving a single object within a grid
Moving multiple objects within a grid
2
Backtracking
Explore how to use recursive functions to enumerate all the combinations that satisfy specific conditions.
K Way Exhaustive Search (Simple)
K Way Exhaustive Search (Conditional)
Choose M out of N
Permutation
3
Depth-First Search
Learn how to apply DFS to graphs and solve a variety of problems using this technique.
DFS
4
Breadth-First Search
Discover how to use BFS on graphs and tackle different types of problems with this approach.
Traversal
Equal Weighted Graph
5
Dynamic Programming I
Explore a wide range of basic Dynamic Programming problems and their solutions.
Call themselves that have same shape and merge
One Object Moving in Grid
Optionally Select that meets the conditions
Selecting Items appropriately
6
Dynamic Programming II
Learn how to define appropriate states for more challenging Dynamic Programming problems.
Define state to apply condition
Linear Shape
String Matching
Significantly reduce coding errors and improve code accuracy
Confidently implement correct solutions that meet problem requirements
Master problem-solving techniques for permutations, combinations, graph traversals, and simulations
Solve approximately half of the problems in typical coding tests with confidence
Develop a structured approach to tackling complex algorithmic challenges
Understand data structure and algorithm concepts but lack practical problem-solving experience
Want to reduce the frequency of coding mistakes
Aim to improve efficiency in solving simulation problems
Seek to enhance skills in generating permutations and combinations
Want to become proficient in graph traversal and shortest path algorithms
Need more practice with BFS and its applications in various problem types
Desire to bridge the gap between theoretical knowledge and practical implementation