Algorithmic Problem Solving in C++: Graphs and Dynamic Programming

Transitioning from basic programming to designing highly efficient algorithms is the key to solving complex computational problems. This course guides you through the core concepts of graph theory, dynamic programming, and optimization techniques using C++. You will transition from writing simple code to analyzing and implementing complex algorithms with optimal time and space complexity. Through step-by-step written explanations, clear pseudocode, and practical code walkthroughs, you will learn how to approach difficult algorithmic challenges with confidence and write clean, efficient C++ code. What you'll learn: - Understand the foundations of recursion, backtracking, and divide-and-conquer strategies - Implement dynamic programming patterns including pick-or-leave, range queries, and grid optimization - Solve shortest-path problems using Dijkstra, Bellman-Ford, and Floyd-Warshall algorithms - Construct minimum spanning trees using Prim's and Kruskal's approaches - Apply modern C++ features like std::span and optimized standard containers to algorithmic design - Analyze the time and space complexity of complex recursive and graph-based solutions The course starts with fundamental concepts of recursion and backtracking before moving into advanced graph theory and dynamic programming. You will progress from foundational definitions to comprehensive, step-by-step written code implementations and analytical exercises. This course is designed for learners who have a basic understanding of C++ syntax and standard library containers and want to build strong algorithmic problem-solving skills. No prior advanced algorithm experience is required. Start mastering complex algorithms and write highly optimized C++ code today.