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This course is about designing algorithms for computational problems, and how to think clearly about analyzing correctness and running time. The main goal of this course is to provide the intellectual tools needed for designing and analyzing your own algorithms for new problems you need to solve in the future. The course explores fundamental algorithm design techniques such as greedy, divide and conquer, dynamic programming, network flow, reduction, approximation, linear programming and randomization for efficient algorithm construction. The course describes Turing machines and explains what NP-completeness means with respect to possibilities for solving these problems efficiently.

• Q&A

  • Chapter 1 Review
  • Chapter 2 Amortized Analysis
  • Chapter 3 Heaps
  • Chapter 4 Greedy Algorithm
  • Chapter 5 Divide-and-Conquer Algorithm
  • Chapter 6 Dynamic Programming
  • Chapter 7 Network Flow
  • Chapter 8 Linear Programming
  • Chapter 9 NP Completeness

• Java

  • 7 Basic Sorting Algorithms in Java (七个基础的排序算法)
  • Design Pattern in Java (设计模式Java版)

• Python

  • 7 Basic Sorting Algorithms in Python (七个基础的排序算法)
  • Design Pattern in Python (设计模式Python版)