Midterm 2 Information Information and list of topics (Preliminary) - CompSci 161, Spring 2026 (Dillencourt)

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NOTE: This is the finalized version.

List of topics

The test will cover the material covered in the first five sets of lecture notes and the sixth set of lecture notes up through and including slide 6-30.

The following is a list of topics that may be covered on the Midterm 2. It lists the primary coverage area of this test. The test will not contain questions that focus exclusively on earlier material, but some knowledge of earlier material may be necessary. Midterm 1 covered up through the basic operations on binary heaps. The last slide covered in the Midterm 1 syllabus was slide 3-13, the ExtractMax operation. So primary Midterm 2 coverage begins with heap construction, which is covered in the class notes starting with slide 3-14.

Note: There may be questions about the mechanics of algorithms that were covered in the lectures and in the lecture notes. For these questions, you will need to know the algorithms as described in the lectures and in the lecture notes.

• Comparison-based sorting (starting with slide 3-14):
  • Binary heaps: construction (heapify)
  • Heap sort
  • Summary/Comparison of comparison-based sorting methods
  • Stable sorting
  • Lower bounds on comparison-based sorting / decision tree argument
  • Optimally sorting 5 elements
• Address-calculation sorting:
  • Counting sort
  • Bucket sort
  • Radix sort
• External sorting
  • Polyphase Merge
  • Replacement selection (enhancement to polyphase merge)
• Greedy algorithms:
  • Fractional knapsack
  • Task scheduling to minimize the number of processors required
  • Task scheduling on a uniprocessor to maximize the number of tasks that can be performed
  • Huffman trees and Huffman coding
• Divide and conquer
  • Setting up a divide-and-conquer recurrence equation to describe the running time of an algorithm
  • Solving a divide-and-conquer recurrence equation
    • Simplified method
    • Master method
  • Binary search
  • Merge sort
  • Constructing a binary heap
  • Integer multiplication
  • Strassen's method:
    • You do not need to know the detailed equations for Strassen's method
    • You are expected to know that it is possible to multiply two 2-by-2 matrices with 7 scalar multiplications, and understand why this results in a more efficient algorithm for matrix multiplication
• Dynamic Programming
  • The basic approach:
    • Recursion vs. memorized recursion vs. dynamic programming
    • Expressing the solutions: subproblems, description of function, goal, initial conditions, recurrence equation
    • Modifying the program to obtain the optimum strategy in addition to the optimum value
  • Specific dynamic programming algorithms:
    • Optimal Weighted-interval scheduling
    • Truck-loading problem
    • 0/1 Knapsack problem