CS61B Textbook 2025
  • Contributors
  • DISCLAIMER
  • 1. Introduction
    • 1.1 Your First Java Program
    • 1.2 Java Workflow
    • 1.3 Basic Java Features
    • 1.4 Exercises
  • 2. Defining and Using Classes
  • 3. References, Recursion, and Lists
  • 4. SLLists
  • 5. DLLists
  • 6. Arrays
  • 7. Testing
  • 8. ArrayList
  • 9. Inheritance I: Interface and Implementation Inheritance
    • 9.1 The Problem of Generality
    • 9.2 Hypernyms, Hyponyms, and the Implements Keyword
    • 9.3 Overriding, Interface Inheritance
    • 9.4 Implementation Inheritance, default
    • 9.5 Implementation vs. Interface Inheritance
    • 9.6 Abstract Data Types
  • 10. Inheritance II: Subtype Polymorphism, Comparators, Comparables, Generic Functions
    • 10.1 Polymorphism vs. Function Passing
    • 10.2 Comparables and Comparators
    • 10.3 Writing a Max Function
    • 10.4 Summary
  • 11. There is no chapter 11.
  • 12. Inheritance III: Iterators, Object Methods
    • 12.1 Lists and Sets in Java
    • 12.2 Exceptions
    • 12.3 Iteration
    • 12.4 Object Methods
    • 12.5 Chapter Summary
    • 12.6 Exercises
  • 13. Asymptotics I
    • 13.1 An Introduction to Asymptotic Analysis
    • 13.2 Runtime Characterization
    • 13.3 Checkpoint: An Exercise
    • 13.4 Asymptotic Behavior
    • 13.6 Simplified Analysis Process
    • 13.7 Big-Theta
    • 13.8 Big-O
    • 13.9 Summary
    • 13.10 Exercises
  • 14. Disjoint Sets
    • 14.1 Introduction
    • 14.2 Quick Find
    • 14.3 Quick Union
    • 14.4 Weighted Quick Union (WQU)
    • 14.5 Weighted Quick Union with Path Compression
    • 14.6 Exercises
  • 15. Asymptotics II
    • 15.1 For Loops
    • 15.2 Recursion
    • 15.3 Binary Search
    • 15.4 Mergesort
    • 15.5 Summary
    • 15.6 Exercises
  • 16. ADTs and BSTs
    • 16.2 Binary Search Trees
    • 16.3 BST Definitions
    • 16.4 BST Operations
    • 16.5 BSTs as Sets and Maps
    • 16.6 Summary
    • 16.7 Exercises
  • 17. B-Trees
    • 17.1 BST Performance
    • 17.2 Big O vs. Worst Case
    • 17.3 B-Tree Operations
    • 17.4 B-Tree Invariants
    • 17.5 B-Tree Performance
    • 17.6 Summary
    • 17.7 Exercises
  • 18. Red Black Trees
    • 18.1 Rotating Trees
    • 18.2 Creating LLRB Trees
    • 18.3 Inserting LLRB Trees
    • 18.4 Runtime Analysis
    • 18.5 Summary
    • 18.6 Exercises
  • 19. Hashing I
    • 19.1 Introduction to Hashing: Data Indexed Arrays
      • 19.1.1 A first attempt: DataIndexedIntegerSet
      • 19.1.2 A second attempt: DataIndexedWordSet
      • 19.1.3 A third attempt: DataIndexedStringSet
    • 19.2 Hash Code
    • 19.3 "Valid" & "Good" Hashcodes
    • 19.4 Handling Collisions: Linear Probing and External Chaining
    • 19.5 Resizing & Hash Table Performance
    • 19.6 Summary
    • 19.7 Exercises
  • 20. Hashing II
    • 20.1 Hash Table Recap, Default Hash Function
    • 20.2 Distribution By Other Hash Functions
    • 20.3 Contains & Duplicate Items
    • 20.4 Mutable vs. Immutable Types
  • 21. Heaps and Priority Queues
    • 21.1 Priority Queues
    • 21.2 Heaps
    • 21.3 PQ Implementation
    • 21.4 Summary
    • 21.5 Exercises
  • 22. Tree Traversals and Graphs
    • 22.1 Tree Recap
    • 22.2 Tree Traversals
    • 22.3 Graphs
    • 22.4 Graph Problems
  • 23. Graph Traversals and Implementations
    • 23.1 BFS & DFS
    • 23.2 Representing Graphs
    • 23.3 Summary
    • 23.4 Exercises
  • 24. Shortest Paths
    • 24.1 Introduction
    • 24.2 Dijkstra's Algorithm
    • 24.3 A* Algorithm
    • 24.4 Summary
    • 24.5 Exercises
  • 25. Minimum Spanning Trees
    • 25.1 MSTs and Cut Property
    • 25.2 Prim's Algorithm
    • 25.3 Kruskal's Algorithm
    • 25.4 Chapter Summary
    • 25.5 MST Exercises
  • 26. Prefix Operations and Tries
    • 26.1 Introduction to Tries
    • 26.2 Trie Implementation
    • 26.3 Trie String Operations
    • 26.4 Summary
    • 26.5 Exercises
  • 27. Software Engineering I
    • 27.1 Introduction to Software Engineering
    • 27.2 Complexity
    • 27.3 Strategic vs Tactical Programming
    • 27.4 Real World Examples
    • 27.5 Summary, Exercises
  • 28. Reductions and Decomposition
    • 28.1 Topological Sorts and DAGs
    • 28.2 Shortest Paths on DAGs
    • 28.3 Longest Path
    • 28.4 Reductions and Decomposition
    • 28.5 Exercises
  • 29. Basic Sorts
    • 29.1 The Sorting Problem
    • 29.2 Selection Sort & Heapsort
    • 29.3 Mergesort
    • 29.4 Insertion Sort
    • 29.5 Summary
    • 29.6 Exercises
  • 30. Quicksort
    • 30.1 Partitioning
    • 30.2 Quicksort Algorithm
    • 30.3 Quicksort Performance Caveats
    • 30.4 Summary
    • 30.5 Exercises
  • 31. Software Engineering II
    • 31.1 Complexity II
    • 31.2 Sources of Complexity
    • 31.3 Modular Design
    • 31.4 Teamwork
    • 31.5 Exerises
  • 32. More Quick Sort, Sorting Summary
    • 32.1 Quicksort Flavors vs. MergeSort
    • 32.2 Quick Select
    • 32.3 Stability, Adaptiveness, and Optimization
    • 32.4 Summary
    • 32.5 Exercises
  • 33. Software Engineering III
    • 33.1 Candy Crush, SnapChat, and Friends
    • 33.2 The Ledger of Harms
    • 33.3 Your Life
    • 33.4 Summary
    • 33.5 Exercises
  • 34. Sorting and Algorithmic Bounds
    • 34.1 Sorting Summary
    • 34.2 Math Problems Out of Nowhere
    • 34.3 Theoretical Bounds on Sorting
    • 34.4 Summary
    • 34.5 Exercises
  • 35. Radix Sorts
    • 35.1 Counting Sort
    • 35.2 LSD Radix Sort
    • 35.3 MSD Radix Sort
    • 35.4 Summary
    • 35.5 Exercises
  • 36. Sorting and Data Structures Conclusion
    • 36.1 Radix vs. Comparison Sorting
    • 36.2 The Just-In-Time Compiler
    • 36.3 Radix Sorting Integers
    • 36.4 Summary
    • 36.5 Exercises
  • 37. Software Engineering IV
    • 37.1 The end is near
  • 38. Compression and Complexity
    • 38.1 Introduction to Compression
    • 38.2 Prefix-free Codes
    • 38.3 Shannon-Fano Codes
    • 38.4 Huffman Coding Conceptuals
    • 38.5 Compression Theory
    • 38.6 LZW Compression
    • 38.7 Summary
    • 38.8 Exercises
  • 39. Compression, Complexity, P = NP
    • 39.1 Models of Compression
    • 39.2 Optimal Compression, Kolmogorov Complexity
    • 39.3 Space/Time-Bounded Compression
    • 39.4 P = NP
    • 39.5 Exercises
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On this page
  • Generic Functions (Warumup)
  • Approach One: Generics
  • Approach Two: Generic Static Methods
  • Writing our Max Function, Type Bounds
  • Bonus: An Even Better Type Bound
  1. 10. Inheritance II: Subtype Polymorphism, Comparators, Comparables, Generic Functions

10.3 Writing a Max Function

We've seen that Java provides a nice Collections.max function for us. Collections also includes other handy functions like min, sort, and reverse.

In this chapter we'll see how we can write such functions ourselves.

Generic Functions (Warumup)

As a warmup suppose we want a function which picks a random item from an array. Some code is shown below:

public class RandomPicker {
    public static <T> T randomItem(T[] items) {
        Random random = new Random();
        int randomIndex = random.nextInt(x.length);
        return x[randomIndex];
    }
}

And a sample program that uses this function is given below:

public class RandomPickerDemo {
   public static void main(String[] args) {
       String[] x = {"hi", "little", "cat"};
       System.out.println(RandomPicker.pickRandom(x));
   }
}

Next, we'll try to make it so that the pickRandom function can work on any type of array.

Approach One: Generics

We can try to use Generics, as shown in the code below:

public class RandomPicker<T> {
   public static T pickRandom(T[] x) {
       Random random = new Random();
       int randomIndex = random.nextInt(x.length);
       return x[randomIndex];
   }
}

However, this code doesn't really make sense as written. The pickRandom function is static, but we have to instantiate a RandomPicker object to specify the generic type. That is, we'd need to do something like:

String[] x = {"hi", "little", "cat"};
RandomPicker<String> stringPicker = new RandomPicker<>();
System.out.println(stringPicker.pickRandom(x));

but we can't call static methods using an instance.

A fix for this is to make the pickRandom function non-static, as shown below:

public class RandomPicker<T> {
   public T pickRandom(T[] x) {
       Random random = new Random();
       int randomIndex = random.nextInt(x.length);
       return x[randomIndex];
   }
}

If we do this our code will work fine, but it's still very awkward to have to instantate a picker object.

Approach Two: Generic Static Methods

The correct and better approach is to make our static method generic. This is shown below:

public class RandomPicker {
   public static <T> T pickRandom(T[] x) {
       Random random = new Random();
       int randomIndex = random.nextInt(x.length);
       return x[randomIndex];
   }
}

This syntax is confusing (and it'll get worse later!). The way you can read public static <T> T pickRandom(T[] x) is: "I am declaring a public static function that works on objects of type , it returns a T, it is called pickRandom, and it takes an array of Ts as its input.

After noting to Java that our static method is generic, we can call it without instantiating a RandomPicker object as shown below:

public class RandomPickerDemo {
   public static void main(String[] args) {
       String[] x = {"hi", "little", "cat"};
       System.out.println(RandomPicker.pickRandom(x));
   }
}

Note that unlike generic classes, we don't need to specify the generic type when calling a generic static method (i.e. there's no <String> after pickRandom). The type is automatically inferred.

One minor annoyance is that our pickRnadom function will not work with arrays of primitive types, i.e. we can use int[], double[], float[].

public class RandomPickerDemo {
   public static void main(String[] args) {
       int[] x = {1, 2, 3, 4, 5, 6, 7, 8};
       System.out.println(RandomPicker.pickRandom(x));
   }
}

There's no way around this, and in fact real Java libraries often have multiple functions, one for each primitive type. For example, there exists Arrays.sort(int[]), Arrays.sort(double[]), Arrays.sort(float[]), etc. One day maybe Project Valhalla will fix this.

Writing our Max Function, Type Bounds

Using the idea of a generic static function, we can write our max function as follows:

public class Maximizer {
   public static <T> T max(T[] items) {
       T maxItem = items[0];
       for (int i = 0; i < items.length; i += 1) {
           int cmp = items[i].compareTo(maxItem);
           if (cmp > 0) {
               maxItem = items[i];
           }
       }
       return maxItem;
   }
}

There's one last problem, Java doesn't know that T has a compareTo method. We can fix this by adding a type bound to our generic type, as shown below:

public class Maximizer {
   public static <T extends Comparable<T>> T max(T[] items) {
       T maxItem = items[0];
       for (int i = 0; i < items.length; i += 1) {
           int cmp = items[i].compareTo(maxItem);
           if (cmp > 0) {
               maxItem = items[i];
           }
       }
       return maxItem;
   }
}

This tells Java that T, whatever it is, has to implement Comparable<T>. If you try to pass an arary of objects into max that does not implement Comparable<T>, you'll get a compile-time error.

The way to read public static <T extends Comparable<T>> T max(T[] items) is: "I am declaring a public static function that works on objects of type , it returns a T, it is called max, and it takes an array of Ts as its input. Additionally, T has to implement Comparable."

Bonus: An Even Better Type Bound

In real industrial strength Java code, the correct way to specify our method is:

public class Maximizer {
   public static <T extends Comparable<? super T>> T max(T[] items) {
		...
   }
}
Previous10.2 Comparables and ComparatorsNext10.4 Summary

Last updated 3 months ago