Advanced Algorithm Design · Algorithm Design

Greedy Algorithm

Greedy algorithms make the locally best choice at each step, but students must learn when that local choice is globally valid.

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Student Focus

We teach greedy methods alongside counterexamples so students do not overapply them.

Guided Lesson Notes

Understanding Greedy Algorithm

Greedy Algorithm focuses on algorithmic reasoning, correctness, and choosing the right structure before writing code. Greedy algorithms make the locally best choice at each step, but students must learn when that local choice is globally valid.

The mental model is this: start with inputs and outputs, then decide what information must be remembered at each step. That picture matters because it tells the student what information is available immediately and what must be searched, stored, or recomputed.

The core invariant is that every step must preserve the meaning of the variables and move measurably closer to the goal. If a solution cannot state that rule, the code may still run on a sample input but fail on edge cases.

A strong implementation usually uses pseudocode, traces, helper functions, tests, and complexity checks. The goal is not just to memorize an API; the goal is to know why each operation is allowed and what it costs.

In competitive programming, Greedy Algorithm tends to appear when the problem is less about syntax and more about selecting a strategy that scales. Spotting that signal is often the difference between a nested-loop solution and an efficient one.

Visual Model

A small picture for Greedy Algorithm

Model

Trace

Analyze

Key Ideas

Local choice strategy
Counterexamples
Exchange-style intuition

Practice Prompts

Solve interval scheduling greedily.
Find a problem where an obvious greedy choice fails.

Vocabulary

Terms students should be able to say clearly

Input

The data the algorithm receives.

Output

The result the algorithm must produce.

Precondition

A fact that must be true before the algorithm runs.

Correctness

Why the algorithm always returns the required answer.

Complexity

How time and memory grow with input size.

Tradeoff

A choice where improving one cost may worsen another.

Worked Example

Worked example: tracing Greedy Algorithm

Use a tiny input and focus on local choice strategy. The goal is to see how the topic changes state before scaling it to a full problem.

1Write a small input where Greedy Algorithm is clearly useful.
2Label the part of the input related to Local choice strategy.
3Perform one operation and explain which invariant is still true afterward.
4Run a second operation that touches an edge case, such as an empty side, duplicate value, boundary index, disconnected vertex, or tie.
5Finish by saying which operation dominates the runtime and why.

Complexity Check

Costs students should be able to explain

Operation	Typical Cost	Reason
Trace a small input	Small n	Use a tiny example to understand state changes before coding.
Implement the core idea	Depends on topic	Choose data structures that match the operations you need.
Analyze growth	Big-O	Explain how work changes as the input gets larger.

Common Mistakes

What to watch while practicing

Coding before drawing the structure or state changes.
Forgetting the invariant that makes the algorithm correct.
Testing only the sample input and skipping boundary cases.
Giving Big-O without explaining which operation dominates the work.

Interview-Style Coding Problem

Greedy Algorithm interview problem: Maximum Product Subarray

Interview medium

Problem

Given an integer array that may contain negative values and zeros, find the maximum product of a non-empty contiguous subarray.

Input

The first line contains n. The second line contains n integers.

Output

Print the maximum product.

Sample Input

6
-2 3 -4 0 -1 -2

Sample Output

Why the sample works

The subarray -2, 3, -4 has product 24, which is the maximum.

Approach

1Track both the maximum and minimum product ending at the current index.
2A negative value can turn the minimum product into the new maximum product.
3Swap the two trackers when the current value is negative.
4Update the global best after processing each value.

O(n) time and O(1) extra space.

Java Solution

import java.util.*;

public class Main {
  public static void main(String[] args) {
    Scanner sc = new Scanner(System.in);
    int n = sc.nextInt();
    long currentMax = sc.nextLong();
    long currentMin = currentMax;
    long answer = currentMax;

    for (int i = 1; i < n; i++) {
      long x = sc.nextLong();
      if (x < 0) {
        long temp = currentMax;
        currentMax = currentMin;
        currentMin = temp;
      }

      currentMax = Math.max(x, currentMax * x);
      currentMin = Math.min(x, currentMin * x);
      answer = Math.max(answer, currentMax);
    }

    System.out.println(answer);
  }
}

Python Solution

n = int(input())
a = list(map(int, input().split()))

current_max = a[0]
current_min = a[0]
answer = a[0]

for x in a[1:]:
    if x < 0:
        current_max, current_min = current_min, current_max

    current_max = max(x, current_max * x)
    current_min = min(x, current_min * x)
    answer = max(answer, current_max)

print(answer)

Practice Challenge

Make the idea your own

Create a two-minute explanation of Greedy Algorithm: define it, trace one example, name one edge case, and give the runtime of the main operation.

Tutoring Connection

Turn the topic into usable problem-solving skill

Students can use this page before a lesson, after a difficult homework assignment, or while preparing for AP Computer Science A extensions, Advanced Topics in CS, USACO growth, or a college data structures course.

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