ISSUE#2.2 - LEO - ISSUE DEVELOPMENT

[ba-00001]

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Algorithm Development Template

ISSUE#: [Issue Number]
Title: [Issue Title]
Prepared by: [Developer Name]
Date: [MM/DD/YYYY]

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1. Problem Definition and Clarification

• Objective: Clearly state the problem that the algorithm needs to solve.
• Key Questions:
  • What is the problem to be solved?
  • What are the expected outcomes?
• Sub-tasks: Break down the issue into smaller tasks.

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2. Input/Output Specifications

• Input Data:
  • Define the required input data types, format, and constraints.
• Expected Output:
  • Specify what the algorithm should return or produce after execution.

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3. Constraints and Requirements

• Time Complexity: What is the acceptable time complexity for the solution?
• Space Complexity: Define memory usage constraints.
• Resource Constraints:
  • CPU, memory, and other hardware limitations.
• Ethical Considerations (if applicable):
  • How will the algorithm ensure fairness, privacy, or comply with legal requirements?

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4. Data Structures

• Chosen Data Structures: List the data structures (arrays, trees, hash maps, etc.) that are ideal for solving the problem and why.
• Justification: Explain why these data structures will improve efficiency.

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5. Algorithm Design Approach

• Design Paradigm:
  • Choose one or more design paradigms (e.g., Greedy, Divide and Conquer, Dynamic Programming, etc.).
  • Explain why the paradigm is suitable for this problem.
• Modular Design:
  • Break down the algorithm into reusable functions or components for clarity and maintainability.

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6. Edge Cases and Error Handling

• Edge Cases:
  • Identify possible edge cases (e.g., null values, empty inputs, extreme data).
• Error Handling:
  • Include strategies to handle invalid input or unexpected behavior.

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7. Optimization

• Initial Version: Describe the basic working version of the algorithm.
• Optimization Techniques: List any steps taken to optimize the algorithm for better performance (e.g., memoization, pruning, parallelization).

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8. Testing and Debugging

• Test Cases:
  • Typical input cases.
  • Boundary cases.
  • Edge cases.
• Debugging Tools: Specify the tools or strategies used for debugging.

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9. Documentation and Maintenance

• Documentation:
  • Provide clear explanations of the algorithm, its functions, and how to use it.
• Maintenance Plan:
  • Outline plans for future updates, bug fixes, and ongoing improvements.

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Comments/Notes

[Additional notes or comments regarding the issue development.]

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Version History

Date	Changes Made	Version
MM/DD/YYYY	Initial Draft	1.0
MM/DD/YYYY	[Describe subsequent changes]	X.X

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[leochirinos10]

ISSUE#: TheAlgorithms#5636 Title: Moore's Voting Algorithm Prepared by: Leonardo Chirinos Date: 10/23/2024

1. Problem Definition and Clarification

Objective: Implement Moore's Voting Algorithm for finding the majority element in an array, where the majority element is defined as an element that appears more than half the time.

Key Questions: What is the problem to be solved?

• Find the majority element in an array.

What are the expected outcomes?

• The algorithm should return candidate if it is the majority element, otherwise return -1.

Sub-tasks: Break down the issue into smaller tasks. I: write the findMajorityElement() function which takes an int array as a parameter and returns an int candidate if there is a candidate found, otherwise return -1.

II: write the findCandidate() function which identifies a potential candidate for the majority element. It iterates through the array, adjusting the candidate's count based on matching elements.

III: write the isMajority() function which verifies whether the candidate occurs more than n/2 times in the array.

2. Input/Output Specifications

Input Data: The input will be an array of ints.

Expected Output: The expected output will be the int candidate, which is the majority element in the array.

3. Constraints and Requirements

Time Complexity: The time complexity of this implementation is O(n), where n is the number of elements in the array.

Space Complexity: The space complexity is O(1) since we only use a few extra variables.

Resource Constraints: There are no special CPU or memory constraints, as the algorithm efficiently operates in linear time and constant space.

Ethical Considerations (if applicable): No ethical considerations for this algorithm.

4. Data Structures

Chosen Data Structures: The algorithm takes in an array and using an enhanced for loop it iterates through the array.

Justification: No additional data structures are needed for this algorithm.

5. Edge Cases and Error Handling

Edge Cases:

• The array has no majority elements
• The array has one majority element

Error Handling:

• If the array has no majority element, the algorithm should return -1.
• No exceptions should be thrown, as all cases (including null lists) are properly handled.

6. Optimization

Initial Version: The initial version of the algorithm implements the algorithm that iterates through the array, with no further optimization required.

Optimization Techniques: No significant optimizations are needed, as this approach already achieves the best time and space complexity for the problem.

7. Testing and Debugging

Test Cases:

Typical input cases.

• An array with a majority element.
• An array without a majority element.

Debugging Tools

• Assertions like assertTrue() and assertFalse() to check the correctness of the algorithm's output.

8. Documentation and Maintenance

Documentation:

• The class findMajorityElement() contains test cases to ensure correctness.

Maintenance Plan:

• The current implementation is sufficient to find the majority element.

Comments/Notes

• This algorithm is optimal in both time and space, making it suitable for scenarios where memory usage is a concern.