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JAVA CHEAT SHEET AND ALGO., Cheat Sheet of Algorithms and Programming

Indira Gandhi National Open UniversityAlgorithms and Programming

JAVA ALGO. CHEAT SHEET TO LEARN MORE ABOUT DATA SCIENCE AND TECHNOLOGY

Typology: Cheat Sheet

2022/2023

Uploaded on 08/04/2023

keshavkashyap104092
keshavkashyap104092 🇮🇳

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1. What is an algorithm?
• An algorithm is a step-by-step procedure or a set of rules for
solving a specific problem or accomplishing a particular task.
2. What are the key properties of a good algorithm?
• The key properties of a good algorithm are correctness,
efficiency, clarity, and generality. A good algorithm should
produce the correct output, be efficient in terms of time and
space complexity, be easy to understand, and applicable to a
wide range of inputs.
3. What is the difference between an algorithm and a program?
• An algorithm is a high-level description of a solution to a
problem, whereas a program is the implementation of that
algorithm in a specific programming language.
4. What is the time complexity of an algorithm?
• Time complexity measures the amount of time an algorithm
takes to run as a function of the input size. It helps analyze how
the running time increases with the size of the input.
5. What is the space complexity of an algorithm?
• Space complexity measures the amount of memory an
algorithm requires as a function of the input size. It helps
analyze how the memory usage increases with the size of the
input.
6. What is the "Big O" notation used for in algorithm analysis?
• "Big O" notation is used to express the upper bound or worst-
case time complexity of an algorithm. It provides a way to
describe how the algorithm's performance grows as the input
size increases.
7. What is the difference between the average-case and worst-case
time complexity of an algorithm?
• The worst-case time complexity represents the maximum
amount of time an algorithm takes to complete for any given
input of size 'n'. The average-case time complexity represents
the expected or average amount of time an algorithm takes to
complete for random inputs of size 'n'.
8. What is the difference between recursion and iteration?
• Recursion is a technique where a function calls itself to solve a
smaller version of the problem until it reaches the base case.
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Download JAVA CHEAT SHEET AND ALGO. and more Cheat Sheet Algorithms and Programming in PDF only on Docsity!

1. What is an algorithm?

• An algorithm is a step-by-step procedure or a set of rules for

solving a specific problem or accomplishing a particular task.

2. What are the key properties of a good algorithm?

• The key properties of a good algorithm are correctness,

efficiency, clarity, and generality. A good algorithm should

produce the correct output, be efficient in terms of time and

space complexity, be easy to understand, and applicable to a

wide range of inputs.

3. What is the difference between an algorithm and a program?

• An algorithm is a high-level description of a solution to a

problem, whereas a program is the implementation of that

algorithm in a specific programming language.

4. What is the time complexity of an algorithm?

• Time complexity measures the amount of time an algorithm

takes to run as a function of the input size. It helps analyze how

the running time increases with the size of the input.

5. What is the space complexity of an algorithm?

• Space complexity measures the amount of memory an

algorithm requires as a function of the input size. It helps

analyze how the memory usage increases with the size of the

input.

6. What is the "Big O" notation used for in algorithm analysis?

• "Big O" notation is used to express the upper bound or worst-

case time complexity of an algorithm. It provides a way to

describe how the algorithm's performance grows as the input

size increases.

7. What is the difference between the average-case and worst-case

time complexity of an algorithm?

• The worst-case time complexity represents the maximum

amount of time an algorithm takes to complete for any given

input of size 'n'. The average-case time complexity represents

the expected or average amount of time an algorithm takes to

complete for random inputs of size 'n'.

8. What is the difference between recursion and iteration?

• Recursion is a technique where a function calls itself to solve a

smaller version of the problem until it reaches the base case.

Iteration, on the other hand, uses loops to repeat a set of

instructions until a certain condition is met.

9. What is a sorting algorithm? Can you name some sorting

algorithms?

• A sorting algorithm arranges elements in a specific order (e.g.,

ascending or descending). Some sorting algorithms are Bubble

Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort, and

Heap Sort.

10. Explain the Divide and Conquer strategy used in algorithms.

• Divide and Conquer is a problem-solving technique where a

problem is divided into smaller subproblems that are solved

independently. Then, the solutions of the subproblems are

combined to solve the original problem.

  1. What is a sorting algorithm?
    • A sorting algorithm is an algorithm that arranges elements in a specific order, such as ascending or descending, based on a certain comparison criterion.
  2. What is the importance of sorting algorithms?
    • Sorting algorithms are essential as they help in organizing data, making it easier to search, retrieve, and analyze information efficiently.
  3. What is the difference between comparison-based and non-comparison based sorting algorithms?
    • Comparison-based sorting algorithms compare elements using comparison operators (e.g., greater than, less than) to determine their order. Non-comparison based sorting algorithms, such as counting sort or radix sort, use other techniques that do not rely on direct comparisons.
  4. Explain the concept of stability in sorting algorithms.
    • Stability in sorting algorithms means that the relative order of equal elements remains the same in the sorted output as it was in the original input.
  5. Name some comparison-based sorting algorithms.
    • Some comparison-based sorting algorithms include Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort, and Heap Sort.
  6. What is the time complexity of the Bubble Sort algorithm?
    • The time complexity of Bubble Sort is O(n^2) in the worst and average cases and O(n) in the best case (when the input is already sorted).
  7. Explain how the Merge Sort algorithm works.
    • Merge Sort is a divide-and-conquer sorting algorithm. It divides the unsorted list into n sublists, each containing one element, and then
  • Binary Search is applicable when the data collection is sorted, as it relies on dividing the search space in half at each step. If the data is unsorted, the algorithm will not work correctly.
  1. What are BFS and DFS, and what are they used for?
  • BFS (Breadth-First Search) and DFS (Depth-First Search) are graph traversal algorithms used to visit all the vertices (nodes) of a graph. They help explore and search for elements in a graph, and are widely used in various applications, such as finding the shortest path, connected components, and spanning trees.
  1. Explain how BFS algorithm works.
  • BFS starts from a selected node and explores all its neighbors before moving on to their neighbors. It explores vertices level by level, following a breadth-wise approach. It uses a queue data structure to keep track of the nodes to be visited.
  1. Explain how DFS algorithm works.
  • DFS starts from a selected node and explores as far as possible along each branch before backtracking. It explores vertices in depth-first manner, going as deep as possible before backtracking to explore other branches. It uses a stack data structure (can be implemented using recursion as well) to keep track of the nodes to be visited.
  1. What is the difference between BFS and DFS?
  • The main difference lies in their exploration order. BFS explores level by level, while DFS goes as deep as possible before backtracking. As a result, BFS guarantees finding the shortest path in unweighted graphs, whereas DFS may not.
  1. What is the time complexity of BFS and DFS for graph traversal?
  • Both BFS and DFS have a time complexity of O(V + E), where V is the number of vertices and E is the number of edges in the graph.
  1. Which data structures are used in BFS and DFS?
  • BFS uses a queue to keep track of nodes to be visited, while DFS uses a stack (implemented through recursion or an explicit stack) to keep track of nodes.
  1. In which scenarios would you choose BFS over DFS, and vice versa?
  • Use BFS when you need to find the shortest path in unweighted graphs or when you want to explore nodes in level-order. Use DFS when you need to explore deeply rooted paths, such as in maze-solving or backtracking problems.
  1. Can you find the shortest path in a weighted graph using BFS?
  • No, BFS can only find the shortest path in an unweighted graph. For weighted graphs, you need to use algorithms like Dijkstra's or Bellman- Ford.
  1. What is the space complexity of BFS and DFS?
  • Both BFS and DFS have a space complexity of O(V) as they need to store the visited nodes, where V is the number of vertices in the graph.
  1. Can BFS and DFS be applied to directed and undirected graphs?
  • Yes, both BFS and DFS can be applied to both directed and undirected graphs without any modifications.