Design and Analysis of Algorithms (DAA) Tutorial

Our DAA Tutorial is designed for beginners and professionals both.

Our DAA Tutorial includes all topics of algorithm, asymptotic analysis, algorithm control structure, recurrence, master method, recursion tree method, simple sorting algorithm, bubble sort, selection sort, insertion sort, divide and conquer, binary search, merge sort, counting sort, lower bound theory etc.

What is Algorithm?

A finite set of instruction that specifies a sequence of operation is to be carried out in order to solve a specific problem or class of problems is called an Algorithm.

Why study Algorithm?

As the speed of processor increases, performance is frequently said to be less central than other software quality characteristics (e.g. security, extensibility, reusability etc.). However, large problem sizes are commonplace in the area of computational science, which makes performance a very important factor. This is because longer computation time, to name a few mean slower results, less through research and higher cost of computation (if buying CPU Hours from an external party). The study of Algorithm, therefore, gives us a language to express performance as a function of problem size.

DAA Tutorial Index

Prerequisite

Before learning DAA Tutorial, you must have the basic knowledge of Data Structure, Programming and Mathematics.

Audience

Our DAA Tutorial is designed to help beginners and professionals.

Problems

We assure that you will not find any problem in this DAA Tutorial. But if there is any mistake, please post the problem in contact form.

MCQ Exercise on Design and Analysis of Algorithms

Question 1: Which of the following best defines an algorithm?

1. A large set of random instructions.
2. A finite set of instructions that specifies a sequence of operations to solve a specific problem or class of problems.
3. A set of programs written in a high-level programming language.
4. An infinite loop of instructions.

Question 2: Which notation is used to describe the upper bound of an algorithm's running time?

1. Θ (Theta)
2. Ω (Omega)
3. O (Big O)
4. o (Small o)

Question 3: Which sorting algorithm has the best average-case time complexity?

1. Bubble Sort
2. Selection Sort
3. Insertion Sort
4. Merge Sort

Question 4: In the Divide and Conquer method, which of the following algorithms helps find a specific value within a sorted array?

1. Bubble Sort
2. Binary Search
3. Quick Sort
4. Insertion Sort

Question 5: Which problem-solving approach is particularly useful for optimization problems where the problem can be broken down into overlapping subproblems?

1. Greedy Algorithm
2. Divide and Conquer
3. Dynamic Programming
4. Backtracking