Algorithms in C

                                                                            Algorithms in C Language

What is an Algorithm?

• It is a step-by-step approach to solving a particular problem.

• It gives a crisp solution.

• It can be understood by non-technical people as well.

• There are possibilities that a problem can have one or more algorithmic approaches for its solution.

• Algorithms are free of programming language constructs.

Algorithmic Analysis

• Analysis helps in identifying the optimal Algorithm.

• Analysis aids in understanding the trade-off of time and space complexity.

Types of Analysis

There are different types of analysis:

• Best case

• Average case

• Worst case

Qualities of Good Algorithms

• There should be precisely defined input and output.

• Algorithms must be in an effective way where they can solve problems compared to many other solutions.

• Each step in an Algorithm should be unambiguous.

• An algorithm shouldn’t be written in code but should be written in a way where you can attempt to write it in any code.

Algorithm Complexity

• Space Complexity : The amount of memory space required by the life cycle of an algorithm represents the space complexity. It is an extra memory that an algorithm requires for its implementation. Space complexity S(P) of any algorithm P is S(P) = C +S(I), where C is the fixed one or a constant and S(I) varies based on the instance characteristic I.

For Example,

Algorithm: adding two numbers SUM(a,b)

Step 1: Start

Step 2: c ← a+b+10

Step 3: Stop

Here, you have one constant and three variables. Thus, the space complexity will be S(P) = 1+3.

Space Complexity has low priority criteria in the selection of optimal algorithms.

• Time complexity : Time Complexity determines the total number of unit operations undertaken to solve a particular problem. Unit operation is an operation that is independent and can’t be broken down into simpler operations. Thus, the time complexity is the time required by the algorithm to run to its completion. Numerically time requirements can be denoted as T(n). T(n) represents the number of steps in an algorithm where each step has taken constant time. Time Complexity has the higher priority criteria in the section of optimal Algorithm.

Recursion

• Recursion is about function calling itself. It comes in handy when the function is breakable into sub-problems. Many data structures follow these recursive methods.

• While using the recursive function, you should make sure that you define an appropriate exit condition from the function; otherwise, it may slip into an infinite loop.

• Recursive functions are advantageous in solving many mathematical problems such as finding the factorial of a number, generating Fibonacci series, etc.

Steps of Recursion

• Base Condition

• Logic

• Recursive Call

Tail Recursion

Tail recursion makes use of recursive functions as the last statement of its function. No task left after the recursive call makes it easier for the compiler to optimize the code. No activation record maintenance is required here. This is the faster approach to Recursion. 

Non-Tail Recursion

Here the last statement of the function is not a recursive function. It is a slower recursive approach as compared to tail recursion. Non-Tail Recursion needs to maintain the activation record.

Direct Recursion

A function calling itself is called Direct Recursion. This represents a one-step recursive call which is called inside its own function body. Only one function is involved here.

Indirect Recursion

It is a mutual recursion where function f1 calls f2 and f2 calls f1, representing an Indirect Recursion. These functions make use of two-step recursive calls. More than one function is involved in Indirect Recursion.

Binary Search Algorithm

Binary Search is one of the searching techniques. It can be used on a sorted array. This searching technique adapts the divide and conquers strategy, and search space is always reduced to half in every iteration. This is a very efficient technique for searching, but it needs some order on which partitions of the array will occur. 

• Binary Search - Iterative Algorithm

binarySearch(arr, size)

  loop until beg is not equal to end

  midIndex = (beg + end)/2

  if (item == arr[midIndex] )

  return midIndex

  else if (item > arr[midIndex] )

 beg = midIndex + 1

  else

  end = midIndex - 1

• Binary Search - Recursive Algorithm

binarySearch(arr, item, beg, end)

  if beg<=end

  midIndex = (beg + end) / 2

  if item == arr[midIndex]

  return midIndex

  else if item < arr[midIndex]

  return binarySearch(arr, item, midIndex + 1, end)

  else

  return binarySearch(arr, item, beg, midIndex - 1)

  return -1

Some of the Sorting Algorithms are:

• Selection Sort Algorithm

• Insertion Sort Algorithm

• Quick Sort Algorithm

• Merge Sort Algorithm

Why take Algorithms in C Course?

Optimal Algorithms are the vital goal of all the developers in the industry. Because C language is more appropriate for Beginners in algorithms to learn and understand, this Free course provides you the same opportunity.

This Algorithms in C course contains 2.5 hours of video content. You will briefly get introduced to what algorithms are, their analysis, and the various types of complexities such as time and space. Recursion concepts are talked about in-depth. 

Algorithms in the C course also walk you through various searching and sorting algorithms such as binary search, quick sort, merge sort, and much more are discussed with examples to understand it better. You can also attend the Quiz provided at the end of the course.

Get hold of this Free Algorithms in C course offered by Great Learning which strengthens your basic concepts on algorithms, and you can also receive the course completion certificate at the end. Enroll in this Course Now!