We have expert tutors to help the students to solve Data structure Assignment. Data structure is one of the basic building block of Computer programming. So, our tutors also provide online tutorial to students who wants coder for coming future. We provide service at affordable price. We never compromise for future of students. We always give our effort to give better services to students.

It is important to learn all concept of Data structure if any programmer want to build a scalable application. Its doesn’t matter which program you used for developed.

Algorithm is set of well-structured instruction which is used to solve the problem. It is created independents of underlying language. Algorithm is used to manipulate data in different ways such as sorting data items, searching and inserting data items.

**Finiteness:**An Algorithm must be terminate after infinite number of steps with finite amount of time.**Unambiguous:**Each steps of Algorithm are clear, unambiguous and precisely define.**Input:**An algorithm must have finite number of input or must have zero input.**Output:**Algorithms must have at least one desired output or may be more defined output.**Effectiveness:**The algorithms must be effective. Each step must be executed at finite time.

**Step 1 : ** Start

**Step 2 : ** Declare variable as number1, number2 and sum

**Step 3 : ** Read value number1 and number2

**Step 4 : ** Add number1 and number2 and assign value to sum
number1+number2→ sum

**Step 5 : ** Display sum

**Step 6 : ** Stop

**Priori Analysis:**Priori analysis is theoretical analysis whose efficiency is counted by various method.**Posterior Analysis:**This type of analysis totally depends on system. SO can only implementation this analysis after running in the system.

**Search:**This Algorithm is used to search items in the Data table.**Delete:**It is used to Delete items from Data structure.**Update:**It is used to update items in Data structure.**Sort:**It is used to Sort items in order.**Insert:**It is used insert item in Data structure.

The amount of time taken by algorithms to execute the complex is known as Time Complexity. The time complexity is defined by Numeral function T(n). Where T(n) is number of steps for constant time interval.

Space complexity of Algorithm defined for memory space in its life cycle. The space complexity of any algorithm P is S(P) = C + SP(I). Where as S(I) is variable part and C is the fixed parts of Algorithm.

**Sequential search:**The best example of the Sequential search is Linear search. The array is traversed sequentially and each element is checked**Interval Search:**This type of searching algorithms is applied only for sorted data. Interval search is more efficient searching algorithm than Linear search. Every time it targets the middle of search structure and divide the search space in half. Example of interval search is Binary search.

**Liner search:**Linear search is type of sequential search. This is very simple search algorithm. In this type of search, data is search on all items one by one. If the match is found it return the particular item otherwise search will in progress till end of the Data collection.

**Pseudo Code:**LinearSearch(a,n,key) Begin for j = 0 to n-1 by 1 do if a[j] == key then return J; endif endfor return -1; End

**Binary Search:**Binary Search algorithm is one of the popular search algorithms with execution complexity is O (log n). This search only works for sorted set of elements. Starting with the interval cover all array or list. If the value of the search key is small than items in half of interval, lower half narrow interval is applied. This algorithm work on the principle Divide and conquer approach in which the list is divided into two equal half and then item is compared with the middle elements of the list. After applying this if the match is found then location of the middle items can throw back, otherwise we search on half depending on the output came by the match in first step.

**Pseudo Code:**begin compStr(String1 , String2) i = 0; While (String1 [j] == String2 [i] && String1 [j] != '\0') j++; end While If (String1 [j] > String2[j]) return -1; end If return (String1 [j] < String2[j]); end compStr begin Binary_Search (listOfProduct, toSearch, start , end) If start > end retunn -1 end If middle = start + end / 2 if listOfProduct[mid] is Empty left = mid - 1; right = mid + 1; while (true): If left < start && right > end return -1; End If If right<=end && !listOfProduct[right] isEmpty mid = right; break; End If if left>=start && !listOfProduct[left].isEmpty mid = left; break; End If right++; left--; End While end If if compStr (str, listOfProduct [mid]) == 0 return mid; end If if (compStr (str, listOfProduct [mid]) < 0) return Binary_Search (arr, str, mid+1, end); end If //search is small than mid return Binary_Search (listOfProduct, start, str, mid-1); end Binary Search

- Worst case → O(log n)
- Average case → O(log n)
- Best case → O(1)

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