Programming Archives - Page 6 of 9

Dynamic Programming approach explained with simple example

Dynamic Programming is a programming technique which is used for solving complex problems by breaking it into comparatively simpler subproblems and finding the optimal solutions of the complex problems by finding the optimal solutions of these subproblems.

Even though, the name “Dynamic Programming” might scare people but actually its kind of simple if we follow some basic techniques to approach any complex problem.

Steps to tackle a problem using Dynamic Programming approach:

1) Define smaller problems from the original complex problems.

2) Solve these smaller problems using recursion.

3) Use smaller problems results to solve the bigger complex problem.

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Heap sort explained with simple example

by Sandeep Kumar

Heap sort is one of the fastest sorting algorithm, which works on Divide and Conquer algorithm. In this sorting algorithm, all the data elements are inserted into a heap (max or min) and then removes item from the root of the heap will give the sorted(descending or ascending) data list.

Pros:
1) Faster algorithm and best performance O(nlogn).
2) Simple implementation.
3) No extra space is needed.
4) Good for cases to find biggest/smallest or top k biggest/smallest element finding kind of scenarios.

Cons:
1) Not a stable sort means position of relative identical data items is not guaranteed.

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Quick sort explained with simple example

by Sandeep Kumar

Quick sort is one of the fastest sorting algorithm, which works on Divide and Conquer algorithm. In this sorting algorithm, an element is picked as a pivot element and list is divided into sub-lists around the pivot element. These divided sub-lists finally sorted into which results into sorted initial list.

Pros:
1) Faster algorithm and best performance O(nlogn) is for worst distribution case scenario.
2) Simple implementation due to recursive nature.
3) No extra space is needed.

Cons:
1) Worst case scenario performance is O(n*n).
2) Not good for Linked list kind of sorting data where memory is not contiguous.

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Merge sort explained with simple example

by admin

Merge sort is one of the simplest sorting algorithm, which works on Divide and Conquer algorithm. In this sorting algorithm, input data is divided into two halves/sublist which then acts as input data and furthur divided into two halves so that finally every sublist contains a single element. Finally, these sublists are merged in sorted order which results into sorted data.

Pros:
1) Simple algorithm and hence efficient for small list of data.
2) Good for sorting data represented in Linked list.
3) This algorithm is stable (performance is equal in all cases) and insensitive to the distribution of initial data.

Cons:
1) Extra space needed in O(n).

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Selection sort explained with simple example

by admin

Selection sort is one of the simplest sorting algorithm, which traverse through the list, picks the minimum item from the unsorted list and place it in its place in the final sorted list.
This sorting algorithm contains (n – 1) traversal.

Pros:
1) Simple algorithm and hence efficient for small list of data.
2) No extra memory needed.
3) Good for cases when list is almost sorted (only few misplaced items).
4) Very less number of swap operation(O(n)), as in every pass only 1 swap of data is needed at max.

Cons:
1) Very slow algorithm. Not good for very large set of data.

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Insertion sort explained with simple example

by admin

Insertion sort is one of simplest sorting algorithm which traverse through the list, picking one item at a time and place it in its place in the final sorted list. This sorting algorithm contains (n – 1) traversal.

Pros:
1) Simple algorithm and hence efficient for small list of data.
2) No extra memory needed.
3) Good for cases when list is almost sorted (only few misplaced items).

Cons:
1) Very slow algorithm. Not good for very large set of data.

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Bubble sort explained with simple example

by admin

Bubble sort is one of the simplest sorting algorithm which traverse through the list, compares items at adjacent places, and swaps them to make list sorted. After every pass of list one or more item is placed at its correct position. If in a pass, no swaps are performed then it means list becomes sorted.

Pros:
1) Simple algorithm.
2) No extra memory needed.
3) Good for cases when list is almost sorted (only few misplaced items).

Cons:
1) Very slow algorithm. Not good for very large set of data.

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Sorting algorithms explained in simple manner

by admin

Sorting is an operation in computer science via which data is being arranged in an ordered sequence. There are lots of efficient algorithms are already present to perform sorting operation which we are going to discuss here.

Some of the most used sorting algorithms are as follows:

1) Bubble Sort
2) Insertion Sort
3) Merge Sort
4) Selection Sort
5) Quick Sort
6) Heap Sort

Comparison of various Sorting algorithms in terms of time complexity for best and worst case:

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Call by value and Call by reference explained with simple example

by admin

This is one of very basic concept of C/C++ programming language which is kind of building block of any program or software. Often unknowingly programmers make mistakes in these concept and introduce bugs in softwares 🙂

Basically in C, there are two ways to pass data to a function:

1) Call by value: In this case only value will be passed to the function and the function will store this value in its own local variable. This function will work on this copied data (not original) and set some new value based on some operations. But since this function is working on copied data and not on original data, hence it can’t update the original data in this method.

2) Call by reference: In this case address of the data is passed to the function and function will define a pointer to access this memory location. Since, this function has direct access to the original data memory location, hence it can update the value of original data. Thus, this method is called Call/Pass by reference as we are passing reference of the memory instead of direct data.

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Binary tree explained with simple example

by admin

A Binary tree is a tree in which any node can have at max 2 nodes which are known as left subtree and right subtree. If each node of a binary tree contains Zero or Two nodes then this binary tree is known as proper binary tree else it will be called improper binary tree.

Typically a binary tree can be shown as follows:

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