This document provides an overview of linear search and binary search algorithms.
It explains that linear search sequentially searches through an array one element at a time to find a target value. It is simple to implement but has poor efficiency as the time scales linearly with the size of the input.
Binary search is more efficient by cutting the search space in half at each step. It works on a sorted array by comparing the target to the middle element and determining which half to search next. The time complexity of binary search is logarithmic rather than linear.
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
linear search and binary search, Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
a. Concept and Definition✓
b. Inserting and Deleting nodes ✓
c. Linked implementation of a stack (PUSH/POP) ✓
d. Linked implementation of a queue (Insert/Remove) ✓
e. Circular List
• Stack as a circular list (PUSH/POP) ✓
• Queue as a circular list (Insert/Remove) ✓
f. Doubly Linked List (Insert/Remove) ✓
For more course related material:
https://github.com/ashim888/dataStructureAndAlgorithm/
Personal blog
www.ashimlamichhane.com.np
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
Binary Search is a searching algorithm used in a sorted array by repeatedly dividing the search interval in half. The idea of binary search is to use the information that the array is sorted and reduce the time complexity to O(Log n).
a. Concept and Definition✓
b. Inserting and Deleting nodes ✓
c. Linked implementation of a stack (PUSH/POP) ✓
d. Linked implementation of a queue (Insert/Remove) ✓
e. Circular List
• Stack as a circular list (PUSH/POP) ✓
• Queue as a circular list (Insert/Remove) ✓
f. Doubly Linked List (Insert/Remove) ✓
For more course related material:
https://github.com/ashim888/dataStructureAndAlgorithm/
Personal blog
www.ashimlamichhane.com.np
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
Binary Search is a searching algorithm used in a sorted array by repeatedly dividing the search interval in half. The idea of binary search is to use the information that the array is sorted and reduce the time complexity to O(Log n).
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2. Linear Search
The linear search is a sequential search, which uses a loop to
step through an array, starting with the first element.
It compares each element with the value being searched
for, and stops when either the value is found or the end of
the array is encountered.
If the value being searched is not in the array, the algorithm
will unsuccessfully search to the end of the array.
3. Linear Search
Since the array elements are stored in linear order
searching the element in the linear order make it easy and
efficient.
The search may be successful or unsuccessfully. That is, if
the required element is found them the search is successful
other wise it is unsuccessfully.
4. Advantages
The linear search is simple - It is very easy to understand
and implement
It does not require the data in the array to be stored in any
particular order
5. Disadvantages
It has very poor efficiency because it takes lots of
comparisons to find a particular record in big files
The performance of the algorithm scales linearly with the
size of the input
Linear search is slower then other searching algorithms
12. Analysis of Linear Search
How long will our search take?
In the best case, the target value is in the first element of the
array.
So the search takes some tiny, and constant, amount of time.
In the worst case, the target value is in the last element of the
array.
So the search takes an amount of time proportional to the
length of the array.
13. Analysis of Linear Search
In the average case, the target value is somewhere in the array.
In fact, since the target value can be anywhere in the array, any
element of the array is equally likely.
So on average, the target value will be in the middle of the array.
So the search takes an amount of time proportional to half the
length of the array
14. Binary Search
The general term for a smart search through sorted data is a binary
search.
1. The initial search region is the whole array.
2. Look at the data value in the middle of the search region.
3. If you’ve found your target, stop.
4. If your target is less than the middle data value, the new search
region is the lower half of the data.
5. If your target is greater than the middle data value, the new
search region is the higher half of the data.
6. Continue from Step 2.
18. 18
Time Complexity of Binary Search
How fast is binary search?
Think about how it operates: after you examine a value, you cut the
search region in half.
So, the first iteration of the loop, your search region is the whole
array.
The second iteration, it’s half the array.
The third iteration, it’s a quarter of the array.
...
The kth iteration, it’s (1/2k-1) of the array.
19. 19
Need to Sort Array
Binary search only works if the array is already sorted.
It turns out that sorting is a huge issue in computing – and the
subject of our next lecture.