Linear Search for design and analysis of algorithm
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2. It also explains bubble sort, which bubbles larger values up and smaller values down through multiple passes. Radix sort sorts elements based on individual digits or characters.
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Searching Algorithms:
Linear Search: A basic algorithm that sequentially checks each element in a data structure until a match is found.
Binary Search: A more efficient algorithm for sorted arrays or lists that repeatedly divides the search interval in half.
Hashing: Using a hash function to map keys to specific locations for quick retrieval (e.g., in hash tables).
Data Structures for Searching:
Arrays: Basic data structure often used in linear searches.
Lists: Linked lists or dynamic arrays can be used for searching.
Trees:
Binary Search Tree (BST): A binary tree where the left subtree contains values less than the root, and the right subtree contains values greater than the root.
Balanced Trees: Trees like AVL and Red-Black trees maintain balance for efficient searching.
Heaps: Used for priority queues and can support efficient operations for finding the minimum or maximum.
Hash Tables: Utilizes hash functions for quick lookups.
Search Variations:
Searching for Minimum/Maximum: Algorithms designed to find the minimum or maximum element in a data structure efficiently.
Substring Search: Searching for a specific substring within a larger text or string.
Pattern Matching: Searching for a specific pattern or sequence of elements within a data structure.
Advanced Search Techniques:
Trie: A tree-like data structure used for efficient string searching and storage.
Bloom Filter: A probabilistic data structure for quickly checking whether an element is a member of a set.
K-Dimensional Trees: Used for multidimensional data, like spatial searching in geographic information systems.
Optimizations and Indexing:
Indexing: Techniques to create indexes or data structures that accelerate searching in large datasets.
Skip Lists: A data structure that uses multiple levels of linked lists to speed up searches.
Parallel and Distributed Searching:
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Distributed Search: Strategies for searching in distributed systems or databases.
Search Complexity and Analysis:
Time Complexity: Analyzing the efficiency of search algorithms in terms of the number of operations required.
Space Complexity: Analyzing the memory usage of search data structures.
Searching in Specialized Applications:
Graph Search: Techniques for traversing and searching in graphs.
Geospatial Search: Searching .
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1) Linear search sequentially checks each element of a list one-by-one until the target element is found. It is simple but inefficient, especially for large lists.
2) Binary search works on a sorted list by checking the middle element first. It then eliminates half of the remaining list and searches the other half recursively until the target is found. It is more efficient than linear search for large sorted lists.
3) Pseudocode algorithms are provided for both linear search and binary search to illustrate the search process step-by-step.
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1) Linear search sequentially checks each element of a list one-by-one until the target element is found. It is simple but inefficient, especially for large lists.
2) Binary search works on a sorted list by checking the middle element first. It then eliminates half of the remaining list and searches the other half recursively until the target is found. It is more efficient than linear search for large sorted lists.
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Transform tomorrow: Master benefits analysis with Gen AI today webinar, 30 A...
Transform tomorrow: Master benefits analysis with Gen AI today webinar, 30 A...Association for Project Management Transform tomorrow: Master benefits analysis with Gen AI today webinar
Wednesday 30 April 2025
Joint webinar from APM AI and Data Analytics Interest Network and APM Benefits and Value Interest Network
Presenter:
Rami Deen
Content description:
We stepped into the future of benefits modelling and benefits analysis with this webinar on Generative AI (Gen AI), presented on Wednesday 30 April. Designed for all roles responsible in value creation be they benefits managers, business analysts and transformation consultants. This session revealed how Gen AI can revolutionise the way you identify, quantify, model, and realised benefits from investments.
We started by discussing the key challenges in benefits analysis, such as inaccurate identification, ineffective quantification, poor modelling, and difficulties in realisation. Learnt how Gen AI can help mitigate these challenges, ensuring more robust and effective benefits analysis.
We explored current applications and future possibilities, providing attendees with practical insights and actionable recommendations from industry experts.
This webinar provided valuable insights and practical knowledge on leveraging Gen AI to enhance benefits analysis and modelling, staying ahead in the rapidly evolving field of business transformation.
Transform tomorrow: Master benefits analysis with Gen AI today webinar, 30 A...
Transform tomorrow: Master benefits analysis with Gen AI today webinar, 30 A...Association for Project Management
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Linear Search for design and analysis of algorithm
- 2. What Is Searching? • Searching is the process of fetching a specific element in a collection of elements. • The collection can be an array or a linked list. If you find the element in the list, the process is considered successful, and it returns the location of that element. • And in contrast, if you do not find the element, it deems the search unsuccessful.
- 3. What Is a Linear Search Algorithm? • Linear search, often known as sequential search, is the most basic search technique. • In this type of search, you go through the entire list and try to fetch a match for a single element. If you find a match, then the address of the matching target element is returned. • On the other hand, if the element is not found, then it returns a NULL value.
- 4. Example • Following is a step-by-step approach employed to perform Linear Search Algorithm.
- 5. • The procedures for implementing linear search are as follows: • Step 1: First, read the search element (Target element) in the array. • Step 2: In the second step compare the search element with the first element in the array. • Step 3: If both are matched, display "Target element is found" and terminate the Linear Search function.
- 6. • Step 4: If both are not matched, compare the search element with the next element in the array. • Step 5: In this step, repeat steps 3 and 4 until the search (Target) element is compared with the last element of the array. • Step 6 - If the last element in the list does not match, the Linear Search Function will be terminated, and the message "Element is not found" will be displayed. • So far, you have explored the fundamental definition and the working terminology of the Linear Search Algorithm.
- 12. Linear Search Terminates • A perfect match is found, you stop comparing any further elements and terminate the Linear Search Algorithm and display the element found at location 4.