Basic Algorithms in Programming, Study notes of Computer Science

An introduction to basic algorithms in programming. Algorithms are step-by-step procedures or sets of rules for solving specific problems or performing specific tasks. The document covers searching and sorting algorithms, which are essential for solving a wide range of problems efficiently. It explains the concepts of linear search, binary search, bubble sort, insertion sort, selection sort, merge sort, and quick sort. useful for computer science students who want to learn the basics of algorithms.

Typology: Study notes

2022/2023

Available from 10/15/2023

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Basic Algorithms
Certainly! Here are notes on basic algorithms in programming:
Basic Algorithms
Algorithms are step-by-step procedures or sets of rules for solving specific
problems or performing specific tasks. They are the building blocks of
computer programs and are essential for solving a wide range of problems
efficiently. Here are some basic algorithms and algorithmic concepts:
1. Searching Algorithms
Searching algorithms are used to find the presence or location of a specific
element in a data structure, such as an array or list.
a. Linear Search
- Linear search is a simple algorithm that checks each element in a collection
one by one until a match is found or the end of the collection is reached.
- It is suitable for unordered lists.
b. Binary Search
- Binary search is a more efficient algorithm for searching in sorted collections.
- It repeatedly divides the search range in half until the target element is found
or the search range becomes empty.
2. Sorting Algorithms
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Basic Algorithms

Certainly! Here are notes on basic algorithms in programming: Basic Algorithms Algorithms are step-by-step procedures or sets of rules for solving specific problems or performing specific tasks. They are the building blocks of computer programs and are essential for solving a wide range of problems efficiently. Here are some basic algorithms and algorithmic concepts:

  1. Searching Algorithms Searching algorithms are used to find the presence or location of a specific element in a data structure, such as an array or list. a. Linear Search
  • Linear search is a simple algorithm that checks each element in a collection one by one until a match is found or the end of the collection is reached.
  • It is suitable for unordered lists. b. Binary Search
  • Binary search is a more efficient algorithm for searching in sorted collections.
  • It repeatedly divides the search range in half until the target element is found or the search range becomes empty.
  1. Sorting Algorithms

Sorting algorithms arrange elements in a specific order, such as ascending or descending. Sorting is a common operation in computer science and data processing. a. Bubble Sort

  • Bubble sort repeatedly compares adjacent elements and swaps them if they are in the wrong order.
  • It continues until no more swaps are needed. b. Insertion Sort
  • Insertion sort builds the sorted array one element at a time by taking elements from the unsorted portion and inserting them into the correct position in the sorted portion. c. Selection Sort
  • Selection sort repeatedly selects the minimum element from the unsorted portion and places it at the beginning of the sorted portion. d. Merge Sort
  • Merge sort is a divide-and-conquer algorithm that divides the array into smaller subarrays, sorts them, and then merges them back together. e. Quick Sort
  1. Dynamic Programming
  • Dynamic programming is a technique used to solve problems by breaking them down into smaller overlapping subproblems and storing the results of these subproblems to avoid redundant computations.
  • It is commonly used in optimization problems and is more efficient than naive recursive approaches.
  1. Graph Algorithms
  • Graph algorithms are used to solve problems related to graphs, which consist of nodes (vertices) and edges (connections between nodes).
  • Common graph algorithms include breadth-first search (BFS) and depth-first search (DFS).
  1. Common Data Structures Understanding data structures like arrays, lists, stacks, queues, trees, and hashmaps is crucial for implementing and optimizing algorithms.
  2. Algorithm Design Strategies
  • When faced with a problem, it's important to choose the right algorithmic approach:
  • Divide and Conquer : Break the problem into smaller subproblems and solve them recursively.
  • Dynamic Programming : Solve a problem by solving related subproblems only once and storing their solutions.
  • Greedy Algorithm : Make locally optimal choices at each step to achieve a global optimum.
  • Brute Force : Try all possible solutions and select the best one. These basic algorithms and algorithmic concepts are fundamental building blocks in computer science and are used to solve a wide range of real-world problems efficiently. Understanding them is essential for anyone pursuing a career in programming or computer science.