35 docs tagged with "dsa"

Bubble Sort is a simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order. The pass through the list is repeated until the list is sorted. The algorithm, which is a comparison sort, is named for the way smaller elements 'bubble' to the top of the list. Although the algorithm is simple, it is too slow and impractical for most problems even when compared to insertion sort. It can be practical if the input is usually in sort order but may occasionally have some out-of-order elements nearly in position.

Insertion Sort is a simple sorting algorithm that builds the final sorted array one item at a time. It is much less efficient on large lists than more advanced algorithms such as quicksort, heapsort, or merge sort.

Selection Sort is an in-place comparison sorting algorithm that divides the input list into two parts: the sublist of items already sorted and the sublist of items remaining to be sorted. It repeatedly finds the minimum element from the unsorted part and puts it at the beginning of the unsorted part. The algorithm maintains two subarrays in a given array. The subarray which is already sorted and the remaining subarray which is unsorted. In every iteration of selection sort, the minimum element from the unsorted subarray is picked and moved to the sorted subarray.

An array is a collection of items stored at contiguous memory locations. It is a data structure that stores a fixed-size sequential collection of elements of the same type. An array is used to store a collection of data, but it is often more useful to think of an array as a collection of variables of the same type.

Backtracking is a technique used to solve problems by exploring all possible solutions.

In this blog post, we'll dive into the binary search algorithm, a fundamental technique in computer science for efficiently finding an element in a sorted array.

Thsi page containes Bucket Sort, with codes in python, java and c++

Data structures are used to represent data in a meaningful way. They provide a way to store and organize data in memory. This section covers the different types of data structures and their applications.

Data Structures and Algorithms are the building blocks of computer science. They are the tools you'll use to build software systems. This section is a collection of resources to help you understand and master Data Structures and Algorithms.

Understanding the differences between recursion and iteration is crucial for selecting the appropriate approach for solving various problems in data structures and algorithms. This guide highlights the key differences, advantages, and use cases of both recursion and iteration.

Dynamic programming is a technique used in computer programming to solve complex problems by breaking them down into smaller overlapping subproblems.

Heaps are a type of binary tree-based data structure commonly used in computer science. They are often used to implement priority queues, where elements with higher priority are dequeued first. Heaps have two main variations: max heaps, where the parent node is always greater than or equal to its children, and min heaps, where the parent node is always less than or equal to its children. Heaps have efficient insertion and deletion operations, making them suitable for applications that require efficient priority-based processing.

In this blog post, we'll explore the iterative binary search algorithm, a fundamental technique in computer science for efficiently finding an element in a sorted array. You'll learn what iterative binary search is, how it works, and its time complexity. We'll also cover practical applications and common problems you can solve using this algorithm. By the end, you'll have a thorough understanding of iterative binary search and how to implement it in your programming projects.

A collection of easy, medium, and hard LinkedList problems from LeetCode to help you practice and master LinkedList concepts in Data Structures and Algorithms.

A linked list is a linear data structure in which elements are not stored in contiguous memory locations. Instead, each element, called a node, contains a data part and a reference (or link) to the next node in the sequence. Linked lists are used in various applications such as dynamic memory allocation, implementation of data structures like stacks and queues, and more.

Master Theorem for solving divide and conquer recurrences. Explained with examples.

Learn about mathematical algorithms and how they can be applied in data structures and algorithms. Understand the importance of choosing the right algorithm and implementing it correctly for efficient problem-solving.

A matrix is a two-dimensional data structure consisting of rows and columns, where each element is identified by its row and column index. It is commonly used in various fields, including mathematics, computer science, and data analysis, to represent and manipulate structured data.

A matrix is a two-dimensional data structure consisting of rows and columns, where each element is identified by its row and column index. It is commonly used in various fields, including mathematics, computer science, and data analysis, to represent and manipulate structured data.

The Pattern Searching Algorithm is a powerful tool used to find occurrences of a pattern within a larger text or string.

A queue is a linear data structure that follows the First In First Out (FIFO) principle. This means that the first element added to the queue will be the first one to be removed. Queues are used in various applications such as process scheduling, breadth-first search, and more.

This page explains Radix sort, with code implementations and resources for further learning.

Recursion is a programming technique where a function calls itself directly or indirectly to solve a problem. It is used in various applications such as solving mathematical problems, implementing data structures, and more.

A collection of 20 Leetcode questions focused on recursion, categorized into easy, medium, and hard levels.

In this blog post, we'll explore the recursive binary search algorithm, a fundamental technique in computer science for efficiently finding an element in a sorted array. You'll learn what recursive binary search is, how it works, and its time complexity. We'll also cover practical applications and common problems you can solve using this algorithm. By the end, you'll have a thorough understanding of recursive binary search and how to implement it in your programming projects.

Space complexity is a measure of the amount of working storage an algorithm needs. It is a measure of the amount of memory space an algorithm needs to solve a problem as a function of the size of the input to the problem. It is the amount of memory space required by the algorithm to execute in its life cycle.

Stack is a linear data structure that follows LIFO principle

A string is a sequence of characters. It is a data structure that represents a sequence of characters, either as a literal constant or as some kind of variable. In data structures and algorithms, strings are used in a wide range of applications such as text processing, pattern matching, and data serialization.

Time Complexity is a measure of the amount of time an algorithm takes to solve a problem as a function of the size of the input to the problem. It is commonly estimated by counting the number of elementary operations performed by the algorithm, where an elementary operation takes a fixed amount of time to perform.

In this blog post, we'll delve into the world of two-dimensional arrays, a vital data structure in programming. You'll learn what 2D arrays are, how to initialize and traverse them, and their common uses in real-world applications like matrix operations, image processing, and game boards. We'll also tackle classic algorithmic challenges involving 2D arrays, such as rotating a matrix and finding the largest sum subgrid. By the end, you'll have a solid understanding of how to effectively use 2D arrays to solve complex problems in your programming projects.