And algorithms geeksforgeeks

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algorithms: a comprehensive guide for geeks

algorithms are the bedrock of computer science. they are precise, step-by-step instructions for solving a specific problem. this tutorial aims to provide a comprehensive understanding of algorithms, focusing on key concepts, common techniques, and examples to get you started. we'll explore topics relevant to the geeksforgeeks audience, covering fundamental algorithms and data structures.

**table of contents**

1. **what is an algorithm?**
2. **characteristics of a good algorithm**
3. **algorithm design techniques**
4. **data structures and their impact on algorithms**
5. **common algorithms & code examples:**
* 5.1. sorting algorithms (bubble sort, insertion sort, selection sort, merge sort, quick sort)
* 5.2. searching algorithms (linear search, binary search)
* 5.3. graph algorithms (bfs, dfs)
* 5.4. dynamic programming (fibonacci sequence, knapsack problem)
6. **algorithm analysis: time and space complexity**
7. **algorithm examples and implementation (python)**
8. **optimization techniques**
9. **resources for further learning**

**1. what is an algorithm?**

an algorithm is a well-defined sequence of steps to solve a computational problem. it takes input, processes it, and produces output. think of it as a recipe: you have ingredients (input), instructions (algorithm), and a finished dish (output).

**formally:**

* **input:** data provided to the algorithm.
* **steps:** a finite, ordered sequence of unambiguous instructions.
* **output:** the result produced by the algorithm.
* **finiteness:** an algorithm must terminate after a finite number of steps.
* **definiteness:** each step must be precisely defined, with no ambiguity.
* **effectiveness:** each step must be basic enough to be carried out, in principle, by a person using only pencil and paper.

**example:**

let's say we want to find the largest number in a list. a simple algorithm could be:

1. assume the ...

#Algorithms #GeeksforGeeks #numpy
And algorithms
GeeksforGeeks
data structures
algorithm analysis
coding problems
competitive programming
sorting algorithms
searching algorithms
dynamic programming
graph algorithms
recursion techniques
time complexity
space complexity
interview preparation
programming tutorials

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