Introduction

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You might have an algorithm for getting from office to home, for making a chunk of code that calculates the terms of the Fibonacci sequence, or for finding what you’re looking for in a retail store. Algorithms are the building blocks of computer programs or sequence of unambiguous instructions ( the term 'unambiguous' indicates that there is no room for subjective interpretation) that tells how the problem could be addressed and solved -- which is definitely overblown in their importance like road maps for accomplishing a given, well-defined automated reasoning task -- which always have a clear stopping point.

Long division and column addition are examples that everyone is familiar with -- even a simple function for adding two numbers is implementation of a particular algorithm. Online grammar checking uses algorithms. Financial computations use algorithms. Robotic field uses algorithms for controlling their robot using algorithms. An encryption algorithm transforms data according to specified actions to protect it. A search engine like Google uses search engine algorithms (such as, takes search strings of keywords as input, searches its associated database for relevant web pages, and returns results). In fact, it is difficult to think of a task performed by your computer that does not use computer rules that are a lot like a recipes (called algorithms).

The use of computer algorithms (step-by-step techniques used for Problem-solving) plays an essential role in space search programs. Scientists have to use enormous calculations, and they are managed by high-end supercomputers, which are enriched with detailed sets of instructions that computers follow to arrive at an answer. Algorithms have applications in many different disciplines from science to math to physics and, of course, computing -- and provide us the most ideal option of accomplishing a task. Here is some importance of algorithms in computer programming.

• To improve the effectiveness of a computer program: An algorithm (procedure or formula for solving a problem, based on conducting a sequence of specified actions) can be used to improve the speed at which a program executes a problem and has the potential of reducing the time that a program takes to solve a problem.
• Proper usage of resources: The right selection of an algorithm will ensure that a program consumes the least amount of memory. Apart from memory, the algorithm can determine the amount of processing power that is needed by a program.

The algorithm for a child's morning routine could be the following:

Step 1: Wake up and turn off alarm

Step 2: Get dressed

Step 3: Brush teeth

Step 4: Eat breakfast

Step 5: Go to school

 The algorithm to add two numbers entered by user would look something like this:

Step 1: Start

Step 2: Declare variables num1, num2 and sum

Step 3: Read values num1 and num2

Step 4: Add num1 and num2 and assign the result to sum

 sum ← num1 + num2

 Step 5: Display sum

Step 6: Stop

 Two of these algorithms accomplish exactly the same goal, but each algorithm does it in completely different way to achieve the required output or to accomplish our task. In computer programming, there are often many different ways – algorithms (any well-defined computational procedure that takes some value, or set of values, as input and produces some value, or set of values as output) -- to accomplish any given task. Each algorithm has credits and demerits in different situations. If you have a million integer values between -2147483648 and +2147483647 and you need to sort them, the bin sort is the accurate algorithm to use. If you have a million book titles, the quick sort algorithm might be the best choice. By knowing the toughness and weaknesses of the different algorithms, you pick the best one to accomplish a specific task or to solve a specific problem.

One of the most important aspects of an algorithm is how fast it can manipulate data in various ways, such as inserting a new data item, searching for a particular item or sorting an item. It is often easy to come up with a list of rules to follow in order to solve a problem, but if the algorithm is too slow, it's back to the drawing board. Efficiency of an algorithm depends on its design and implementation. Since every procedure or formula for solving a problem based on conducting a sequence of specified actions -- uses computer resources to run -- execution time and internal memory usage are important considerations to analyze an algorithm.

Why Study Algorithms?

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Algorithms are the heart of computer science (usually means a procedure or basically instance of logic written in software that solves a recurrent problem of finding an item with specific properties among collection of items or transforming data according to specified actions to protect it), and the subject has countless practical applications as well as intellectual depth that is widely used throughout all areas of information technologyincluding solving a mathematical problem (as of finding the greatest common divisor ) in a finite number of steps that often involves repetition of an operation. The word algorithm -- a mathematical concept whose roots date back to 600 AD with invention of the decimal system -- derives from the name of the ninth century Persian mathematician and geographer, Mohammed ibn-Musa al-Khwarizmi, who was part of the royal court in Baghdad and who lived from about 780 to 850. On the other hand, it turns out algorithms (widely recognized as the foundation of modern computer coding) have a long and distinguished history stretching back as far as the Babylonians.

Although there is some available body of facts or information about early multiplication algorithms in Egypt (around 1700-2000 BC) the oldest algorithm is widely recognized to be valid or correct to have been found on a set of Babylonian clay tablets that date to around 1600 - 1800 BC. Their exact significance only came to be revealed or exposed around 1972 when an American computer scientist, mathematician, and professor emeritus at Stanford University Donald E. Knuth published the first English translations of various Babylonian cuneiform mathematical tablets.

Here are some short extracts from his 1972 manuscript that explain these early algorithms:-

"The calculations described in Babylonian tablets are not merely the solutions to specific individual problems; they are actually general procedures for solving a whole class of problems." - Pages 672 to 673 of "Ancient Babylonian Algorithms".

The wedge-shaped marks on clay tablets also seem to have been an early form of instruction manual:-

"Note also the stereotyped ending, 'This is the procedure,' which is commonly found at the end of each section on a table. Thus the Babylonian procedures are genuine algorithms, and we can commend the Babylonians for developing a nice way to explain an algorithm by example as the algorithm itself was being defined...." - Pages 672 to 673 of "Ancient Babylonian Algorithms".

 The use of computers, however, has raised the use of algorithms in daily transactions (like accessing an automated teller machine (ATM ), booking an air or train or buying something online) to unprecedented levels of real-world problems with solutions requiring advanced algorithms abounds. From Google search to morning routines, algorithms are ubiquitous in our everyday life -- and their use is only likely to grow to break down tasks into chunks that can be solved through specific implementations. Many of the problems, though they may not seem realistic, need the set of well-defined algorithmic knowledge that comes up every day in the real world. By developing a good understanding of a series of logical steps in an algorithmic language, you will be able to choose the right one for a problem and apply it properly. Different algorithms play different roles in programming – and algorithms are used by computer programs where a program –

• Get input data.
• Process it using the complex logics.
• Stop when it finds an answer or some conditions are met.
• Produce the desired output.

To give you a better picture, here is the most common type of algorithms:

• Searching Algorithms
• Sorting Algorithms
• Path finding Algorithms
• Tree and graph based algorithms
• Approximate Algorithms
• Compression Algorithms
• Random Algorithms
• Pattern Matching
• Sequence Finding and a lot more

You only need to define your problem then select the right algorithm to use.The word algorithm may not appear closely connected to kids, but the truth is that -- for kids -- understanding the process of building a step by step method of solving a problem helps them build a strong foundation in logical thinking and problem solving. Here are some problems you can ask your kid to discuss algorithmic solutions with you:

• How do we know if a number is odd or even?
• How do we calculate all of the factors of a number?
• How can we tell if a number is prime?
• Given a list of ten numbers in random order, how can we put them order?

 Algorithms has shown it can yield results in all industries — from predicting insurance sales opportunities and generating the millions of search inquiries every day to automating medicine research, optimizing transportation routes, and much more. While algorithms help companies like Master Card and Visa to keep their users' information, such as card number, password, and bank statement safely -- algorithms aren't perfect. They fail and some fail spectacularly. Over the past few years, there have been some serious fails with algorithms, which are the formulas or sets of rules used in digital decision-making processes. Now people are questioning whether we're putting too much trust in the algorithms. When algorithms go bad: Online failures show humans are still needed. Disturbing events at Facebook, Instagram and Amazon reveal the importance of context.

Best Algorithm Books to Read and Distinguish Yourself

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• Introduction to Data Science Algorithms
• High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems
• Algorithms Unlocked
• Machine Learning Models and Algorithms for Big Data Classification
• Data Structures and Algorithm Analysis in Java
• The Algorithmic Beauty of Plants
• Beginning Algorithms
• Algorithms for Bioinformatics: Lecture 1: Primer to algorithms and molecular biology
• Algorithms for Bioinformatics: Lecture 3: Greedy Algorithms and Genomic Rearrangements
• How to Think About Algorithms
• Algorithm Theory – SWAT 2004
• Algorithmic Foundations of Robotics XI
• Algorithmic Topology of 3-Manifolds and Classification
• Algorithms for 5G physical layer
• Algorithmic Foundation of Robotics VII
• Algorithms for Sensor Systems
• Algorithms
• Applied Natural Language Processing with Python
• Approximation Algorithms for Complex Systems
• Geometry Algorithms
• U-Turning Ant Colony Algorithm powered by Great Deluge Algorithm for the solution of TSP Problem
• An Introduction to Algorithmic Trading: Basic to Advanced Strategies
• Data Structures and Algorithms in Python
• Data Structures and Algorithms with JavaScript
• Algorithms For Dummies
• Data Structures And Algorithms Made Easy
• Data Structures and Algorithms with Python
• Disk-Based Algorithms for Big Data
• Dynamic Programming: A Computational Tool
• Grokking Algorithms: An illustrated guide for programmers and other curious people
• Hacker's Delight
• Introduction to Algorithms
• Learning F# Functional Data Structures and Algorithms
• Learning JavaScript Data Structures and Algorithms
• Mathematics and Physics for Programmers
• Python Algorithms: Mastering Basic Algorithms in the Python Language
• Sets, Logic and Maths for Computing
• The Algorithm Design Manual
• The Data Compression Book
• Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies
• Algorithms and Complexity
• Algorithms: a functional programming approach
• Algorithms and Protocols for Wireless Sensor Networks
• Computer Arithmetic Algorithms
• Internet Security: Cryptographic Principles, Algorithms and Protocols
• Introduction to Genetic Algorithms
• Introduction to Parallel Processing: Algorithms and Architectures
• Experimental Algorithmics
• Harmony Search Algorithm
• Machine Learning For Dummies
• C Programming: Data Structures and Algorithms
• Mathematical and Algorithmic Foundations of the Internet
• Practical Algorithms in Pediatric Hematology and Oncology
• Python Deep Learning
• CS1102S: Data Structures and Algorithms
• Statistical Arbitrage: Algorithmic Trading Insights and Techniques
• Stochastic Algorithms for Visual Tracking
• Successful Algorithmic Trading
• Understanding Machine Learning: From Theory to Algorithms
• Competitive Programmer's Handbook
• Planning Algorithms
• Clojure Data Structures and Algorithms Cookbook
• Accuracy and Stability of Numerical Algorithms
• Algorithms for Diophantine Equations
• Algorithms For Interviews
• Algorithms for Next Generation Networks
• Algorithms for Parallel Polygon Rendering
• Algorithms on Strings
• Approximate Iterative Algorithms
• Approximation Algorithms
• Biologically Inspired Algorithms for Financial Modelling
• Boolean Functions: Theory, Algorithms, and Applications
• Cellular Genetic Algorithm
• Encyclopedia of Algorithms
• Monte Carlo: Concepts, Algorithms, and Applications
• Multiobjective Evolutionary Algorithms and Applications
• Algorithm Design
• Analysis of Algorithms: An Active Learning Approach
• A Common-Sense Guide to Data Structures and Algorithms
• Algorithms in a Nutshell
• Algorithms to Live By
• Algorithms Unplugged
• Data Structures and Algorithms Using VISUAL BASIC.NET
• Bioinformatics Algorithms
• FAST TRANSFORMS: Algorithms, Analyses, Applications
• Genetic Algorithms in Electromagnetics
• Handbook of Algorithms for Physical design Automation
• Handbook of Applied Algorithms
• Inductive Learning Algorithms for Complex Systems Modeling
• Algorithms and Theory of Computation Handbook
• Matrix Algorithms
• Pattern Matching Algorithms
• Petascale Computing: Algorithms and Applications
• PLANAR GRAPHS: Theory and Algorithms
• Search Algorithms and Applications
• Statistical Mechanics: Algorithms and Computations
• Thinking Recursively
• Essential Algorithms: A Practical Approach to Computer Algorithms
• Everyday Data Structures
• Fundamentals of Computer Algorithms
• Computer Algorithms
• PHP 7 Data Structures and Algorithms
• Image Fusion: Algorithms and Applications
• Triangulations: Structures for Algorithms and Applications
• Energy-Aware System Design: Algorithms and Architectures
• Evolutionary Robotics: From Algorithms to Implementations
• Handbook of Memetic Algorithms
• Handbook of Sensor Networks: Algorithms and Architectures
• Optimization Algorithms on Matrix Manifolds
• Problems on Algorithms
• Rigid Body Dynamics Algorithms
• Sensors: Theory, Algorithms, and Applications
• Algorithms for Collective Communication
• Graphs, Networks and Algorithms
• JavaScript Data Structures and Algorithms
• Computer Animation: Algorithms and Techniques
• Algorithms of Informatics
• Classification Algorithms for Codes and Designs
• Computational Geometry
• Fun with Algorithms
• Genetic Algorithms for Applied CAD Problems
• Gravitational N-Body Simulations: Tools and Algorithms
• Handbook of Chemoinformatics Algorithms
• Integral Closure: Rees Algebras, Multiplicities, Algorithms
• Learning Kernel Classifiers: Theory and Algorithms
• Logic, Automata, and Algorithms
• Matters Computational: Ideas, Algorithms, Source Code
• Pattern Discovery in Bioinformatics: Theory and Algorithms
• Permutation Group Algorithms
• Sampling Algorithms
• Scheduling Algorithms
• Shadow Algorithms Data Miner
• Binary Quadratic Forms: An Algorithmic Approach
• Fundamental Algorithms
• Data Mining Algorithms in C++
• Computer Vision: Algorithms and Applications
• Elementary Functions: Algorithms and Implementation
• Fast Algorithms for Signal Processing
• Information Retrieval Architecture and Algorithms
• Lossy Image Compression: Domain Decomposition-Based Algorithms
• Practical Genetic Algorithms
• Recent Advances in Algorithms and Combinatorics
• Software Tools and Algorithms for Biological Systems
• Super-Recursive Algorithms
• Synthesis and optimization of DSP algorithms
• Text Algorithms
• The Design of Approximation Algorithms
• Visibility Algorithms in the Plane
• Parallel Graph Algorithms
• Algorithms: Greedy Algorithms
• Introduction to the Design and Analysis of Algorithms
• Introduction to Algorithms
• Molecular Bioinformatics: Algorithms and Applications
• Theory and Algorithms for Linear Optimization
• Wavelets: Algorithms And Applications
• Finite Fields: Theory, Applications and Algorithms
• Online Algorithms
• Algorithms Notes for Professionals
• Annotated Algorithms in Python: with Applications in Physics, Biology, and Finance
• Distributed Computing: Principles, Algorithms, and Systems
• Think Data Structures: Algorithms and Information Retrieval in Java
• Numerical Algorithms
• From Algorithms to Z-Scores: Probabilistic and Statistical Modeling in Computer Science
• Algorithms for Reinforcement Learning
• Bandit Algorithms
• Algorithms and Data Structures in VLSI Design
• MULTIAGENT SYSTEMS: Algorithmic, Game-Theoretic, and Logical Foundations
• Art Gallery Theorems and Algorithms
• Quantum Algorithms
• Algorithms for Concurrent Systems
• Ensemble Methods: Foundations and Algorithms
• Data Mining and Analysis: Fundamental Concepts and Algorithms
• Algorithms and Data Structures for External Memory
• Mastering Algorithms with C
• Spectral Algorithms
• The Yacas Book of Algorithms
• Solutions for Introduction to algorithms
• Algorithms For Clustering Data
• Python and Algorithms
• Clever Algorithms: Nature-Inspired Programming Recipes
• Global Optimization Algorithms: Theory and Application
• Universal lossless data compression algorithms
• Parameterized Algorithms
• Sams Teach Yourself Data Structures and Algorithms in 24 Hours
• Information Theory, Inference, and Learning Algorithms
• Instructor's Manual: Introduction to Algorithms
• Principles of Robot Motion: Theory, Algorithms, and Implementation
• Top 10 algorithms in data mining
• Algorithms, Flowcharts, Data Types, and Pseudo-codes
• Auction Algorithms
• The CYK Algorithm
• Types of Machine Learning Algorithms
• Types of Algorithms
• Algorithms and Collusion: Competition Policy in the Digital Age
• Complexity of Algorithms
• The Relevance of Algorithms
• Algorithm Specification
• Some introductory notes on Design and Analysis of Algorithms
• Principles of Algorithmic Problem Solving
• Problem Solving with Algorithms and Data Structures
• Knowing algorithms
• Tunability: Importance of Hyperparameters of Machine Learning Algorithms
• Trust But Verify: A Guide to Algorithms and the Law
• Algorithm and Flow Chart
• The Learning Behind Gmail Priority Inbox
• Invitation to Computer Science
• Algorithms in Everyday Mathematics
• Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms
• The Running Time of Programs
• Parallel Programming and Parallel Algorithms
• Logic and Theory of Algorithms
• Study of Page Rank Algorithms
• What is an Evolutionary Algorithm?
• Web Crawling Algorithms
• Algorithmic Puzzles
• The Social Power of Algorithms
• Introduction to Algorithms: A Creative Approach
• Google's Deep-Web Crawl
• Algorithmic Game Theory
• Computer Age Statistical Inference: Algorithms, Evidence, and Data Science
• Computational Geometry: Algorithms and Applications
• Automate This: How Algorithms Came to Rule Our World
• Notes on PageRank Algorithm
• Algorithmic Harms beyond Facebook and Google: Emergent Challenges of Computational Agency
• Data Structures and Algorithms Tutorial
• Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization
• Understanding Google Algorithms And Seo Is Essential For Online Marketer
• First Course in Algorithms through Puzzles
• Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data
• The Alphabet and the Algorithm
• The Jacobi-Perron Algorithm Its Theory and Application
• Computer Science Distilled: Learn the Art of Solving Computational Problems
• Algorithm Collections for Digital Signal Processing Applications Using Matlab
• Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms
• The LLL Algorithm: Survey and Applications
• System Theory, the Schur Algorithm and Multidimensional Analysis
• A branch and cut algorithm for nonconvex quadratically constrained quadratic programming
• A Programmer's Companion to Algorithm Analysis
• The EM Algorithm and Related Statistical Models
• Pearls of Functional Algorithm Design
• Structure and Interpretation of Computer Programs
• Mathematics for Algorithm and System Analysis
• A Common-Sense Guide to Data Structures and Algorithms
• Concrete Mathematics
• Ideals, Varieties, and Algorithms
• Machine Learning Refined
• Decision Making under Uncertainty
• Principles of Data Mining
• The Algorithmic Foundations of Differential Privacy
• The Nature of Code
• Reinforcement Learning: An Introduction
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction
• The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
• Hello World!: Computer Programming for Kids and Other Beginners
• Vector Models for Data-Parallel Computing
• Analysis for Computer Scientists: Foundations, Methods, and Algorithms
• The Everyday Life of an Algorithm
• Introduction to Design and Analysis of Algorithms - In Simple Way
• Knapsack Problems
• AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java
• Models of Computation: Exploring the Power of Computing
• Open Data Structures (in C++)
• Open Data Structures (in Java)
• Open Data Structures (in pseudocode)
• Think Complexity
• Advanced Data Structures
• Streaming Data Mining
• How to Think Like a Computer Scientist