In computer science, algorithmic efficiency is a property of an algorithm which relates to the amount of computational resources used by the algorithm. An algorithm must be analyzed to determine its resource usage, and the efficiency of an algorithm can be measured based on the usage of different resources. Algorithmic efficiency can be thought of as analogous to engineering productivity for a repeating or continuous process. For maximum efficiency it is desirable to minimize resource usage. However, different resources such as time and space complexity cannot be compared directly, so which of two algorithms is considered to be more efficient often depends on which measure of efficiency is considered most important. For example, bubble sort and timsort are both algorithms to sort a list of items from smallest to largest. Bubble sort sorts the list in time proportional to the number of elements squared , but only requires a small amount of extra memory which is constant with respect to the length of the list. Timsort sorts the list in time linearithmic (proportional to a quantity times its logarithm) in the list's length, but has a space requirement linear in the length of the list. If large lists must be sorted at high speed for a given application, timsort is a better choice; however, if minimizing the memory footprint of the sorting is more important, bubble sort is a better choice. (Wikipedia).
Order/Efficiency/Run-time of an algorithm (Decision Maths 1)
Powered by https://www.numerise.com/ How to calculate the order/efficiency/run-time of an algorithm and why these are important. www.hegartymaths.com http://www.hegartymaths.com/
From playlist Decision Maths 1 OCR Exam Board (A-Level tutorials)
What Is An Algorithm ? | Introduction to Algorithms | How To Write An Algorithm? | Simplilearn
This video is based on What Is An Algorithm ? The Introduction to Algorithms tutorial will explain to you How To Write An Algorithm? and it will cover the following topics ✅00:00- Introduction to Algorithms ✅01:46- What Is an Algorithm? The algorithm is a step-by-step procedure or set o
From playlist C++ Tutorial Videos
Searching and Sorting Algorithms (part 4 of 4)
Introductory coverage of basic searching and sorting algorithms, as well as a rudimentary overview of Big-O algorithm analysis. Part of a larger series teaching programming at http://codeschool.org
From playlist Searching and Sorting Algorithms
Introduction to Algorithms - What are they and how are they useful?
#3B1B #SoMe2 This is my submission for this year's SoME, SoME2!! I hope you enjoy, and please feel free to leave any comments. Any feedback is hugely appreciated~! ーーーーーーーーーーーーーーーーーーーーーーー Time Stamps: 00:00 Intro 00:37 Introduction to Algorithms 03:47 Exploring Algorithms - Binary Searc
From playlist Summer of Math Exposition 2 videos
We can describe the efficiency of an algorithm, program, or a programmatic operation, in terms of the time it takes, the amount of memory it uses, or the amount of secondary storage space it needs to do its work. However, these performance measures depend on a number of factors, not least
From playlist Big O Complexity
Algorithms Explained: Computational Complexity
An overview of computational complexity including the basics of big O notation and common time complexities with examples of each. Understanding computational complexity is vital to understanding algorithms and why certain constructions or implementations are better than others. Even if y
From playlist Algorithms Explained
Fast By Default: Algorithmic Performance Optimization in Practice
We’ve learned to rely on sophisticated frameworks and fast engines so much that we’re slowly forgetting how computers work. With modern development tools, it’s easy to locate the exact code that’s slowing down your application, but what do you do next? Why exactly is it slow, and how do yo
From playlist Performance and Testing
Algorithms Explained: What is an Algorithm?
This video defines what an algorithm is, distinguishes algorithms from recipes and functions and gives some examples of algorithms. This is the first video in an "Algorithms Explained" series that discusses algorithms at a conceptual level. Videos in this series that discuss specific algo
From playlist Algorithms Explained
Exponential Running Time - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
The P versus NP problem - Efficient computation and the limits of human knowledge - AVI Wigderson
Speaker : Avi Wigderson ( IAS, Princeton ) Date and Time : 27 Dec 2009, 05:30 PM Venue : J. N. Tata Auditorium, IISc, Bangalore The P vs. NP problem is a central outstanding problem of computer science and mathematics. In this talk I will attempt to describe its technical, scientific and
From playlist Public Lectures
The Complexity of the Non-commutative Determinant - Srikanth Srinivasan
The Complexity of the Non-commutative Determinant Srikanth Srinivasan Institute for Advanced Study October 11, 2010 I will talk about the computational complexity of computing the noncommutative determinant. In contrast to the case of commutative algebras, we know of (virtually) no efficie
From playlist Mathematics
Machine learning techniques in quantum information (...) - A. Rocchetto - Workshop 1 - CEB T2 2018
Andrea Rocchetto (University of Oxford and UCL) / 15.05.2018 Machine learning techniques in quantum information theory: a selection of results During this talk I will present a selection of results at the intersection of quantum information, quantum computation, and machine learning. Fir
From playlist 2018 - T2 - Measurement and Control of Quantum Systems: Theory and Experiments
Andrew Childs - Efficient quantum algorithm for dissipative nonlinear differential equations
Recorded 24 January 2022. Andrew Childs of the University of Maryland presents "Efficient quantum algorithm for dissipative nonlinear differential equations" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: While there has been extensive previous work on efficient quantum alg
From playlist Quantum Numerical Linear Algebra - Jan. 24 - 27, 2022
P vs NP and mathematics - Avi Wigderson [ICM 2006]
slides for this talk: https://drive.google.com/open?id=1zf378AB1S-MBUmeh5D0ST2_qIKDT_qm4 ICM Madrid Videos 23.08.2006 P-NP and mathematics: a computational complexity perspective Avi Wigderson Institute for Advanced Study, Princeton, USA https://www.mathunion.org/icm/icm-videos/icm-200
From playlist Number Theory
Lecture 3 - Computer Science for Biologists
This is Lecture 3 of the CSE549 (Computational Biology) course taught by Professor Steven Skiena [http://www.cs.sunysb.edu/~skiena/] at Stony Brook University in 2010. The lecture slides are available at: http://www.algorithm.cs.sunysb.edu/computationalbiology/pdf/lecture3.pdf More infor
From playlist CSE549 - Computational Biology - 2010 SBU
MIT 6.006 Introduction to Algorithms, Spring 2020 Instructor: Jason Ku View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This lecture reviews the main concepts and learning goals for the course
From playlist MIT 6.006 Introduction to Algorithms, Spring 2020
Oracle Separation of Quantum Polynomial time and the Polynomial Hierarchy - Avishay Tal
Computer Science/Discrete Mathematics Seminar I Topic: Oracle Separation of Quantum Polynomial time and the Polynomial Hierarchy Speaker: Avishay Tal Affiliation: University of California, Berkeley Date: Oct 1, 2018 For more video please visit http://video.ias.edu
From playlist Mathematics
Shuangping Li (Princeton) -- Binary perceptron
It was recently shown that almost all solutions in the symmetric binary perceptron are isolated, even at low constraint density. In apparent contrast to that, some algorithms have been shown empirically to succeed in finding solutions at low density. This phenomenon has been explained nume
From playlist Northeastern Probability Seminar 2021
Compare Algorithm Complexity Given The Execution Time as a Function
This video explains how to use a limit at infinity to compare the complexity (growth rate) of two functions. http://mathispower4u.com
From playlist Additional Topics: Generating Functions and Intro to Number Theory (Discrete Math)