Approximation algorithms | Computational complexity theory
In computer science and operations research, approximation algorithms are efficient algorithms that find approximate solutions to optimization problems (in particular NP-hard problems) with provable guarantees on the distance of the returned solution to the optimal one. Approximation algorithms naturally arise in the field of theoretical computer science as a consequence of the widely believed P ≠ NP conjecture. Under this conjecture, a wide class of optimization problems cannot be solved exactly in polynomial time. The field of approximation algorithms, therefore, tries to understand how closely it is possible to approximate optimal solutions to such problems in polynomial time. In an overwhelming majority of the cases, the guarantee of such algorithms is a multiplicative one expressed as an approximation ratio or approximation factor i.e., the optimal solution is always guaranteed to be within a (predetermined) multiplicative factor of the returned solution. However, there are also many approximation algorithms that provide an additive guarantee on the quality of the returned solution. A notable example of an approximation algorithm that provides both is the classic approximation algorithm of Lenstra, Shmoys and Tardos for scheduling on unrelated parallel machines. The design and analysis of approximation algorithms crucially involves a mathematical proof certifying the quality of the returned solutions in the worst case. This distinguishes them from heuristics such as annealing or genetic algorithms, which find reasonably good solutions on some inputs, but provide no clear indication at the outset on when they may succeed or fail. There is widespread interest in theoretical computer science to better understand the limits to which we can approximate certain famous optimization problems. For example, one of the long-standing open questions in computer science is to determine whether there is an algorithm that outperforms the 1.5 approximation algorithm of Christofides to the metric traveling salesman problem. The desire to understand hard optimization problems from the perspective of approximability is motivated by the discovery of surprising mathematical connections and broadly applicable techniques to design algorithms for hard optimization problems. One well-known example of the former is the Goemans–Williamson algorithm for maximum cut, which solves a graph theoretic problem using high dimensional geometry. (Wikipedia).
Minimax Approximation and the Exchange Algorithm
In this video we'll discuss minimax approximation. This is a method of approximating functions by minimisation of the infinity (uniform) norm. The exchange algorithm is an iterative method of finding the approximation which minimises the infinity norm. FAQ : How do you make these animatio
From playlist Approximation Theory
Polynomial approximations -- Calculus II
This lecture is on Calculus II. It follows Part II of the book Calculus Illustrated by Peter Saveliev. The text of the book can be found at http://calculus123.com.
From playlist Calculus II
Approximating Functions in a Metric Space
Approximations are common in many areas of mathematics from Taylor series to machine learning. In this video, we will define what is meant by a best approximation and prove that a best approximation exists in a metric space. Chapters 0:00 - Examples of Approximation 0:46 - Best Aproximati
From playlist Approximation Theory
Approximation & Estimation | Numbers | Maths | FuseSchool
An approximation is anything that is similar, but not exactly the same as something else. For example, if you were to say a 57 minute journey would take “about an hour”, you would be approximating. A value can be approximated by rounding, usually to a value that it is easier to work with
From playlist MATHS: Numbers
Polynomial approximation of functions (part 1)
Using a polynomial to approximate a function at f(0). More free lessons at: http://www.khanacademy.org/video?v=sy132cgqaiU
From playlist Calculus
Error bounds for Taylor approximations -- Calculus II
This lecture is on Calculus II. It follows Part II of the book Calculus Illustrated by Peter Saveliev. The text of the book can be found at http://calculus123.com.
From playlist Calculus II
Linear Approximations and Differentials
Linear Approximation In this video, I explain the concept of a linear approximation, which is just a way of approximating a function of several variables by its tangent planes, and I illustrate this by approximating complicated numbers f without using a calculator. Enjoy! Subscribe to my
From playlist Partial Derivatives
Using Taylor Polynomials to Approximate Functions
This video shows how to determine a Taylor Polynomial to approximate a function. http://mathispower4u.yolasite.com/
From playlist Infinite Sequences and Series
Accelerating MCMC for Computationally Intensive Models by Natesh Pillai
Program Advances in Applied Probability II (ONLINE) ORGANIZERS: Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE: 04 January 2021 to 08 Januar
From playlist Advances in Applied Probability II (Online)
Anthony Nouy: Adaptive low-rank approximations for stochastic and parametric equations [...]
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Numerical Analysis and Scientific Computing
On the (Computational) Approximability of Quadratic Maximization over Convex... - Vijay Bhattiprolu
Short Talks by Postdoctoral Members Topic: On the (Computational) Approximability of Quadratic Maximization over Convex Sets Speaker: Vijay Bhattiprolu Affiliation: Member, School of Mathematics Date: September 22, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Frédéric Vivien : Algorithmes d’approximation - Partie 2
Résumé : Dans la deuxième partie de ce cours nous considérerons un problème lié, celui des algorithmes compétitifs. Dans le cadre de l'algorithmique « en-ligne », les caractéristiques d'une instance d'un problème ne sont découvertes qu'au fur et à mesure du traitement de l'instance (comme
From playlist Mathematical Aspects of Computer Science
Richard Lassaigne: Introduction à la théorie de la complexité
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Mathematical Aspects of Computer Science
Anthony Nouy: Approximation and learning with tree tensor networks - Lecture 2
Recorded during the meeting "Data Assimilation and Model Reduction in High Dimensional Problems" the July 21, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Luca Récanzone A kinetic description of a plasma in external and self-consistent fiel
From playlist Numerical Analysis and Scientific Computing
Solving Laplacian Systems of Directed Graphs - John Peebles
Computer Science/Discrete Mathematics Seminar II Topic: Solving Laplacian Systems of Directed Graphs Speaker: John Peebles Affiliation: Member, School of Mathematics Date: March 02, 2021 For more video please visit http://video.ias.edu
From playlist Mathematics
A Framework for Quadratic Form Maximization over Convex Sets -Vijay Bhattiprolu
Computer Science/Discrete Mathematics Seminar II Topic: A Framework for Quadratic Form Maximization over Convex Sets Speaker: Vijay Bhattiprolu Affiliation: Member, School of Mathematics Date: April 28, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Lower bounds on the size of semidefinite programming relaxations - Steurer
https://www.math.ias.edu/seminars/abstract?event=83574
From playlist Computer Science/Discrete Mathematics
Polynomial approximation of functions (part 2)
Approximating a function with a polynomial by making the derivatives equal at f(0) (Maclauren Series) More free lessons at: http://www.khanacademy.org/video?v=3JG3qn7-Sac
From playlist Calculus
Multilevel weighted least squares polynomial approximation – Sören Wolfers, KAUST
Many problems in science and engineering involve an underlying unknown complex process that depends on a large number of parameters. The goal in many applications is to reconstruct, or learn, the unknown process given some direct or indirect observations. Mathematically, such a problem can
From playlist Approximating high dimensional functions