Optimization algorithms and methods | Metaheuristics | Monte Carlo methods
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. It is often used when the search space is discrete (for example the traveling salesman problem, the boolean satisfiability problem, protein structure prediction, and job-shop scheduling). For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to exact algorithms such as gradient descent or branch and bound. The name of the algorithm comes from annealing in metallurgy, a technique involving heating and controlled cooling of a material to alter its physical properties. Both are attributes of the material that depend on their thermodynamic free energy. Heating and cooling the material affects both the temperature and the thermodynamic free energy or Gibbs energy.Simulated annealing can be used for very hard computational optimization problems where exact algorithms fail; even though it usually achieves an approximate solution to the global minimum, it could be enough for many practical problems. The problems solved by SA are currently formulated by an objective function of many variables, subject to several constraints. In practice, the constraint can be penalized as part of the objective function. Similar techniques have been independently introduced on several occasions, including Pincus (1970), Khachaturyan et al (1979, 1981), Kirkpatrick, Gelatt and Vecchi (1983), and Cerny (1985). In 1983, this approach was used by Kirkpatrick, Gelatt Jr., Vecchi, for a solution of the traveling salesman problem. They also proposed its current name, simulated annealing. This notion of slow cooling implemented in the simulated annealing algorithm is interpreted as a slow decrease in the probability of accepting worse solutions as the solution space is explored. Accepting worse solutions allows for a more extensive search for the global optimal solution. In general, simulated annealing algorithms work as follows. The temperature progressively decreases from an initial positive value to zero. At each time step, the algorithm randomly selects a solution close to the current one, measures its quality, and moves to it according to the temperature-dependent probabilities of selecting better or worse solutions, which during the search respectively remain at 1 (or positive) and decrease toward zero. The simulation can be performed either by a solution of kinetic equations for density functions or by using the stochastic sampling method. The method is an adaptation of the Metropolis–Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, published by N. Metropolis et al. in 1953. (Wikipedia).
How to Model Custom Physical Components in Simscape
Simscape™ extends the MATLAB® language with constructs for modeling implicit equations. Learn more about Simscape: http://goo.gl/Jhsth7 Get a free Product Trial: https://goo.gl/5NvCdU Download Sample Lift Table Model: http://goo.gl/k4fYwA These extensions of MATLAB are used to model a tra
From playlist Physical Modeling
Simulating in Real Time: Hydraulic Actuator
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Configure multiple, independent solvers to enable real-time simulation. The model of a hydraulic aileron actuation system is simulated on a real-time target. For more video
From playlist Physical Modeling
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Model an aileron by assembling parts and joints. Actuate with a Simulink signal and view results on a Simulink scope. For more videos, visit http://www.mathworks.com/produ
From playlist Physical Modeling
Accelerated motion and oscillation!
In this video i demonstrate accelerated motion with interface. I show the graphs of simple accelerating motion and simple harmonic motion with force and motion sensor!
From playlist MECHANICS
A solar system, a simulation made with Excel
An Excel simulation of the solar system. You can see how things are recursively computed: the mutual gravity force from the locations, the accelerations, the velocities, and finally the updated locations. The solar eclipse is also shown. This is clip is intended to illustrate Chapter 24 Ap
From playlist Physics simulations
Data Modeling Tutorial | Data Modeling for Data Warehousing | Data Warehousing Tutorial | Edureka
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, th
From playlist Data Warehousing Tutorial Videos
Sharing Models Using Simscape Editing Mode
A model developer uses Simscape™ and Simscape add-on products to develop a model of a hydraulic lift. Learn more about Simscape: http://goo.gl/Jhsth7 Get a free Product Trial: https://goo.gl/5NvCdU A model user, who does not have licenses for the Simscape add-on products, is able to use t
From playlist Physical Modeling
AQC2016 - Classical Modeling of Quantum Tunneling
A Google TechTalk, June 29, 2016, presented by Itay Hen (USC) ABSTRACT: Tunneling is widely believed to be the main advantage quantum annealers have over their classical counterparts. With neither provable speedups nor no-go theorems demonstrated, the true power of quantum annealers remai
From playlist Adiabatic Quantum Computing Conference 2016
AQC - 2016 Quantum vs. Classical Optimization - A Status Report on the Arms Race
A Google TechTalk, June 27, 2016, presented by Helmut Katzgraber (Texas A&M) ABSTRACT: To date, a conclusive detection of quantum speedup remains elusive. However, recent results from quantum Monte Carlo simulations, as well as the D-Wave 2X quantum annealer show a scaling that clearly o
From playlist Adiabatic Quantum Computing Conference 2016
RubyConf 2022: Simulated Annealing: The Most Metal Algorithm Ever 🤘 by Chris Bloom
Simulated annealing is a fascinating algorithm that's designed to help find a particular type of solution (near-optimal, aka "good enough") to a particular type of problem (constrained optimization). It's inspired by the science of metallurgy, and because it's grounded in a real-world proc
From playlist RubyConf 2022: Mini and Houston
AQC 2016 - Simulated Quantum Annealing Can Be Exponentially Faster Than Classical
A Google TechTalk, June 27, 2016, presented by Elizabeth Crosson (Caltech) ABSTRACT: Simulated Quantum Annealing Can Be Exponentially Faster Than Classical Simulated Annealing: Cost functions with thin, high energy barriers can exhibit exponential separations between the run-time of class
From playlist Adiabatic Quantum Computing Conference 2016
AQC 2016 - What is the Computational Value of Finite Range Tunneling?
A Google TechTalk, June 27, 2016, presented by Vasil Denchev (Google) ABSTRACT: Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling can provide considerable computational advantage. For
From playlist Adiabatic Quantum Computing Conference 2016
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist 3D Printing
AQC 2016 - Floquet Quantum Annealing with Superconducting Circuit
A Google TechTalk, June 29, 2016, presented by Oleksandr Kyriienko (Niels Bohr Institute) ABSTRACT: The successful application of a quantum annealing procedure largely relies on the possibility to implement a non-trivial Hamiltonian in a fully controlled system. The circuit QED platform ha
From playlist Adiabatic Quantum Computing Conference 2016
AQC 2016 - Simulated Annealing Comparison Between All-to-All Connectivity Schemes
A Google TechTalk, June 29, 2016, presented by Tameem Albash (USC) ABSTRACT: Quantum annealing aims to exploit quantum mechanics to speed up the solution to optimization problems. Most problems exhibit complete connectivity between the logical spin variables after they are mapped to the I
From playlist Adiabatic Quantum Computing Conference 2016
Double Dwell Reciprocating 3D Model
Based on a video from https://www.youtube.com/user/thang010146. This user has hundreds of amazing videos with mechanisms. This one can be seen here: https://www.youtube.com/watch?v=8h9mjKA5SjQ. Free 3D model at https://skfb.ly/onUTn.
From playlist Mechanisms
Annealing glasses by cyclic shear deformation by Srikanth Sastry
DISCUSSION MEETING: 7TH INDIAN STATISTICAL PHYSICS COMMUNITY MEETING ORGANIZERS : Ranjini Bandyopadhyay, Abhishek Dhar, Kavita Jain, Rahul Pandit, Sanjib Sabhapandit, Samriddhi Sankar Ray and Prerna Sharma DATE: 19 February 2020 to 21 February 2020 VENUE: Ramanujan Lecture Hall, ICTS Ba
From playlist 7th Indian Statistical Physics Community Meeting 2020
Masayuki Ohzeki: "Quantum annealing and machine learning - new directions of quantum"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Quantum annealing and machine learning - new directions of quantum" Masayuki Ohzeki - Tohoku University Abstract: Quantum annealing is a generic solver of combinator
From playlist Machine Learning for Physics and the Physics of Learning 2019
This video is used for Hologram technology, just make the hologram device at home with a very simple way, I'll put a video of how to make the Hologram device. Enjoy!
From playlist OPTICS
Stanford Seminar - How to Compute with Schrödinger's Cat: An Introduction to Quantum Computing
"How to Compute with Schrödinger's Cat: An Introduction to Quantum Computing" - Eleanor Rieffel of NASA Ames Research & Wolfgang Polak, Independent Consultant About the talk: The success of the abstract model of classical computation in terms of bits, logical operations, algorithms, and
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