• Study Resource
  • Explore
    • Arts & Humanities
    • Business
    • Engineering & Technology
    • Foreign Language
    • History
    • Math
    • Science
    • Social Science

    Top subcategories

    • Advanced Math
    • Algebra
    • Basic Math
    • Calculus
    • Geometry
    • Linear Algebra
    • Pre-Algebra
    • Pre-Calculus
    • Statistics And Probability
    • Trigonometry
    • other →

    Top subcategories

    • Astronomy
    • Astrophysics
    • Biology
    • Chemistry
    • Earth Science
    • Environmental Science
    • Health Science
    • Physics
    • other →

    Top subcategories

    • Anthropology
    • Law
    • Political Science
    • Psychology
    • Sociology
    • other →

    Top subcategories

    • Accounting
    • Economics
    • Finance
    • Management
    • other →

    Top subcategories

    • Aerospace Engineering
    • Bioengineering
    • Chemical Engineering
    • Civil Engineering
    • Computer Science
    • Electrical Engineering
    • Industrial Engineering
    • Mechanical Engineering
    • Web Design
    • other →

    Top subcategories

    • Architecture
    • Communications
    • English
    • Gender Studies
    • Music
    • Performing Arts
    • Philosophy
    • Religious Studies
    • Writing
    • other →

    Top subcategories

    • Ancient History
    • European History
    • US History
    • World History
    • other →

    Top subcategories

    • Croatian
    • Czech
    • Finnish
    • Greek
    • Hindi
    • Japanese
    • Korean
    • Persian
    • Swedish
    • Turkish
    • other →
 
Profile Documents Logout
Upload
(The hint on the previous problem tells us exactly what needs to be
(The hint on the previous problem tells us exactly what needs to be

solutions - UA Center for Academic Success
solutions - UA Center for Academic Success

Preconditioning of Markov Chain Monte Carlo Simulations Using
Preconditioning of Markov Chain Monte Carlo Simulations Using

Antiderivatives
Antiderivatives

An Improved BKW Algorithm for LWE with Applications to
An Improved BKW Algorithm for LWE with Applications to

Prime Numbers and the Riemann Hypothesis
Prime Numbers and the Riemann Hypothesis

Evolutionary programming made faster
Evolutionary programming made faster

Automatic Planning – Chapter 7: Heuristic Search
Automatic Planning – Chapter 7: Heuristic Search

... node ← a node n with n.state=problem.InitialState frontier ← a priority queue ordered by ascending h [g + h], only element n loop do if Empty?(frontier) then return failure n ← Pop(frontier) if problem.GoalTest(n.State) then return Solution(n) for each action a in problem.Actions(n.State) do n0 ← Ch ...
Automatic Planning – Chapter 7: Heuristic Search
Automatic Planning – Chapter 7: Heuristic Search

... admissible heuristics for Π. We say that h1 , . . . , hn are additive if h1 + · · · + hn is admissible, i.e., for all states s in Π we have h1 (s) + · · · + hn (s) ≤ h∗ (s). → An ensemble of heuristics is additive if its sum is admissible. Remarks: Example: h1 considers only tiles 1 . . . 7, and h2 ...
Limitations of Front-to-End Bidirectional Heuristic Search
Limitations of Front-to-End Bidirectional Heuristic Search

... If a unidirectional heuristic search expands the majority of its nodes deeper than the solution midpoint, we call its heuristic weak and show that a bidirectional heuristic search expands no fewer nodes than a bidirectional bruteforce search. If the majority are expanded at shallower depth than the ...
Limitations of Front-To-End Bidirectional Heuristic Search
Limitations of Front-To-End Bidirectional Heuristic Search

High order schemes based on operator splitting and - HAL
High order schemes based on operator splitting and - HAL

mixture densities, maximum likelihood, EM algorithm
mixture densities, maximum likelihood, EM algorithm

Phase Diagram for the Constrained Integer Partitioning Problem.
Phase Diagram for the Constrained Integer Partitioning Problem.

The complexity of Minesweeper and strategies for game playing
The complexity of Minesweeper and strategies for game playing

Line search methods, Wolfe`s conditions
Line search methods, Wolfe`s conditions

... 1.2. Backtracking. The sufficient decrease condition alone is not enough to guarantee that the algorithm makes a reasonable progress along the given search direction. However, if the line search algorithm chooses the step length by backtracking, the curvature condition can be dispensed. This means t ...
Analysis and Numerics of the Chemical Master Equation
Analysis and Numerics of the Chemical Master Equation

The XStar N-body Solver Theory of Operation By Wayne Schlitt
The XStar N-body Solver Theory of Operation By Wayne Schlitt

26 Optimal Bounds for Johnson-Lindenstrauss
26 Optimal Bounds for Johnson-Lindenstrauss

Numerical Methods for the solution of Hyperbolic
Numerical Methods for the solution of Hyperbolic

Availability-aware Mapping of Service Function Chains
Availability-aware Mapping of Service Function Chains

Introduction to Initial Value Problems
Introduction to Initial Value Problems

Stochastic Search and Surveillance Strategies for
Stochastic Search and Surveillance Strategies for

SOME DISCRETE EXTREME PROBLEMS
SOME DISCRETE EXTREME PROBLEMS

Computing the Greatest Common Divisor of - CECM
Computing the Greatest Common Divisor of - CECM

1 2 3 4 5 ... 22 >

Simulated annealing



Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more efficient than exhaustive enumeration — provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution.The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. Both are attributes of the material that depend on its thermodynamic free energy. Heating and cooling the material affects both the temperature and the thermodynamic free energy. While the same amount of cooling brings the same amount of decrease in temperature it will bring a bigger or smaller decrease in the thermodynamic free energy depending on the rate that it occurs, with a slower rate producing a bigger decrease.This notion of slow cooling is implemented in the Simulated Annealing algorithm as a slow decrease in the probability of accepting worse solutions as it explores the solution space. Accepting worse solutions is a fundamental property of metaheuristics because it allows for a more extensive search for the optimal solution.The method was independently described by Scott Kirkpatrick, C. Daniel Gelatt and Mario P. Vecchi in 1983, and by Vlado Černý in 1985. The method is an adaptation of the Metropolis–Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, invented by M.N. Rosenbluth and published in a paper by N. Metropolis et al. in 1953.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report