Questions tagged [simulated-annealing]
The simulated-annealing tag has no usage guidance.
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Simulated Annealing of estimators given PDF
I have the PDF of a Gumbel Distribution which goes like:
$$ f(x) = \frac{e^{-e^{\frac{x-a}{b}} + \frac{x-a}{b}}}{b}; b > 0 $$
The parameters of this distribution are a and b.
I wish to estimate ...
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Constant Mode Equation for a Weibull Distribution
I am trying to build a movement class for a simulated annealing algorithm for predicting an optimal spare parts policy. For better or worse I am looking to the Weibull distribution to move about the ...
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Sampling from distribution parametrized by $ \log(1-p) $ given $ \log (p) $
The context for the problem is that I'm working with a modified genetic algorithm where the fitness score of each chromosome is given by a log-probability $ \log(p) \in (-\infty, 0) $. Thus, I have a ...
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Variable Selection of Linear Regression via Simulated Annealing
I am dealing a problem currently about the simulated annealing.
The problem is:
$$Y = β_1X_1 + β_2X_2+ \dots + β_{1000}X_{1000} +
\epsilon,\ \epsilon ∼ \mathcal{N}(0, σ_2) $$ We take the Residual ...
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What is the difference between simulated annealing and deterministic annealing?
Not sure if this is the right place, but I was wondering if someone could briefly explain to me the differences & similarities between simulated annealing and deterministic annealing?
I know that ...
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GenSA simulated annealing solver of R gives EXACTLY the same result regardless of settings
I've been using R's "GenSA" package for simulated annealing to solve a very complicated high dimensional optimization problem (63 unknowns). I found that every time I run GenSA I get exactly the same ...
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Why are neural networks better at avoiding local minima?
In simulated annealing, from my understanding, it is a process where it stochastically searches the whole landscape at the beginning for the global minima and then hones down on the best solution it ...
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How to simulate spatial point patterns that have spatial structure similar to that of given spatial point pattern?
I have some spatial point pattern X distributed in polygon wind and I wonder how can I simulate different point patterns that by their spatial properties (for example, number of points, spatial ...
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Simulated annealing acceptance probability puzzle
My understanding of simulated annealing (SA) is that at any iteration $t$, a new sample $Y_t$ is generated, which, if the objective function $E$ is improved, i.e., $E(Y_t)<E(X_{t-1})$, then $Y_t$ ...
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What is the relationship between Metropolis Hastings and Simulated Annealing?
Context and Problem
In the Wikipedia page for Simulated Annealing they state
The simulation can be performed either by a solution of kinetic equations for density functions[2][3] or by using the ...
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What is the role of simulated annealing in Gibbs sampling?
While I was reading about Gibbs sampling, I happened to see "simulated annealing" but what is it doing in Gibbs sampling? Although I don't understand the full context of simulated annealing, I am ...
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Does the following can be considered a Metropolis Method? [closed]
Suppose, from the current state C it is possible to move to D different neighbouring states. In simulated annealing, we select a neighbouring state $D_i$ randomly and then accept it with probability
$...
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Simulated Annealing vs. Basin-hopping algorithm
I was planning to use Simulated Annealing algorithm (scipy.optimize implementation) to optimise my black-box objective function, but the documentation mentions that the method is
Deprecated in ...
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Multiple Importance Sampling and Metropolis-Hastings on extended state space
Let
$(E,\mathcal E,\lambda),(E',\mathcal E',\lambda')$ be measure spaces
$k\in\mathbb N$
$p,q_1,\ldots,q_k:E\to(0,\infty)$ be probability densities on $(E,\mathcal E,\lambda)$
$w_1,\ldots,w_k:E\to[0,...
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Training Neural Network with Simulated Annealing
I am trying to train a simple neural network with simulated annealing. I have programmed a neural network with an input layer of 784 input nodes (28 x 28 pixels, I am using the MNIST database to train)...
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antagonistic simulated annealing
Simulated annealing aims at a series of target distributions
$$\pi_T(x)\propto\exp\{T\,H(x)\}$$
to find the maximum of the function $H$ and its argument
$$\arg_x\max_{x\in \mathfrak X} H(x)$$
if the ...
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Simulated Annealing vs SGD with (warm) Restarts
What's the difference between simulated annealing and stochastic gradient descent with restarts? They both seem like they are occasionally going backwards at a decreasing rate. Also what is the ...
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Simulated Annealing Parameter Tuning
My question concerns parameter tuning for simulated annealing (SA). I've the following toy equation
$$
y = (x^2+x) \times cos(2x) + 20 \text{ if } x \in (-10, 10)
$$
My problem is that the solution ...
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How can I find the bounds that gets Simulated Annealing to converge?
According to Wikipedia on Simulated Annealing,
For any given finite problem, the probability that the simulated annealing algorithm terminates with a global optimal solution approaches 1 as the ...
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