Questions tagged [simulated-annealing]
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questions
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Does Basin Hopping Optimize On Noisy Functions?
Basin Hopping (thence Simulated Annealing) looks for gradients of the unknown objective function to locate its minima through a noisy process of visiting various points in the function's domain. ...
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17 views
How random search converges to find optimum of a function? [duplicate]
Good morning,
Suppose that we have a set: $ L = \{ x_{1} ,..., x_{N} \} $ and a given function $ f(x) $ we need to find it's minimum $ x^{*} $. The set $ L $ is belonging to an interval $ [a , b ] $ ...
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1answer
51 views
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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24 views
Temperature in Softmax and simulated annealing in Metropolis-Hastings?
We can add a temperature to the Softmax to make the Softmax softer or harder by setting it higher or lower(refer to this answer). And in reinforcement learning, a high temperature will lead to the ...
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35 views
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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1answer
45 views
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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0answers
30 views
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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1answer
450 views
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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1answer
84 views
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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2answers
61 views
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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1answer
350 views
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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1answer
164 views
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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1answer
64 views
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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votes
1answer
1k views
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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0answers
48 views
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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0answers
281 views
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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62 views
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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0answers
335 views
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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0answers
918 views
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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0answers
283 views
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 ...
