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Am I correct in saying the energy in the simulated annealing algorithm is equal to the change in cost?
So I can calculate it with the follow:
energy = cost(prevSolution) - cost(currentSolution);
The term 'energy' can have various definitions, but it usually means the current objective value, or the cost of the current state, that is cost(currentSolution).
The difference you defined, along with some transition function, usually specifies the transition probability from one state to another.
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I need to do a job where I need to place a particular object(Hanger) in a standard distance.
The rules are:
We should try to place each object in a given standard distance from each other.
There is a max distance from one object to adjacent object which in no way should be violated.
From the start and end also similar standard and maximum distance rule applies.
And there are some portions given where the objects placement needs to be avoided.
I'm not even able to start... which algorithm to use.
If anyone has any suggestion how I can achieve this or some related source please let me know.
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Can any algorithm that performs automatic learning be called a "machine learning algorithm"? Or is this designation is reserved to the known ML algorithms like SVM, Feature Selection... ?
Any algorithm that learns to do a task by itself and gets better at it is considered machine learning even if it just as simple as computing the joint probability. Only condition is automated learning, that's all.
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Are there any algorithms for finding the maximum value of a continuous function, which is proofed to be bounded upside?
For example, a function similar to sin.
I think Newton's method and Mid-point method are for finding a fixed value, any other methods for finding maximum value?
For general functions that are "Lipschitz-continuous" (meaning that the output changes by at most a constant factor times the change in input) see e.g. http://link.springer.com/article/10.1007%2FBF00938542#page-1 and http://link.springer.com/article/10.1007%2Fs10898-012-9937-9#page-1 . If your function is arbitrary continuous and not Lipschitz-continuous, then in theory the function could change to an arbitrarily high or low value over an arbitrarily small region, so provable global optimization is very hard.
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I have number x=[0,n], where n>0.
I want to construct a function y=f(x) such that the value increase slowly from 0 and increase very fast when approaching n, and when reach n, y is infinity. What is a good function to model this?
1/(n-x) - 1/n will work.
There are plenty of other functions log, atan, x^(-k),... that goes to infinity at some point.
a^y is another set of functions with fast grows - maybe more suitable for coding as it can reach arbitrary large (but finite) values.
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How can we use Breadth First Search as a strategy for propositional theorem proving (I can't see a clear problem formulation: what are the actions available at each state and what a state is).
I've been looking for explanations everywhere in the net; all documents mention BFS but none of them gives an algorithm.
Thank you for your help
A state is a list of derivations. A transition from state to state applies an inference rule where each premise appears as a conclusion in the list of derivations and extends the list with the new derivation.
With these states and transitions, you can do a usual BFS until you hit the conclusion you're looking for.