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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.
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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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How can we solve question which could not be solved by master method
Is there any method or we should leave it
Master theorem is just a shorthand for another, more cumbersome method: approximating manually the result (i.e. thinking of the size of the recursion trees and how much time is spent in each node) and checking the result.
There is also more general Akra-Bazzi method.
Of course, it has strengths, but should not be considered a “magic bullet” too.
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Sorry that this question is subjective, but given that the Stack Overflow has the largest Z3 user base, I want to give it a try.
I have a big constraint satisfaction problem that consists of many integer propositional logic formulas and a few first order logic formulas that only contain integers(quantifiers). I care very much about the efficiency, because I am building an interactive program synthesizer.
I am now using z3 solver and the check time is sometimes too long. I wonder if z3 is the best tool to tackle the problem I mentioned in above or there is a better tool? How about CPLEX?
Any suggestion will be appreciated.
Edit:
Sorry, the code has been remove for privacy reason. I can email you my code personally if you are willing to take a look. Thanks in advance.
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I am currently reading a programming text book and as I discover different algorithms used in the book I'm finding it necessary to understand how they work by working through them. Is there a standard & efficient way to work through simple algorithms on paper?
Write the algorithm down on the paper. Write the corresponding graphs and variables that you use in algorithm.
Now follow algorithm step by step and note what changed with variables and graphs etc.
Time slices. Make a table, where the column headers are variables involved, and row headers are step numbers. Fill in row zero with initial values if any, and each row represents the result of the current step on the previous row.
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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.