Actually, we can prove mathematically that such a program cannot exist!
The halting problem is a decision problem in computability theory. Basically, you want a program that can read other programs and determine whether they contain an infinite loop. If the halting machine finishes in a finite amount of time, the output comes as ‘yes’, otherwise as ‘no’.
What is the joke here? Reduction = Proof by Contradiction and Construction Assume MBis a TM that decides LB. It's now six years later, and you are a well-established software developer. Apparently, these wet-behind-the-ears newbies tend to accidently write a lot of programs with infinite loops (you've been there, right?). Maybe it is just me, but I interpreted this to be the "DoesItHalt" function actually *running* "program", then when "program" completes (halts) it returns true. Professor Thorsten Altenkirch has a way of using Python to demonstrate the issue. We have reached a contradiction, so (as long as nothing else is questionable) our assumption must be wrong.
You decide on the following specification:Inputs: A Python program file. If we had Turing machine A then we could build B. Essentially, this is a test to see if the program read from the input terminates when given itself as input. Programs with infinite loops can be difficult to debug and challenging to grade. Of course, while a total solution to the generalized halting problem is impossible, lesser solutions can work pretty well in concrete cases. Put more specifically, does goofy.py halt when given itself as its input?Let's think it through.
It only makes a decision. When you first see a problem like this, it's hard not to think, 'this is just a silly gimmick, a word game.' Do a construction using MBto build MA, a TM that decides LA. The sentence in bold is false It should be noted that Randall's solution, barring its unsoundness, solves more than the halting problem in the form it is usually stated. After all, compilers and interpreters are common examples of programs that analyze other programs. Also, even if the claim would be true in "practical" sense, it would not solve the problem, because as you said, the stopping would be because of reasons external to the actual program. Imagine a student who tends to produce programs with infinite loops in them.
You can represent both the program that is being analyzed and the proposed input to the program as Python strings.The second thing you notice is that this description sounds similar to something you've heard about before. That's a contradiction; goofy.py can't both halt and not halt.
The halting problem was one of the first problems that were proven to be undecidable. The halting problem is a cornerstone problem in computer science. This is accomplished with a Goofy.py then calls the terminates function and sends the input program as both the program to test and the input data for the program.
It is used mainly as a way to prove a given task is impossible, by showing that … This is a purely technical blog concerning topics such as Python, Ruby, Scala, Go, JavaScript, Linux, open source software, the Web, and lesser-known programming languages.
The function next returns the next valid string. In fact, let A be a Turing machine that solves the halting problem for one input, and B a Turing machine that solves the halting problem for every input. Let's just imagine for a moment that this book has inspired you to pursue a career as a computer professional. In 1936, Alan Turing proved that an algorithm to solve the Halting problem for all possible program-input pairs cannot exist.
We'll use this technique to show that the halting problem cannot be solved.We begin by assuming that there is some algorithm that can determine if a program terminates when executed on a particular input. > This is an example of NP-hard problem that is not NP-complete.
In the call to terminates, both the program and the data will be a copy of goofy.py, so if goofy.py halts when given itself as input, terminates will return true. I think that the title text is a direct reference to Karl Popper's falsifiability argument, since this is one of the most common example of a non-falsifiable statement. The input for the program.
The pass statement actually does nothing; if the terminates function returns true, goofy.py will go into an infinite loop.OK, this seems like a silly program, but there is nothing in principle that keeps us from writing it, provided that the terminates function exists.
But as soon as terminates returns false, goofy.py quits, so it does halt!
The halting problem is a cornerstone problem in computer science. It's got to be one or the other.Let's try it the other way around. Therefore I feel the comment about the number of inputs in the explanation can be removed.
The halting problem requires two parameters (a program and its parameters), while Randall's function only accepts one (the program). Throughout the lifetime of the universe, the computer only said: 'THERE IS INSUFFICIENT DATA FOR A MEANINGFUL ANSWER.')
Thanks.
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halting problem python