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An infinite loop is a sequence of instructions in a computer program which loops endlessly, ... The same can happen in Python: x = 0.1 while x!= 1: print (x) x += 0.1.
In computer science, a for-loop or for loop is a control flow statement for specifying iteration. Specifically, a for-loop functions by running a section of code repeatedly until a certain condition has been satisfied. For-loops have two parts: a header and a body. The header defines the iteration and the body is the code executed once per ...
If while is omitted, we get an infinite loop. The construction here can be thought of as a do loop with the while check in the middle. Hence this single construction can replace several constructions in most programming languages. Languages lacking this construct generally emulate it using an equivalent infinite-loop-with-break idiom:
Infinite loop – Programming idiom; Infinite regress – Philosophical problem; Infinitism – Philosophical view that knowledge may be justified by an infinite chain of reasons; Infinity mirror – Parallel or angled mirrors, creating smaller reflections that appear to recede to infinity
A conditional loop has the potential to become an infinite loop when nothing in the loop's body can affect the outcome of the loop's conditional statement. However, infinite loops can sometimes be used purposely, often with an exit from the loop built into the loop implementation for every computer language , but many share the same basic ...
This allows right folds to operate on infinite lists. By contrast, foldl will immediately call itself with new parameters until it reaches the end of the list. This tail recursion can be efficiently compiled as a loop, but can't deal with infinite lists at all — it will recurse forever in an infinite loop.
Python 2.5 implements better support for coroutine-like functionality, based on extended generators ; Python 3.3 improves this ability, by supporting delegating to a subgenerator ; Python 3.4 introduces a comprehensive asynchronous I/O framework as standardized in PEP 3156, which includes coroutines that leverage subgenerator delegation
A common algorithm design tactic is to divide a problem into sub-problems of the same type as the original, solve those sub-problems, and combine the results. This is often referred to as the divide-and-conquer method; when combined with a lookup table that stores the results of previously solved sub-problems (to avoid solving them repeatedly and incurring extra computation time), it can be ...