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They provide an elegant way of handling errors, without resorting to exception handling; when a function that may fail returns a result type, the programmer is forced to consider success or failure paths, before getting access to the expected result; this eliminates the possibility of an erroneous programmer assumption.
In Python, functions are first-class objects that can be created and passed around dynamically. Python's limited support for anonymous functions is the lambda construct. An example is the anonymous function which squares its input, called with the argument of 5:
C does not provide direct support to exception handling: it is the programmer's responsibility to prevent errors in the first place and test return values from the functions. In any case, a possible way to implement exception handling in standard C is to use setjmp/longjmp functions:
This creates static tables at compile time and link time that relate ranges of the program counter to the program state with respect to exception handling. [22] Then, if an exception is thrown, the runtime system looks up the current instruction location in the tables and determines what handlers are in play and what needs to be done.
For example, in the expression (f(x)-1)/(f(x)+1), the function f cannot be called only once with its value used two times since the two calls may return different results. Moreover, in the few languages which define the order of evaluation of the division operator's operands, the value of x must be fetched again before the second call, since ...
In Python, functions are first-class objects, just like strings, numbers, lists etc. This feature eliminates the need to write a function object in many cases. Any object with a __call__() method can be called using function-call syntax. An example is this accumulator class (based on Paul Graham's study on programming language syntax and ...
In many applications, objective functions, including loss functions as a particular case, are determined by the problem formulation. In other situations, the decision maker’s preference must be elicited and represented by a scalar-valued function (called also utility function) in a form suitable for optimization — the problem that Ragnar Frisch has highlighted in his Nobel Prize lecture. [4]