The compiler is simply able to transform this
int fac_times (int n, int acc) {
if (n == 0) return acc;
else return fac_times(n - 1, acc * n);
}
into something like this:
int fac_times (int n, int acc) {
label:
if (n == 0) return acc;
acc *= n--;
goto label;
}
You ask why "it doesn't require stack to remember its return address".
I would like to turn this around. It does use the stack to remember the return address. The trick is that the function in which the tail recursion occurs has its own return address on the stack, and when it jumps to the called function, it will treat this as it's own return address.
Concretely, without tail call optimization:
f: ...
CALL g
RET
g:
...
RET
In this case, when g
is called, the stack will look like:
SP -> Return address of "g"
Return address of "f"
On the other hand, with tail call optimization:
f: ...
JUMP g
g:
...
RET
In this case, when g
is called, the stack will look like:
SP -> Return address of "f"
Clearly, when g
returns, it will return to the location where f
was called from.
EDIT: The example above use the case where one function calls another function. The mechanism is identical when the function calls itself.
Tail recursion can usually be transformed into a loop by the compiler, especially when accumulators are used.
// tail recursion
int fac_times (int n, int acc = 1) {
if (n == 0) return acc;
else return fac_times(n - 1, acc * n);
}
would compile to something like
// accumulator
int fac_times (int n) {
int acc = 1;
while (n > 0) {
acc *= n;
n -= 1;
}
return acc;
}
The return values in regular recursive function are composed of two types of values:
Let's look at your example:
int factorial (int n) {
if (n == 0) return 1;
else return n * factorial(n - 1);
}
The frame f(5) "stores" the result of it's own computation (5) and the value of f(4), for example. If i call factorial(5), just before the stack calls begin to collapse, i have:
[Stack_f(5): return 5 * [Stack_f(4): 4 * [Stack_f(3): 3 * ... [1[1]]
Notice that each stack stores, besides the values i mentioned, the whole scope of the function. So, the memory usage for a recursive function f is O(x), where x is the number of recursive calls i have to made. So, if i needb 1kb of RAM to calculate factorial(1) or factorial(2), i need ~100k to calculate factorial(100), and so on.
In a Tail Recursion, i pass the result of the partial calculations in each recursive frame to the next one using parameters. Let's see our factorial example, Tail Recursive:
int factorial (int n) {
int helper(int num, int accumulated)
{
if num == 0 return accumulated
else return helper(num - 1, accumulated*num)
}
return helper(n, 1)
}
Let's look at it's frames in factorial(4):
[Stack f(4, 5): Stack f(3, 20): [Stack f(2,60): [Stack f(1, 120): 120]]]]
See the differences? In "regular" recursive calls the return functions recursively compose the final value. In Tail Recursion they only reference the base case (last one evaluated). We call accumulator the argument that keeps track of the older values.
The regular recursive function go as follows:
type regular(n)
base_case
computation
return (result of computation) combined with (regular(n towards base case))
To transform it in a Tail recursion we:
Look:
type tail(n):
type helper(n, accumulator):
if n == base case
return accumulator
computation
accumulator = computation combined with accumulator
return helper(n towards base case, accumulator)
helper(n, base case)
See the difference?
Since no state is being stored on the Non-Border-Cases of the Tail Call stacks, they aren't so important. Some languages/interpreters then substitute the old stack with the new one. So, with no stack frames constraining the number of calls, the Tail Calls behave just like a for-loop in these cases.
It's up to your compiler to optimize it, or no.
Here is a simple example that shows how recursive functions work:
long f (long n)
{
if (n == 0) // have we reached the bottom of the ocean ?
return 0;
// code executed in the descendence
return f(n-1) + 1; // recurrence
// code executed in the ascendence
}
Tail recursion is a simple recursive function, where recurrence is done at the end of the function, thus no code is done in ascendence, which helps most compilers of high-level programming languages to do what is known as Tail Recursion Optimization, also has a more complex optimization known as the Tail recursion modulo
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