Given:
F(F(n)) = n
F(F(n + 2) + 2) = n
F(0) = 1
where n is a non-negative integer. F(129) = ?
How can we solve such kind of functional equations programmatically? My programming language of choice is Python.
Functional equations, in their most general terms, are really really hard. It is no coincidence that pretty much every international mathematics competition features one of them, usually looking about as innocent as the one you've written. The methods of solving them vary from plain induction to infinite-dimensional Banach space analysis and a generic programming approach to solving them is very unlikely.
In this particular case, here's a straight-forward approach:
Suppose for any two integers m, n we have F(m) = F(n) = k. But then m = F(F(m)) = F(k) = F(F(n)) = n : therefore m = n and F never takes the same value on two different inputs. But we know that F(F(n)) = n = F(F(n+2)+2) - therefore F(n) and F(n+2)+2 must be the same number - which is to say, F(n+2) == F(n) - 2 == F(n-2) - 4 = ... . Now we know F(0) = 1, so F(1) = F(F(0)) = 0. But then F(129) = F(127) - 2 = F(125) - 4 = ... = F(1) - 128 = -128
So there is your solution - but a mechanical algorithm for solving any variation just doesn't exist.
Based on what #ivancho and #aaronasterling have said, I have been able to write this program that should do the trick:
def f(n):
if not n:
return 1
else:
return f(n-2)-2
>>> f(4)
-3
Comment if this is not what you're after
Related
The 12th term, F12, is the first term to contain three digits.
What is the index of the first term in the Fibonacci sequence to contain 1000 digits?
a = 1
b = 1
i = 2
while(1):
c = a + b
i += 1
length = len(str(c))
if length == 1000:
print(i)
break
a = b
b = c
I got the answer(works fast enough). Just looking if there's a better way for this question
If you've answered the question, you'll find plenty of explanations on answers in the problem thread. The solution you posted is pretty much okay. You may get a slight speedup by simply checking that your c>=10^999 at every step instead of first converting it to a string.
The better method is to use the fact that when the Fibonacci numbers become large enough, the Fibonacci numbers converge to round(phi**n/(5**.5)) where phi=1.6180... is the golden ratio and round(x) rounds x to the nearest integer. Let's consider the general case of finding the first Fibonacci number with more than m digits. We are then looking for n such that round(phi**n/(5**.5)) >= 10**(m-1)
We can easily solve that by just taking the log of both sides and observe that
log(phi)*n - log(5)/2 >= m-1 and then solve for n.
If you're wondering "well how do I know that it has converged by the nth number?" Well, you can check for yourself, or you can look online.
Also, I think questions like these either belong on the Code Review SE or the Computer Science SE. Even Math Overflow might be a good place for Project Euler questions, since many are rooted in number theory.
Your solution is completely fine for #25 on project euler. However, if you really want to optimize for speed here you can try to calculate fibonacci using the identities I have written about in this blog post: https://sloperium.github.io/calculating-the-last-digits-of-large-fibonacci-numbers.html
from functools import lru_cache
#lru_cache(maxsize=None)
def fib4(n):
if n <= 1:
return n
if n % 2:
m = (n + 1) // 2
return fib4(m) ** 2 + fib4(m - 1) ** 2
else:
m = n // 2
return (2 * fib4(m - 1) + fib4(m)) * fib4(m)
def binarySearch( length):
first = 0
last = 10**5
found = False
while first <= last and not found:
midpoint = (first + last) // 2
length_string = len(str(fib4(midpoint)))
if length_string == length:
return midpoint -1
else:
if length < length_string:
last = midpoint - 1
else:
first = midpoint + 1
print(binarySearch(1000))
This code tests about 12 times faster than your solution. (it does require an initial guess about max size though)
I've written out a recursive algorithm for a little homegrown computer algebra system, where I'm applying pairwise reductions to the list of operands of an algebraic operation (adjacent operands only, as the algebra is non-commutative). I'm trying to get an idea of the runtime complexity of my algorithm (but unfortunately, as a physicist it's been a very long time since I took any undergrad CS courses that dealt with complexity analysis). Without going into details of the specific problem, I think I can formalize the algorithm in terms of a function f that is a "divide" step and a function g that combines the results. My algorithm would then take the following formal representation:
f(1) = 1 # recursion anchor for f
f(n) = g(f(n/2), f(n/2))
g(n, 0) = n, g(0, m) = m # recursion ...
g(1, 0) = g(0, 1) = 1 # ... anchors for g
/ g(g(n-1, 1), m-1) if reduction is "non-neutral"
g(n, m) = | g(n-1, m-1) if reduction is "neutral"
\ n + m if no reduction is possible
In this notation, the functions f and g receive lists as arguments and return lists, with the length of the input/output lists being the argument and the right-hand-side of the equations above.
For the full story, the actual code corresponding to f and g is the following:
def _match_replace_binary(cls, ops: list) -> list:
"""Reduce list of `ops`"""
n = len(ops)
if n <= 1:
return ops
ops_left = ops[:n//2]
ops_right = ops[n//2:]
return _match_replace_binary_combine(
cls,
_match_replace_binary(cls, ops_left),
_match_replace_binary(cls, ops_right))
def _match_replace_binary_combine(cls, a: list, b: list) -> list:
"""combine two fully reduced lists a, b"""
if len(a) == 0 or len(b) == 0:
return a + b
if len(a) == 1 and len(b) == 1:
return a + b
r = _get_binary_replacement(a[-1], b[0], cls._binary_rules)
if r is None:
return a + b
if r == cls.neutral_element:
return _match_replace_binary_combine(cls, a[:-1], b[1:])
r = [r, ]
return _match_replace_binary_combine(
cls,
_match_replace_binary_combine(cls, a[:-1], r),
b[1:])
I'm interested in the worst-case number of times get_binary_replacement is
called, depending on the size of ops
So I think I've got it now. To restate the problem: find the number of calls to _get_binary_replacement when calling _match_replace_binary with an input of size n.
define function g(n, m) (as in original question) that maps the size of the the two inputs of _match_replace_binary_combine to the size of the output
define a function T_g(n, m) that maps the size of the two inputs of _match_replace_binary_combine to the total number of calls to g that is required to obtain the result. This is also the (worst case) number of calls to _get_binary_replacement as each call to _match_replace_binary_combine calls _get_binary_replacement at most once
We can now consider the worst case and best case for g:
best case (no reduction): g(n,m) = n + m, T_g(n, m) = 1
worst case (all non-neutral reduction): g(n, m) = 1, T_g(n, m) = 2*(n+m) - 1 (I determined this empirically)
Now, the master theorem (WP) applies:
Going through the description on WP:
k=1 (the recursion anchor is for size 1)
We split into a = 2 subproblems of size n/2 in constant (d = 1) time
After solving the subproblems, the amount of work required to combine the results is c = T_g(n/2, n/2). This is n-1 (approximately n) in the worst case and 1 in the best case
Thus, following the examples on the WP page for the master theorem, the worst case complexity is n * log(n), and the best case complexity is n
Empirical trials seem to bear out this result. Any objections to my line of reasoning?
Is it possible to make the result from len(factors) be assigned as a variable? What I have so far is h = int(len(factors)), however i'm not sure if this actually does anything. My code below is attempting to take an integer 'r' and represent 'r' in the form (2^k)*t+1. This part of the code below is dealing with finding this product of powers of two and some other odd integer (2^k)*t.
It could be that I am going about this the wrong way, but from my research and trial and error, I have finally got this to work so far. But now more issues arise when extracting certain values.
from math import *
def executeproth():
r = input("Number to test:")
n = int(r)-1
d = 2
factors = []
while n % 2 == 0:
factors.append(d)
n = int(n/d)
h = int(len(factors))
print(n, factors, h)
# k = eval(2**h)
return factors
executeproth()
For example an input of 29 yields the following:
Number to test:29
14 [2] 1
7 [2, 2] 2
So in this instance, t=7, k=2, so we would have 29=(2^2)*7+1.
What I want to do is now take the third lines values, namely the '2', and use this for further calculations. But the commented out line # k = eval(2**h) throws the error as follows:
TypeError: eval() arg 1 must be a string, bytes or code object
So from what I can understand, the thing I am trying to evaluate is not in the correct form. I also wonder if the problem arises due to the nature of the while loop that keeps feeding values back in and creating multiples lists, as shown, and hence multiple values of h len(factors).
How would one print only the results of the 'final' iteration in the while loop? i.e. 7 [2,2] 2
Here this should fulfil your requirement,I don't think you really need to evaluate k.
Also this addresses the second part of your question too, to print the final result of the loop.
And it is as Gregory pointed out that convert explicitly to int only when needed and eval is for strings, your expression was already in integer terms.
def executeproth():
r = input("Number to test:")
n = int(r) - 1
d = 2
factors = []
while n % 2 == 0:
factors.append(d)
n = n // d
h = len(factors)
#print(n, factors, h)
else:
print"{} = ( 2 ^ {} ) * {} + 1".format(r,h,n)
return factors
executeproth()
First of all, you don't need to explicitly convert a value to an int just to use it in an expression in general. You do need it when processing the input since input() returns a string.
It is more idiomatic to use integer division a // b instead of int(a/b) in python 3.
Finally, eval is for evaluating strings, not expressions. Expressions are always evaluated.
from math import *
def executeproth():
r = input("Number to test:")
n = int(r)-1
d = 2
factors = []
while n % 2 == 0:
factors.append(d)
n = n // d
h = len(factors)
print(n, factors, h)
k = 2**h
# does the same thing but is less efficient
# k = eval("2**h")
return factors
executeproth()
As others have said you don't need eval here. In fact, you should generally avoid using eval since it can be dangerous. And in most situations where you do need to evaluate an expression in string form you can generally get by with the much safer ast.literal_eval. However, at this stage of your learning it's unlikely that you will encounter many situations where you need to work with such advanced features of the language.
Anyway, here are a few more improvements to your code.
You don't need to import the math module since you aren't using any of the functions or constants defined in it. But when you do need to import a module it's best to avoid the from module_name import * form since that pollutes your namespace with all of the names defined in the module.
You don't need to store those 2s in a list - just count them.
It's better to do your input (and input validation) in the outer layers of your program rather than doing it deep in the functions that perform your calculations.
Python provides various augmented assignment operators that you can use when you want to perform a simple operation on a value and store the result back under the original name. Eg count += 1 adds 1 to count, saving the result in count.
Python allows you to return multiple objects as a tuple, so you can return the final value of n and the count of the number of factors of 2 that you've found.
def executeproth(r):
n = r - 1
count = 0
if r != 0:
while n % 2 == 0:
count += 1
n //= 2
return n, count
r = int(input("Number to test: "))
n, count = executeproth(r)
k = 2 ** count
print("{0} = {1} * 2 ** {2} + 1".format(r, n, count))
#v = n*k + 1
#if v != r:
# print("Error!")
The if r != 0: prevents infinite looping if r is zero.
I've also added a (commented-out) test at the end. It's a good idea to do simple tests like that to make sure we're getting what we expect. Writing useful tests is an important part of program development.
Typical output:
Number to test: 0
0 = -1 * 2 ** 0 + 1
Number to test: 29
29 = 7 * 2 ** 2 + 1
Number to test: 57
57 = 7 * 2 ** 3 + 1
The task is to search every power of two below 2^10000, returning the index of the first power in which a string is contained. For example if the given string to search for is "7" the program will output 15, as 2^15 is the first power to contain 7 in it.
I have approached this with a brute force attempt which times out on ~70% of test cases.
for i in range(1,9999):
if search in str(2**i):
print i
break
How would one approach this with a time limit of 5 seconds?
Try not to compute 2^i at each step.
pow = 1
for i in xrange(1,9999):
if search in str(pow):
print i
break
pow *= 2
You can compute it as you go along. This should save a lot of computation time.
Using xrange will prevent a list from being built, but that will probably not make much of a difference here.
in is probably implemented as a quadratic string search algorithm. It may (or may not, you'd have to test) be more efficient to use something like KMP for string searching.
A faster approach could be computing the numbers directly in decimal
def double(x):
carry = 0
for i, v in enumerate(x):
d = v*2 + carry
if d > 99999999:
x[i] = d - 100000000
carry = 1
else:
x[i] = d
carry = 0
if carry:
x.append(carry)
Then the search function can become
def p2find(s):
x = [1]
for y in xrange(10000):
if s in str(x[-1])+"".join(("00000000"+str(y))[-8:]
for y in x[::-1][1:]):
return y
double(x)
return None
Note also that the digits of all powers of two up to 2^10000 are just 15 millions, and searching the static data is much faster. If the program must not be restarted each time then
def p2find(s, digits = []):
if len(digits) == 0:
# This precomputation happens only ONCE
p = 1
for k in xrange(10000):
digits.append(str(p))
p *= 2
for i, v in enumerate(digits):
if s in v: return i
return None
With this approach the first check will take some time, next ones will be very very fast.
Compute every power of two and build a suffix tree using each string. This is linear time in the size of all the strings. Now, the lookups are basically linear time in the length of each lookup string.
I don't think you can beat this for computational complexity.
There are only 10000 numbers. You don't need any complex algorithms. Simply calculated them in advance and do search. This should take merely 1 or 2 seconds.
powers_of_2 = [str(1<<i) for i in range(10000)]
def search(s):
for i in range(len(powers_of_2)):
if s in powers_of_2[i]:
return i
Try this
twos = []
twoslen = []
two = 1
for i in xrange(10000):
twos.append(two)
twoslen.append(len(str(two)))
two *= 2
tens = []
ten = 1
for i in xrange(len(str(two))):
tens.append(ten)
ten *= 10
s = raw_input()
l = len(s)
n = int(s)
for i in xrange(len(twos)):
for j in xrange(twoslen[i]):
k = twos[i] / tens[j]
if k < n: continue
if (k - n) % tens[l] == 0:
print i
exit()
The idea is to precompute every power of 2, 10 and and also to precompute the number of digits for every power of 2. In this way the problem is reduces to finding the minimum i for which there exist a j such that after removing the last j digits from 2 ** i you obtain a number which ends with n or expressed as a formula (2 ** i / 10 ** j - n) % 10 ** len(str(n)) == 0.
A big problem here is that converting a binary integer to decimal notation takes time quadratic in the number of bits (at least in the straightforward way Python does it). It's actually faster to fake your own decimal arithmetic, as #6502 did in his answer.
But it's very much faster to let Python's decimal module do it - at least under Python 3.3.2 (I don't know how much C acceleration is built in to Python decimal versions before that). Here's code:
class S:
def __init__(self):
import decimal
decimal.getcontext().prec = 4000 # way more than enough for 2**10000
p2 = decimal.Decimal(1)
full = []
for i in range(10000):
s = "%s<%s>" % (p2, i)
##assert s == "%s<%s>" % (str(2**i), i)
full.append(s)
p2 *= 2
self.full = "".join(full)
def find(self, s):
import re
pat = s + "[^<>]*<(\d+)>"
m = re.search(pat, self.full)
if m:
return int(m.group(1))
else:
print(s, "not found!")
and sample usage:
>>> s = S()
>>> s.find("1")
0
>>> s.find("2")
1
>>> s.find("3")
5
>>> s.find("65")
16
>>> s.find("7")
15
>>> s.find("00000")
1491
>>> s.find("666")
157
>>> s.find("666666")
2269
>>> s.find("66666666")
66666666 not found!
s.full is a string with a bit over 15 million characters. It looks like this:
>>> print(s.full[:20], "...", s.full[-20:])
1<0>2<1>4<2>8<3>16<4 ... 52396298354688<9999>
So the string contains each power of 2, with the exponent following a power enclosed in angle brackets. The find() method constructs a regular expression to search for the desired substring, then look ahead to find the power.
Playing around with this, I'm convinced that just about any way of searching is "fast enough". It's getting the decimal representations of the large powers that sucks up the vast bulk of the time. And the decimal module solves that one.
I'm trying to model this problem (for details on it, http://www.mpi-hd.mpg.de/personalhomes/bauke/LABS/index.php)
I've seen that the proven minimum for a sequence of 10 digits is 13. However, my application seems to be getting 12 quite frequently. This implies some kind of error in my program. Is there an obvious error in the way I've modeled those summations in this code?
def evaluate(self):
self.fitness = 10000000000 #horrible practice, I know..
h = 0
for g in range(1, len(self.chromosome) - 1):
c = self.evaluateHelper(g)
h += c**2
self.fitness = h
def evaluateHelper(self, g):
"""
Helper for evaluate function. The c sub g function.
"""
totalSum = 0
for i in range(len(self.chromosome) - g - 1):
product = self.chromosome[i] * self.chromosome[(i + g) % (len(self.chromosome))]
totalSum += product
return totalSum
I can't spot any obvious bug offhand, but you're making things really complicated, so maybe a bug's lurking and hiding somewhere. What about
def evaluateHelper(self, g):
return sum(a*b for a, b in zip(self.chromosome, self.chomosome[g:]))
this should return the same values you're computing in that subtle loop (where I think the % len... part is provably redundant). Similarly, the evaluate method seems ripe for a similar 1-liner. But, anyway...
There's a potential off-by-one issue: the formulas in the article you point to are summing for g from 1 to N-1 included -- you're using range(1, len(...)-1), whereby N-1 is excluded. Could that be the root of the problem you observe?
Your bug was here:
for i in range(len(self.chromosome) - g - 1):
The maximum value for i will be len(self.chromosome) - g - 2, because range is exclusive. Thus, you don't consider the last pair. It's basically the same as your other bug, just in a different place.