I found this problem in a programming forum Ohjelmointiputka:
https://www.ohjelmointiputka.net/postit/tehtava.php?tunnus=ahdruu and
https://www.ohjelmointiputka.net/postit/tehtava.php?tunnus=ahdruu2
Somebody said that there is a solution found by a computer, but I was unable to find a proof.
Prove that there is a matrix with 117 elements containing the digits such that one can read the squares of the numbers 1, 2, ..., 100.
Here read means that you fix the starting position and direction (8 possibilities) and then go in that direction, concatenating the numbers. For example, if you can find for example the digits 1,0,0,0,0,4 consecutively, you have found the integer 100004, which contains the square numbers of 1, 2, 10, 100 and 20, since you can read off 1, 4, 100, 10000, and 400 (reversed) from that sequence.
But there are so many numbers to be found (100 square numbers, to be precise, or 81 if you remove those that are contained in another square number with total 312 digits) and so few integers in a matrix that you have to put all those square numbers so densely that finding such a matrix is difficult, at least for me.
I found that if there is such a matrix mxn, we may assume without loss of generalty that m<=n. Therefore, the matrix must be of the type 1x117, 3x39 or 9x13. But what kind of algorithm will find the matrix?
I have managed to do the program that checks if numbers to be added can be put on the board. But how can I implemented the searching algorithm?
# -*- coding: utf-8 -*-
# Returns -1 if can not put and value how good a solution is if can be put. Bigger value of x is better.
def can_put_on_grid(grid, number, start_x, start_y, direction):
# Check that the new number lies inside the grid.
x = 0
if start_x < 0 or start_x > len(grid[0]) - 1 or start_y < 0 or start_y > len(grid) - 1:
return -1
end = end_coordinates(number, start_x, start_y, direction)
if end[0] < 0 or end[0] > len(grid[0]) - 1 or end[1] < 0 or end[1] > len(grid) - 1:
return -1
# Test if new number does not intersect any previous number.
A = [-1,-1,-1,0,0,1,1,1]
B = [-1,0,1,-1,1,-1,0,1]
for i in range(0,len(number)):
if grid[start_x + A[direction] * i][start_y + B[direction] * i] not in ("X", number[i]):
return -1
else:
if grid[start_x + A[direction] * i][start_y + B[direction] * i] == number[i]:
x += 1
return x
def end_coordinates(number, start_x, start_y, direction):
end_x = None
end_y = None
l = len(number)
if direction in (1, 4, 7):
end_x = start_x - l + 1
if direction in (3, 6, 5):
end_x = start_x + l - 1
if direction in (2, 0):
end_x = start_x
if direction in (1, 2, 3):
end_y = start_y - l + 1
if direction in (7, 0, 5):
end_y = start_y + l - 1
if direction in (4, 6):
end_y = start_y
return (end_x, end_y)
if __name__ == "__main__":
A = [['X' for x in range(13)] for y in range(9)]
numbers = [str(i*i) for i in range(1, 101)]
directions = [0, 1, 2, 3, 4, 5, 6, 7]
for i in directions:
C = can_put_on_grid(A, "10000", 3, 5, i)
if C > -1:
print("One can put the number to the grid!")
exit(0)
I also found think that brute force search or best first search is too slow. I think there might be a solution using simulated annealing, genetic algorithm or bin packing algorithm. I also wondered if one can apply Markov chains somehow to find the grid. Unfortunately those seems to be too hard for me to implemented at current skills.
There is a program for that in https://github.com/minkkilaukku/square-packing/blob/master/sqPackMB.py . Just change M=9, N=13 from the lines 20 and 21.
Related
I'm trying to figure out how to solve a problem that seems a tricky variation of a common algorithmic problem but require additional logic to handle specific requirements.
Given a list of coins and an amount, I need to count the total number of possible ways to extract the given amount using an unlimited supply of available coins (and this is a classical change making problem https://en.wikipedia.org/wiki/Change-making_problem easily solved using dynamic programming) that also satisfy some additional requirements:
extracted coins are splittable into two sets of equal size (but not necessarily of equal sum)
the order of elements inside the set doesn't matter but the order of set does.
Examples
Amount of 6 euros and coins [1, 2]: solutions are 4
[(1,1), (2,2)]
[(1,1,1), (1,1,1)]
[(2,2), (1,1)]
[(1,2), (1,2)]
Amount of 8 euros and coins [1, 2, 6]: solutions are 7
[(1,1,2), (1,1,2)]
[(1,2,2), (1,1,1)]
[(1,1,1,1), (1,1,1,1)]
[(2), (6)]
[(1,1,1), (1,2,2)]
[(2,2), (2,2)]
[(6), (2)]
By now I tried different approaches but the only way I found was to collect all the possible solution (using dynamic programming) and then filter non-splittable solution (with an odd number of coins) and duplicates. I'm quite sure there is a combinatorial way to calculate the total number of duplication but I can't figure out how.
(The following method first enumerates partitions. My other answer generates the assignments in a bottom-up fashion.) If you'd like to count splits of the coin exchange according to coin count, and exclude redundant assignments of coins to each party (for example, where splitting 1 + 2 + 2 + 1 into two parts of equal cardinality is only either (1,1) | (2,2), (2,2) | (1,1) or (1,2) | (1,2) and element order in each part does not matter), we could rely on enumeration of partitions where order is disregarded.
However, we would need to know the multiset of elements in each partition (or an aggregate of similar ones) in order to count the possibilities of dividing them in two. For example, to count the ways to split 1 + 2 + 2 + 1, we would first count how many of each coin we have:
Python code:
def partitions_with_even_number_of_parts_as_multiset(n, coins):
results = []
def C(m, n, s, p):
if n < 0 or m <= 0:
return
if n == 0:
if not p:
results.append(s)
return
C(m - 1, n, s, p)
_s = s[:]
_s[m - 1] += 1
C(m, n - coins[m - 1], _s, not p)
C(len(coins), n, [0] * len(coins), False)
return results
Output:
=> partitions_with_even_number_of_parts_as_multiset(6, [1,2,6])
=> [[6, 0, 0], [2, 2, 0]]
^ ^ ^ ^ this one represents two 1's and two 2's
Now since we are counting the ways to choose half of these, we need to find the coefficient of x^2 in the polynomial multiplication
(x^2 + x + 1) * (x^2 + x + 1) = ... 3x^2 ...
which represents the three ways to choose two from the multiset count [2,2]:
2,0 => 1,1
0,2 => 2,2
1,1 => 1,2
In Python, we can use numpy.polymul to multiply polynomial coefficients. Then we lookup the appropriate coefficient in the result.
For example:
import numpy
def count_split_partitions_by_multiset_count(multiset):
coefficients = (multiset[0] + 1) * [1]
for i in xrange(1, len(multiset)):
coefficients = numpy.polymul(coefficients, (multiset[i] + 1) * [1])
return coefficients[ sum(multiset) / 2 ]
Output:
=> count_split_partitions_by_multiset_count([2,2,0])
=> 3
(Posted a similar answer here.)
Here is a table implementation and a little elaboration on algrid's beautiful answer. This produces an answer for f(500, [1, 2, 6, 12, 24, 48, 60]) in about 2 seconds.
The simple declaration of C(n, k, S) = sum(C(n - s_i, k - 1, S[i:])) means adding all the ways to get to the current sum, n using k coins. Then if we split n into all ways it can be partitioned in two, we can just add all the ways each of those parts can be made from the same number, k, of coins.
The beauty of fixing the subset of coins we choose from to a diminishing list means that any arbitrary combination of coins will only be counted once - it will be counted in the calculation where the leftmost coin in the combination is the first coin in our diminishing subset (assuming we order them in the same way). For example, the arbitrary subset [6, 24, 48], taken from [1, 2, 6, 12, 24, 48, 60], would only be counted in the summation for the subset [6, 12, 24, 48, 60] since the next subset, [12, 24, 48, 60] would not include 6 and the previous subset [2, 6, 12, 24, 48, 60] has at least one 2 coin.
Python code (see it here; confirm here):
import time
def f(n, coins):
t0 = time.time()
min_coins = min(coins)
m = [[[0] * len(coins) for k in xrange(n / min_coins + 1)] for _n in xrange(n + 1)]
# Initialize base case
for i in xrange(len(coins)):
m[0][0][i] = 1
for i in xrange(len(coins)):
for _i in xrange(i + 1):
for _n in xrange(coins[_i], n + 1):
for k in xrange(1, _n / min_coins + 1):
m[_n][k][i] += m[_n - coins[_i]][k - 1][_i]
result = 0
for a in xrange(1, n + 1):
b = n - a
for k in xrange(1, n / min_coins + 1):
result = result + m[a][k][len(coins) - 1] * m[b][k][len(coins) - 1]
total_time = time.time() - t0
return (result, total_time)
print f(500, [1, 2, 6, 12, 24, 48, 60])
I'm looking for solution to my problem. Say I have a number X, now I want to generate 20 random numbers whose sum would equal to X, but I want those random numbers to have enthropy in them. So for example, if X = 50, the algorithm should generate
3
11
0
6
19
7
etc. The sum of given numbres should equal to 50.
Is there any simple way to do that?
Thanks
Simple way:
Generate random number between 1 and X : say R1;
subtract R1 from X, now generate a random number between 1 and (X - R1) : say R2. Repeat the process until all Ri add to X : i.e. (X-Rn) is zero. Note: each consecutive number Ri will be smaller then the first. If you want the final sequence to look more random, simply permute the resulting Ri numbers. I.e. if you generate for X=50, an array like: 22,11,9,5,2,1 - permute it to get something like 9,22,2,11,1,5. You can also put a limit to how large any random number can be.
One fairly straightforward way to get k random values that sum to N is to create an array of size k+1, add values 0 and N, and fill the rest of the array with k-1 randomly generated values between 1 and N-1. Then sort the array and take the differences between successive pairs.
Here's an implementation in Ruby:
def sum_k_values_to_n(k = 20, n = 50)
a = Array.new(k + 1) { 1 + rand(n - 1) }
a[0] = 0
a[-1] = n
a.sort!
(1..(a.length - 1)).collect { |i| a[i] - a[i-1] }
end
p sum_k_values_to_n(3, 10) # produces, e.g., [2, 3, 5]
p sum_k_values_to_n # produces, e.g., [5, 2, 3, 1, 6, 0, 4, 4, 5, 0, 2, 1, 0, 5, 7, 2, 1, 1, 0, 1]
Say you have a vertical game board of length n (being the number of spaces). And you have a three-sided die that has the options: go forward one, stay and go back one. If you go below or above the number of board game spaces it is an invalid game. The only valid move once you reach the end of the board is "stay". Given an exact number of die rolls t, is it possible to algorithmically work out the number of unique dice rolls that result in a winning game?
So far I've tried producing a list of every possible combination of (-1,0,1) for the given number of die rolls and sorting through the list to see if any add up to the length of the board and also meet all the requirements for being a valid game. But this is impractical for dice rolls above 20.
For example:
t=1, n=2; Output=1
t=3, n=2; Output=3
You can use a dynamic programming approach. The sketch of a recurrence is:
M(0, 1) = 1
M(t, n) = T(t-1, n-1) + T(t-1, n) + T(t-1, n+1)
Of course you have to consider the border cases (like going off the board or not allowing to exit the end of the board, but it's easy to code that).
Here's some Python code:
def solve(N, T):
M, M2 = [0]*N, [0]*N
M[0] = 1
for i in xrange(T):
M, M2 = M2, M
for j in xrange(N):
M[j] = (j>0 and M2[j-1]) + M2[j] + (j+1<N-1 and M2[j+1])
return M[N-1]
print solve(3, 2) #1
print solve(2, 1) #1
print solve(2, 3) #3
print solve(5, 20) #19535230
Bonus: fancy "one-liner" with list compreehension and reduce
def solve(N, T):
return reduce(
lambda M, _: [(j>0 and M[j-1]) + M[j] + (j<N-2 and M[j+1]) for j in xrange(N)],
xrange(T), [1]+[0]*N)[-1]
Let M[i, j] be an N by N matrix with M[i, j] = 1 if |i-j| <= 1 and 0 otherwise (and the special case for the "stay" rule of M[N, N-1] = 0)
This matrix counts paths of length 1 from position i to position j.
To find paths of length t, simply raise M to the t'th power. This can be performed efficiently by linear algebra packages.
The solution can be read off: M^t[1, N].
For example, computing paths of length 20 on a board of size 5 in an interactive Python session:
>>> import numpy
>>> M = numpy.matrix('1 1 0 0 0;1 1 1 0 0; 0 1 1 1 0; 0 0 1 1 1; 0 0 0 0 1')
>>> M
matrix([[1, 1, 0, 0, 0],
[1, 1, 1, 0, 0],
[0, 1, 1, 1, 0],
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 1]])
>>> M ** 20
matrix([[31628466, 51170460, 51163695, 31617520, 19535230],
[51170460, 82792161, 82787980, 51163695, 31617520],
[51163695, 82787980, 82792161, 51170460, 31628465],
[31617520, 51163695, 51170460, 31628466, 19552940],
[ 0, 0, 0, 0, 1]])
So there's M^20[1, 5], or 19535230 paths of length 20 from start to finish on a board of size 5.
Try a backtracking algorithm. Recursively "dive down" into depth t and only continue with dice values that could still result in a valid state. Propably by passing a "remaining budget" around.
For example, n=10, t=20, when you reached depth 10 of 20 and your budget is still 10 (= steps forward and backwards seemed to cancelled), the next recursion steps until depth t would discontinue the 0 and -1 possibilities, because they could not result in a valid state at the end.
A backtracking algorithms for this case is still very heavy (exponential), but better than first blowing up a bubble with all possibilities and then filtering.
Since zeros can be added anywhere, we'll multiply those possibilities by the different arrangements of (-1)'s:
X (space 1) X (space 2) X (space 3) X (space 4) X
(-1)'s can only appear in spaces 1,2 or 3, not in space 4. I got help with the mathematical recurrence that counts the number of ways to place minus ones without skipping backwards.
JavaScript code:
function C(n,k){if(k==0||n==k)return 1;var p=n;for(var i=2;i<=k;i++)p*=(n+1-i)/i;return p}
function sumCoefficients(arr,cs){
var s = 0, i = -1;
while (arr[++i]){
s += cs[i] * arr[i];
}
return s;
}
function f(n,t){
var numMinusOnes = (t - (n-1)) >> 1
result = C(t,n-1),
numPlaces = n - 2,
cs = [];
for (var i=1; numPlaces-i>=i-1; i++){
cs.push(-Math.pow(-1,i) * C(numPlaces + 1 - i,i));
}
var As = new Array(cs.length),
An;
As[0] = 1;
for (var m=1; m<=numMinusOnes; m++){
var zeros = t - (n-1) - 2*m;
An = sumCoefficients(As,cs);
As.unshift(An);
As.pop();
result += An * C(zeros + 2*m + n-1,zeros);
}
return result;
}
Output:
console.log(f(5,20))
19535230
You are given the locations of various cars in the same lane on a highway as doubles to a vector, in no particular order. How can you find the largest gap between neighboring cars in O(n) time?
It seems like a simple solution would be to sort then check, but of course this isn't linear.
Divide the vector in n+1 equally sized buckets. For each such buckets, store the maximum and the minimum value, all other values can be discarded. Because of the pigeonhole principle, at least one of those parts is empty, so the non-minimum/non-maximum values in either parts don't have an influence for the result.
Then, go over the buckets and calculate the distance to the next and the previous non-empty bucket, and take the maximum; this is the final result.
An example with n=5 and values 5,2,20,17,3. Minimum is 2, maximum is 20 => bucket size is (20-2)/5 = 4.
Bucket: 2 6 10 14 18 20
Min/Max: 2-5 - - 17,17 20,20
Differences: 2-5, 5-17, 17-20.
Maximum is 5-17.
My Python implementation of ipc's solution:
def maximum_gap(l):
n = len(l)
if n < 2:
return 0
(x_min, x_max) = (min(l), max(l))
if x_min == x_max:
return 0
buckets = [None] * (n + 1)
bucket_size = float(x_max - x_min) / n
for x in l:
k = int((x - x_min) / bucket_size)
if buckets[k] is None:
buckets[k] = (x, x)
else:
buckets[k] = (min(x, buckets[k][0]), max(x, buckets[k][1]))
result = 0
for i in range(n):
if buckets[i + 1] is None:
buckets[i + 1] = buckets[i]
else:
result = max(result, buckets[i + 1][0] - buckets[i][1])
return result
assert maximum_gap([]) == 0
assert maximum_gap([42]) == 0
assert maximum_gap([1, 1, 1, 1]) == 0
assert maximum_gap([1, 2, 3, 4, 6, 8]) == 2
assert maximum_gap([5, 2, 20, 17, 3]) == 12
I use a tuple for bucket's elements, None if empty. In the last part, I eliminate preemptively any remaining empty bucket by assigning it to the previous one (this works, since the first one is guaranteed to be non-empty).
Note the special case when all elements are equal.
I have a program (a fractal) that draws lines in an interlaced order. Originally, given H lines to draw, it determines the number of frames N, and draws every Nth frame, then every N+1'th frame, etc.
For example, if H = 10 and N = 3, it draws them in order:
0, 3, 6, 9,
1, 4, 7,
2, 5, 8.
However I didn't like the way bands would gradually thicken, leaving large areas between undrawn for a long time. So the method was enhanced to recursively draw midpoint lines in each group instead of the immediately sebsequent lines, for example:
0, (32) # S (step size) = 32
8, (24) # S = 16
4, (12) # S = 8
2, 6, (10) # S = 4
1, 3, 5, 7, 9. # S = 2
(The numbers in parentheses are out of range and not drawn.) The algorithm's pretty simple:
Set S to a power of 2 greater than N*2, set F = 0.
While S > 1:
Draw frame F.
Set F = F + S.
If F >= H, then set S = S / 2; set F = S / 2.
When the odd numbered frames are drawn on the last step size, they are drawn in simple order just as an the initial (annoying) method. The same with every fourth frame, etc. It's not as bad because some intermediate frames have already been drawn.
But the same permutation could recursively be applied to the elements for each step size. In the example above, the last line would change to:
1, # the 0th element, S' = 16
9, # 4th, S' = 8
5, # 2nd, S' = 4
3, 7. # 1st and 3rd, S' = 2
The previous lines have too few elements for the recursion to take effect. But if N was large enough, some lines might require multiple levels of recursion. Any step size with 3 or more corresponding elements can be recursively permutated.
Question 1. Is there a common name for this permutation on N elements, that I could use to find additional material on it? I am also interested in any similar examples that may exist. I would be surprised if I'm the first person to want to do this.
Question 2. Are there some techniques I could use to compute it? I'm working in C but I'm more interested at the algorithm level at this stage; I'm happy to read code other language (within reason).
I have not yet tackled its implemention. I expect I will precompute the permutation first (contrary to the algorithm for the previous method, above). But I'm also interested if there is a simple way to get the next frame to draw without having to precomputing it, similar in complexity to the previous method.
It sounds as though you're trying to construct one-dimensional low-discrepancy sequences. Your permutation can be computed by reversing the binary representation of the index.
def rev(num_bits, i):
j = 0
for k in xrange(num_bits):
j = (j << 1) | (i & 1)
i >>= 1
return j
Example usage:
>>> [rev(4,i) for i in xrange(16)]
[0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13, 3, 11, 7, 15]
A variant that works on general n:
def rev(n, i):
j = 0
while n >= 2:
m = i & 1
if m:
j += (n + 1) >> 1
n = (n + 1 - m) >> 1
i >>= 1
return j
>>> [rev(10,i) for i in xrange(10)]
[0, 5, 3, 8, 2, 7, 4, 9, 1, 6]