O(nlogn) Algorithm - Find three evenly spaced ones within binary string
I had this question on an Algorithms test yesterday, and I can\'t figure out the answer. It is driving me absolutely crazy, because it was worth about 40 points. I figure
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I thought of a divide-and-conquer approach that might work.
First, in preprocessing you need to insert all numbers less than one half your input size (n/3) into a list.
Given a string:
0000010101000100(note that this particular example is valid)Insert all primes (and 1) from 1 to (16/2) into a list: {1, 2, 3, 4, 5, 6, 7}
Then divide it in half:
100000101 01000100Keep doing this until you get to strings of size 1. For all size-one strings with a 1 in them, add the index of the string to the list of possibilities; otherwise, return -1 for failure.
You'll also need to return a list of still-possible spacing distances, associated with each starting index. (Start with the list you made above and remove numbers as you go) Here, an empty list means you're only dealing with one 1 and so any spacing is possible at this point; otherwise the list includes spacings that must be ruled out.
So continuing with the example above:
1000 0101 0100 010010 00 01 01 01 00 01 001 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0In the first combine step, we have eight sets of two now. In the first, we have the possibility of a set, but we learn that spacing by 1 is impossible because of the other zero being there. So we return 0 (for the index) and {2,3,4,5,7} for the fact that spacing by 1 is impossible. In the second, we have nothing and so return -1. In the third we have a match with no spacings eliminated in index 5, so return 5, {1,2,3,4,5,7}. In the fourth pair we return 7, {1,2,3,4,5,7}. In the fifth, return 9, {1,2,3,4,5,7}. In the sixth, return -1. In the seventh, return 13, {1,2,3,4,5,7}. In the eighth, return -1.
Combining again into four sets of four, we have:
1000: Return (0, {4,5,6,7})0101: Return (5, {2,3,4,5,6,7}), (7, {1,2,3,4,5,6,7})0100: Return (9, {3,4,5,6,7})0100: Return (13, {3,4,5,6,7})Combining into sets of eight:
10000101: Return (0, {5,7}), (5, {2,3,4,5,6,7}), (7, {1,2,3,4,5,6,7})01000100: Return (9, {4,7}), (13, {3,4,5,6,7})Combining into a set of sixteen:
10000101 01000100As we've progressed, we keep checking all the possibilities so far. Up to this step we've left stuff that went beyond the end of the string, but now we can check all the possibilities.
Basically, we check the first 1 with spacings of 5 and 7, and find that they don't line up to 1's. (Note that each check is CONSTANT, not linear time) Then we check the second one (index 5) with spacings of 2, 3, 4, 5, 6, and 7-- or we would, but we can stop at 2 since that actually matches up.
Phew! That's a rather long algorithm.
I don't know 100% if it's O(n log n) because of the last step, but everything up to there is definitely O(n log n) as far as I can tell. I'll get back to this later and try to refine the last step.
EDIT: Changed my answer to reflect Welbog's comment. Sorry for the error. I'll write some pseudocode later, too, when I get a little more time to decipher what I wrote again. ;-)
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How about a simple O(n) solution, with O(n^2) space? (Uses the assumption that all bitwise operators work in O(1).)
The algorithm basically works in four stages:
Stage 1: For each bit in your original number, find out how far away the ones are, but consider only one direction. (I considered all the bits in the direction of the least significant bit.)
Stage 2: Reverse the order of the bits in the input;
Stage 3: Re-run step 1 on the reversed input.
Stage 4: Compare the results from Stage 1 and Stage 3. If any bits are equally spaced above AND below we must have a hit.
Keep in mind that no step in the above algorithm takes longer than O(n). ^_^
As an added benefit, this algorithm will find ALL equally spaced ones from EVERY number. So for example if you get a result of "0x0005" then there are equally spaced ones at BOTH 1 and 3 units away
I didn't really try optimizing the code below, but it is compilable C# code that seems to work.
using System; namespace ThreeNumbers { class Program { const int uint32Length = 32; static void Main(string[] args) { Console.Write("Please enter your integer: "); uint input = UInt32.Parse(Console.ReadLine()); uint[] distancesLower = Distances(input); uint[] distancesHigher = Distances(Reverse(input)); PrintHits(input, distancesLower, distancesHigher); } /// <summary> /// Returns an array showing how far the ones away from each bit in the input. Only /// considers ones at lower signifcant bits. Index 0 represents the least significant bit /// in the input. Index 1 represents the second least significant bit in the input and so /// on. If a one is 3 away from the bit in question, then the third least significant bit /// of the value will be sit. /// /// As programed this algorithm needs: O(n) time, and O(n*log(n)) space. /// (Where n is the number of bits in the input.) /// </summary> public static uint[] Distances(uint input) { uint[] distanceToOnes = new uint[uint32Length]; uint result = 0; //Sets how far each bit is from other ones. Going in the direction of LSB to MSB for (uint bitIndex = 1, arrayIndex = 0; bitIndex != 0; bitIndex <<= 1, ++arrayIndex) { distanceToOnes[arrayIndex] = result; result <<= 1; if ((input & bitIndex) != 0) { result |= 1; } } return distanceToOnes; } /// <summary> /// Reverses the bits in the input. /// /// As programmed this algorithm needs O(n) time and O(n) space. /// (Where n is the number of bits in the input.) /// </summary> /// <param name="input"></param> /// <returns></returns> public static uint Reverse(uint input) { uint reversedInput = 0; for (uint bitIndex = 1; bitIndex != 0; bitIndex <<= 1) { reversedInput <<= 1; reversedInput |= (uint)((input & bitIndex) != 0 ? 1 : 0); } return reversedInput; } /// <summary> /// Goes through each bit in the input, to check if there are any bits equally far away in /// the distancesLower and distancesHigher /// </summary> public static void PrintHits(uint input, uint[] distancesLower, uint[] distancesHigher) { const int offset = uint32Length - 1; for (uint bitIndex = 1, arrayIndex = 0; bitIndex != 0; bitIndex <<= 1, ++arrayIndex) { //hits checks if any bits are equally spaced away from our current value bool isBitSet = (input & bitIndex) != 0; uint hits = distancesLower[arrayIndex] & distancesHigher[offset - arrayIndex]; if (isBitSet && (hits != 0)) { Console.WriteLine(String.Format("The {0}-th LSB has hits 0x{1:x4} away", arrayIndex + 1, hits)); } } } } }Someone will probably comment that for any sufficiently large number, bitwise operations cannot be done in O(1). You'd be right. However, I'd conjecture that every solution that uses addition, subtraction, multiplication, or division (which cannot be done by shifting) would also have that problem.
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This may help....
This problem reduces to the following:
Given a sequence of positive integers, find a contiguous subsequence partitioned into a prefix and a suffix such that the sum of the prefix of the subsequence is equal to the sum of the suffix of the subsequence.
For example, given a sequence of
[ 3, 5, 1, 3, 6, 5, 2, 2, 3, 5, 6, 4 ], we would find a subsequence of[ 3, 6, 5, 2, 2]with a prefix of[ 3, 6 ]with prefix sum of9and a suffix of[ 5, 2, 2 ]with suffix sum of9.The reduction is as follows:
Given a sequence of zeros and ones, and starting at the leftmost one, continue moving to the right. Each time another one is encountered, record the number of moves since the previous one was encountered and append that number to the resulting sequence.
For example, given a sequence of
[ 0, 1, 1, 0, 0, 1, 0, 0, 0, 1 0 ], we would find the reduction of[ 1, 3, 4]. From this reduction, we calculate the contiguous subsequence of[ 1, 3, 4], the prefix of[ 1, 3]with sum of4, and the suffix of[ 4 ]with sum of4.This reduction may be computed in
O(n).Unfortunately, I am not sure where to go from here.
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# <algorithm> def contains_evenly_spaced?(input) return false if input.size < 3 one_indices = [] input.each_with_index do |digit, index| next if digit == 0 one_indices << index end return false if one_indices.size < 3 previous_indexes = [] one_indices.each do |index| if !previous_indexes.empty? previous_indexes.each do |previous_index| multiple = index - previous_index success_index = index + multiple return true if input[success_index] == 1 end end previous_indexes << index end return false end # </algorithm> def parse_input(input) input.chars.map { |c| c.to_i } endI'm having trouble with the worst-case scenarios with millions of digits. Fuzzing from
/dev/urandomessentially gives you O(n), but I know the worst case is worse than that. I just can't tell how much worse. For smalln, it's trivial to find inputs at around3*n*log(n), but it's surprisingly hard to differentiate those from some other order of growth for this particular problem.Can anyone who was working on worst-case inputs generate a string with length greater than say, one hundred thousand?
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Revision: 2009-10-17 23:00
I've run this on large numbers (like, strings of 20 million) and I now believe this algorithm is not O(n logn). Notwithstanding that, it's a cool enough implementation and contains a number of optimizations that makes it run really fast. It evaluates all the arrangements of binary strings 24 or fewer digits in under 25 seconds.
I've updated the code to include the
0 <= L < M < U <= X-1observation from earlier today.
Original
This is, in concept, similar to another question I answered. That code also looked at three values in a series and determined if a triplet satisfied a condition. Here is C# code adapted from that:
using System; using System.Collections.Generic; namespace StackOverflow1560523 { class Program { public struct Pair<T> { public T Low, High; } static bool FindCandidate(int candidate, List<int> arr, List<int> pool, Pair<int> pair, ref int iterations) { int lower = pair.Low, upper = pair.High; while ((lower >= 0) && (upper < pool.Count)) { int lowRange = candidate - arr[pool[lower]]; int highRange = arr[pool[upper]] - candidate; iterations++; if (lowRange < highRange) lower -= 1; else if (lowRange > highRange) upper += 1; else return true; } return false; } static List<int> BuildOnesArray(string s) { List<int> arr = new List<int>(); for (int i = 0; i < s.Length; i++) if (s[i] == '1') arr.Add(i); return arr; } static void BuildIndexes(List<int> arr, ref List<int> even, ref List<int> odd, ref List<Pair<int>> evenIndex, ref List<Pair<int>> oddIndex) { for (int i = 0; i < arr.Count; i++) { bool isEven = (arr[i] & 1) == 0; if (isEven) { evenIndex.Add(new Pair<int> {Low=even.Count-1, High=even.Count+1}); oddIndex.Add(new Pair<int> {Low=odd.Count-1, High=odd.Count}); even.Add(i); } else { oddIndex.Add(new Pair<int> {Low=odd.Count-1, High=odd.Count+1}); evenIndex.Add(new Pair<int> {Low=even.Count-1, High=even.Count}); odd.Add(i); } } } static int FindSpacedOnes(string s) { // List of indexes of 1s in the string List<int> arr = BuildOnesArray(s); //if (s.Length < 3) // return 0; // List of indexes to odd indexes in arr List<int> odd = new List<int>(), even = new List<int>(); // evenIndex has indexes into arr to bracket even numbers // oddIndex has indexes into arr to bracket odd numbers List<Pair<int>> evenIndex = new List<Pair<int>>(), oddIndex = new List<Pair<int>>(); BuildIndexes(arr, ref even, ref odd, ref evenIndex, ref oddIndex); int iterations = 0; for (int i = 1; i < arr.Count-1; i++) { int target = arr[i]; bool found = FindCandidate(target, arr, odd, oddIndex[i], ref iterations) || FindCandidate(target, arr, even, evenIndex[i], ref iterations); if (found) return iterations; } return iterations; } static IEnumerable<string> PowerSet(int n) { for (long i = (1L << (n-1)); i < (1L << n); i++) { yield return Convert.ToString(i, 2).PadLeft(n, '0'); } } static void Main(string[] args) { for (int i = 5; i < 64; i++) { int c = 0; string hardest_string = ""; foreach (string s in PowerSet(i)) { int cost = find_spaced_ones(s); if (cost > c) { hardest_string = s; c = cost; Console.Write("{0} {1} {2}\r", i, c, hardest_string); } } Console.WriteLine("{0} {1} {2}", i, c, hardest_string); } } } }The principal differences are:
- Exhaustive search of solutions
This code generates a power set of data to find the hardest input to solve for this algorithm. - All solutions versus hardest to solve
The code for the previous question generated all the solutions using a python generator. This code just displays the hardest for each pattern length. - Scoring algorithm
This code checks the distance from the middle element to its left- and right-hand edge. The python code tested whether a sum was above or below 0. - Convergence on a candidate
The current code works from the middle towards the edge to find a candidate. The code in the previous problem worked from the edges towards the middle. This last change gives a large performance improvement. - Use of even and odd pools
Based on the observations at the end of this write-up, the code searches pairs of even numbers of pairs of odd numbers to find L and U, keeping M fixed. This reduces the number of searches by pre-computing information. Accordingly, the code uses two levels of indirection in the main loop of FindCandidate and requires two calls to FindCandidate for each middle element: once for even numbers and once for odd ones.
The general idea is to work on indexes, not the raw representation of the data. Calculating an array where the 1's appear allows the algorithm to run in time proportional to the number of 1's in the data rather than in time proportional to the length of the data. This is a standard transformation: create a data structure that allows faster operation while keeping the problem equivalent.
The results are out of date: removed.
Edit: 2009-10-16 18:48
On yx's data, which is given some credence in the other responses as representative of hard data to calculate on, I get these results... I removed these. They are out of date.
I would point out that this data is not the hardest for my algorithm, so I think the assumption that yx's fractals are the hardest to solve is mistaken. The worst case for a particular algorithm, I expect, will depend upon the algorithm itself and will not likely be consistent across different algorithms.
Edit: 2009-10-17 13:30
Further observations on this.
First, convert the string of 0's and 1's into an array of indexes for each position of the 1's. Say the length of that array A is X. Then the goal is to find
0 <= L < M < U <= X-1such that
A[M] - A[L] = A[U] - A[M]or
2*A[M] = A[L] + A[U]Since A[L] and A[U] sum to an even number, they can't be (even, odd) or (odd, even). The search for a match could be improved by splitting A[] into odd and even pools and searching for matches on A[M] in the pools of odd and even candidates in turn.
However, this is more of a performance optimization than an algorithmic improvement, I think. The number of comparisons should drop, but the order of the algorithm should be the same.
Edit 2009-10-18 00:45
Yet another optimization occurs to me, in the same vein as separating the candidates into even and odd. Since the three indexes have to add to a multiple of 3 (a, a+x, a+2x -- mod 3 is 0, regardless of a and x), you can separate L, M, and U into their mod 3 values:
M L U 0 0 0 1 2 2 1 1 0 2 1 1 2 0 2 0 1 1 0 2 2In fact, you could combine this with the even/odd observation and separate them into their mod 6 values:
M L U 0 0 0 1 5 2 4 3 3 4 2 5 1and so on. This would provide a further performance optimization but not an algorithmic speedup.
讨论(0) - Exhaustive search of solutions
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Ok, I'm going to take another stab at the problem. I think I can prove a O(n log(n)) algorithm that is similar to those already discussed by using a balanced binary tree to store distances between 1's. This approach was inspired by Justice's observation about reducing the problem to a list of distances between the 1's.
Could we scan the input string to construct a balanced binary tree around the position of 1's such that each node stores the position of the 1 and each edge is labeled with the distance to the adjacent 1 for each child node. For example:
10010001 gives the following tree 3 / \ 2 / \ 3 / \ 0 7This can be done in O(n log(n)) since, for a string of size n, each insertion takes O(log(n)) in the worst case.
Then the problem is to search the tree to discover whether, at any node, there is a path from that node through the left-child that has the same distance as a path through the right child. This can be done recursively on each subtree. When merging two subtrees in the search, we must compare the distances from paths in the left subtree with distances from paths in the right. Since the number of paths in a subtree will be proportional to log(n), and the number of nodes is n, I believe this can be done in O(n log(n)) time.
Did I miss anything?
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