We have a number of N digits (it can start with 0). We must find the biggest number which can be obtained cutting K disjoint sequences of length L.
N can be very big so our number should be stored as a string.
Example 1)
nr = 12122212212212121222
K = 2, L = 3
answer: 22212212221222
We can cut "121" (from 0th digit) and "121" (from 12th digit).
Example 2)
nr = 0739276145
K = 3, L = 3
answer: 9
We can cut "073", "276" and "145".
I have tried something like this:
void cut(string str, int K, int L) {
if (K == 0)
return;
// here we cut a single sequence of length L
// in a way that the new number is the biggest
cut(str, K - 1, L);
}
But in this way, I can cut 2 sequences which in the initial number are not disjoint, so my method it's not correct. Please help me solve the problem!
You can define cutsrecursively:
cuts(s, 0, L) = s
cuts(s, K, L) = max(s[i:j] + cuts(s[j+L:], K-1, L) for j=i..len(s)-K*L)
As is normal in these problems, you can use dynamic programming to avoid an exponential runtime. You can probably avoid so much string slicing and appending, but this is an example solution in Python:
def cuts(s, K, L):
dp = [s[i:] for i in xrange(len(s)+1)]
for k in xrange(1, K+1):
dp = [max(s[i:j] + dp[j+L] for j in xrange(i, len(dp)-L))
for i in xrange(len(dp)-L)]
return dp[0]
print cuts('12122212212212121222', 2, 3)
print cuts('0739276145', 3, 3)
Output:
22212212221222
9
Related
Given a string S of length n. Choose an integer K and two non-empty sub-sequences A and B of length K such that it satisfies the following conditions:
A = B i.e. for each i the ith character in A is same as the ith character in B.
Let's denote the indices used to construct A as a1,a2,a3,...,an where ai belongs to S and B as b1,b2,b3,...,bn where bi belongs to S. If we denote the number of common indices in A and B by M then M + 1 <= K.
Find the maximum value of K such that it is possible to find the sub-sequences A and B which satisfies the above conditions.
Constraints:
0 < N <= 10^5
Things which I observed are:
The value of K = 0 if the number of characters in the given string are all distinct i.e S = abcd.
K = length of S - 1 if all the characters in the string are same i.e. S = aaaa.
The value of M cannot be equal to K because then M + 1 <= K will not be true i.e you cannot have a sub-sequence A and B that satifies A = B and a1 = b1, a2 = b2, a3 = b3, ..., an = bn.
If the string S is palindrome then K = (Total number of times a character is repeated in the string if the repeatation count > 1) - 1. i.e. S = tenet then t is repeated 2 times, e is repeated 2 times, Total number of times a character is repeated = 4, K = 4 - 1 = 3.
I am having trouble designing the algorithm to solve the above problem.
Let me know in the comments if you need more clarification.
(Update: see O(n) answer.)
We can modify the classic longest common subsequence recurrence to take an extra parameter.
JavaScript code (not memoised) that I hope is self explanatory:
function f(s, i, j, haveUncommon){
if (i < 0 || j < 0)
return haveUncommon ? 0 : -Infinity
if (s[i] == s[j]){
if (haveUncommon){
return 1 + f(s, i-1, j-1, true)
} else if (i == j){
return Math.max(
1 + f(s, i-1, j-1, false),
f(s, i-1, j, false),
f(s, i, j-1, false)
)
} else {
return 1 + f(s, i-1, j-1, true)
}
}
return Math.max(
f(s, i-1, j, haveUncommon),
f(s, i, j-1, haveUncommon)
)
}
var s = "aabcde"
console.log(f(s, s.length-1, s.length-1, false))
I believe we are just looking for the closest equal pair of characters since the only characters excluded from A and B would be one of the characters in the pair and any characters in between.
Here's O(n) in JavaScript:
function f(s){
let map = {}
let best = -1
for (let i=0; i<s.length; i++){
if (!map.hasOwnProperty(s[i])){
map[s[i]] = i
continue
}
best = Math.max(best, s.length - i + map[s[i]])
map[s[i]] = i
}
return best
}
var strs = [
"aabcde", // 5
"aaababcd", // 7
"aebgaseb", // 4
"aefttfea",
// aeft fea
"abcddbca",
// abcd bca,
"a" // -1
]
for (let s of strs)
console.log(`${ s }: ${ f(s) }`)
O(n) solution in Python3:
def compute_maximum_k(word):
last_occurences = {}
max_k = -1
for i in range(len(word)):
if(not last_occurences or not word[i] in last_occurences):
last_occurences[word[i]] = i
continue
max_k = max(max_k,(len(word) - i) + last_occurences[word[i]])
last_occurences[word[i]] = i
return max_k
def main():
words = ["aabcde","aaababcd","aebgaseb","aefttfea","abcddbca","a","acbdaadbca"]
for word in words:
print(compute_maximum_k(word))
if __name__ == "__main__":
main()
A solution for the maximum length substring would be the following:
After building a Suffix Array you can derive the LCP Array. The maximum value in the LCP array corresponds to the K you are looking for. The overall complexity of both constructions is O(n).
A suffix array will sort all prefixes in you string S in ascending order. The longest common prefix array then computes the lengths of the longest common prefixes (LCPs) between all pairs of consecutive suffixes in the sorted suffix array. Thus the maximum value in this array corresponds to the length of the two maximum length substrings of S.
For a nice example using the word "banana", check out the LCP Array Wikipage
I deleted my previous answer as I don't think we need an LCS-like solution (LCS=longest Common Subsequence).
It is sufficient to find the couple of subsequences (A, B) that differ in one character and share all the others.
The code below finds the solution in O(N) time.
def function(word):
dp = [0]*len(word)
lastOccurences = {}
for i in range(len(dp)-1, -1, -1):
if i == len(dp)-1:
dp[i] = 0
else:
if dp[i+1] > 0:
dp[i] = 1 + dp[i+1]
elif word[i] in lastOccurences:
dp[i] = len(word)-lastOccurences[word[i]]
lastOccurences[word[i]] = i
return dp[0]
dp[i] is equal to 0 when all characters from i to the end of the string are different.
I will explain my code by an example.
For "abcack", there are two cases:
Either the first 'a' will be shared by the two subsequences A and B, in this case the solution will be = 1 + function("bcack")
Or 'a' will not be shared between A and B. In this case the result will be 1 + "ck". Why 1 + "ck" ? It's because we have already satisfied M+1<=K so just add all the remaining characters. In terms of indices, the substrings are [0, 4, 5] and [3, 4, 5].
We take the maximum between these two cases.
The reason I'm scanning right to left is to not have O(N) search for the current character in the rest of the string, I maintain the index of the last visited occurence of the character in the dict lastOccurences.
Given length of a string L and N - number of palindromes, build a string with exactly N palindromic substrings in it. For example,
L = 4
N = 2
S = 'aabb' or 'abba'
L = 4
N = 3,4,5
S = impossible
L = 4
N = 6
S = 'aaaa' (palindromes are substrings S[0:2], S[2:4], S[1:3], S[0:3], S[1:4], S[0:4])
UPDATE: all target palindromes should be of length > 1
You can introduce a variable s_i = {0, 1} for each character in the string S, then if a substring S[a..b] is a palindrome, it must be that
(s_a = s_b) and (s_{a+1} = s_{b-1}) and ...
so for each substring you have a clause and exactly N of them must be satisfied. This reduces the problem to satisfiability.
I would also be curious if you can solve it as an optimization problem:
for each substring introduce a variable x_i = {0, 1} that stands for the fact that substring number i is a palindrome (let it be S[a..b]). Then introduce a variable y_i for the clause of that substring:
(s_a-s_b)^2 + (s_{a+1}-s_{b-1})^2 + ...
Then you need to satisfy that \sum_{x_i} = N and minimize \sum_{x_i * y_i}. Obviously the minimum is 0 if a solution exists and the objective is always non-negative.
Edit
the optimisation idea seems to be false, since you need to enforce that if y_i = 0, then it must be that x_i = 0, but satisfiability formulation should work
You are given a string and can change at most Q letters in the string. You are also given a list of substrings (each two characters long), with a corresponding score. Each occurance of the substring within the string adds to your total score. What is the maximum possible attainable score?
String length <= 150, Q <= 100, Number of Substrings <= 700
Example:
String = bpdcg
Q = 2
Substrings:
bz - score: 2
zd - score: 5
dm - score: 7
ng - score: 10
In this example, you can achieve the maximum score b changing the "p" in the string to a "z" and the "c" to an "n". Thus, your new string is "bzdng" which has a score of 2+5+10 = 17.
I know that given a string which already has the letters changed, the score can be checked in linear time using a dictionary matching algorithm such as aho-corasick (or with a slightly worse complexity, Rabin Karp). However, trying each two letter substitution will take too long and then checking will take too long.
Another possible method I thought was to work backwards, to construct the ideal string from the given substrings and then check whether it differs by at most two characters from the original string. However, I am not sure how to do this, and even if it could be done, I think that it would also take too long.
What is the best way to go about this?
An efficient way to solve this is to use dynamic programming.
Let L be the set of letters that start any of the length-2 scoring substrings, and a special letter "*" which stands for any other letter than these.
Let S(i, j, c) be the maximum score possible in the string (up to index i) using j substitutions, where the string ends with character c (where c in L).
The recurrence relations are a bit messy (or at least, I didn't find a particularly beautiful formulation of them), but here's some code that computes the largest score possible:
infinity = 100000000
def S1(L1, L2, s, i, j, c, scores, cache):
key = (i, j, c)
if key not in cache:
if i == 0:
if c != '*' and s[0] != c:
v = 0 if j >= 1 else -infinity
else:
v = 0 if j >= 0 else -infinity
else:
v = -infinity
for d in L1:
for c2 in [c] if c != '*' else L2 + s[i]:
jdiff = 1 if s[i] != c2 else 0
score = S1(L1, L2, s, i-1, j-jdiff, d, scores, cache)
score += scores.get(d+c2 , 0)
v = max(v, score)
cache[key] = v
return cache[key]
def S(s, Q, scores):
L1 = ''.join(sorted(set(w[0] for w in scores))) + '*'
L2 = ''.join(sorted(set(w[1] for w in scores)))
return S1(L1, L2, s + '.', len(s), Q, '.', scores, {})
print S('bpdcg', 2, {'bz': 2, 'zd': 5, 'dm': 7, 'ng': 10})
There's some room for optimisation:
the computation isn't terminated early if j goes negative
when given a choice, every value of L2 is tried, whereas only letters that can complete a scoring word from d need trying.
Overall, if there's k different letters in the scoring words, the algorithm runs in time O(QN*k^2). With the second optimisation above, this can be reduced to O(QNw) where w is the number of scoring words.
Consider the following function on a string:
int F(string S)
{
int N = S.size();
int T = 0;
for (int i = 0; i < N; i++)
for (int j = i + 1; j < N; j++)
if (S[i] > S[j])
T++;
return T;
}
A string S0 of length N with all pairwise distinct characters has a total of N! unique permutations.
For example "bac" has the following 6 permutations:
bac
abc
cba
bca
acb
cab
Consider these N! strings in lexicographical order:
abc
acb
bac
bca
cab
cba
Now consider the application of F to each of these strings:
F("abc") = 0
F("acb") = 1
F("bac") = 1
F("bca") = 2
F("cab") = 2
F("cba") = 3
Given some string S1 of this set of permutations, we want to find the next string S2 in the set, that has the following relationship to S1:
F(S2) == F(S1) + 1
For example if S1 == "acb" (F = 1) than S2 == "bca" (F = 1 + 1 = 2)
One way to do this would be to start at one past S1 and iterate through the list of permutations looking for F(S) = F(S1)+1. This is unfortunately O(N!).
By what O(N) function on S1 can we calculate S2 directly?
Suppose length of S1 is n, biggest value for F(S1) is n(n-1)/2, if F(S1) = n(n-1)/2, means it's a last function and there isn't any next for it, but if F(S1) < n(n-1)/2, means there is at least one char x which is bigger than char y and x is next to y, find such a x with lowest index, and change x and y places. let see it by example:
S1 == "acb" (F = 1) , 1 < 3 so there is a char x which is bigger than another char y and its index is bigger than y, here smallest index x is c, and by first try you will replace it with a (which is smaller than x so algorithm finishes here)==> S2= "cab", F(S2) = 2.
Now let test it with S2, cab: x=b, y=a, ==> S3 = "cba".\
finding x is not hard, iterate the input, and have a variable name it min, while current visited character is smaller than min, set min as newly visited char, and visit next character, first time you visit a character which is bigger than min stop iteration, this is x:
This is pseudocode in c# (but I wasn't careful about boundaries e.g in input.Substring):
string NextString(string input)
{
var min = input[0];
int i=1;
while (i < input.Length && input[i] < min)
{
min = input[i];
i++;
}
if (i == input.Length) return "There isn't next item";
var x = input[i], y=input[i-1];
return input.Substring(0,i-2) + x + y + input.Substring(i,input.Length - 1 - i);
}
Here's the outline of an algorithm for a solution to your problem.
I'll assume that you have a function to directly return the n-th permutation (given n) and its inverse, ie a function to return n given a permutation. Let these be perm(n) and perm'(n) respectively.
If I've figured it correctly, when you have a 4-letter string to permute the function F goes like this:
F("abcd") = 0
F("abdc") = 1
F(perm(3)) = 1
F(...) = 2
F(...) = 2
F(...) = 3
F(perm(7)) = 1
F(...) = 2
F(...) = 2
F(...) = 3
F(...) = 3
F(...) = 4
F(perm(13)) = 2
F(...) = 3
F(...) = 3
F(...) = 4
F(...) = 4
F(...) = 5
F(perm(19)) = 3
F(...) = 4
F(...) = 4
F(...) = 5
F(...) = 5
F(perm(24)) = 6
In words, when you go from 3 letters to 4 you get 4 copies of the table of values of F, adding (0,1,2,3) to the (1st,2nd,3rd,4th) copy respectively. In the 2nd case, for example, you already have one derangement by putting the 2nd letter in the 1st place; this simply gets added to the other derangements in the same pattern as would be true for the original 3-letter strings.
From this outline it shouldn't be too difficult (but I haven't got time right now) to write the function F. Strictly speaking the inverse of F isn't a function as it would be multi-valued, but given n, and F(n) there are only a few cases for finding m st F(m)==F(n)+1. These cases are:
n == N! where N is the number of letters in the string, there is no next permutation;
F(n+1) < F(n), the sought-for solution is perm(n+(N-1)!), ;
F(n+1) == F(n), the solution is perm(n+2);
F(n+1) > F(n), the solution is perm(n+1).
I suspect that some of this might only work for 4 letter strings, that some of these terms will have to be adjusted for K-letter permutations.
This is not O(n), but it is at least O(n²) (where n is the number of elements in the permutation, in your example 3).
First, notice that whenever you place a character in your string, you already know how much of an increase in F that's going to mean -- it's however many characters smaller than that one that haven't been added to the string yet.
This gives us another algorithm to calculate F(n):
used = set()
def get_inversions(S1):
inv = 0
for index, ch in enumerate(S1):
character = ord(ch)-ord('a')
cnt = sum(1 for x in range(character) if x not in used)
inv += cnt
used.add(character)
return inv
This is not much better than the original version, but it is useful when inverting F. You want to know the first string that is lexicographically smaller -- therefore, it makes sense to copy your original string and only change it whenever mandatory. When such changes are required, we should also change the string by the least amount possible.
To do so, let's use the information that the biggest value of F for a string with n letters is n(n-1)/2. Whenever the number of required inversions would be bigger than this amount if we didn't change the original string, this means we must swap a letter at that point. Code in Python:
used = set()
def get_inversions(S1):
inv = 0
for index, ch in enumerate(S1):
character = ord(ch)-ord('a')
cnt = sum(1 for x in range(character) if x not in used)
inv += cnt
used.add(character)
return inv
def f_recursive(n, S1, inv, ign):
if n == 0: return ""
delta = inv - (n-1)*(n-2)/2
if ign:
cnt = 0
ch = 0
else:
ch = ord(S1[len(S1)-n])-ord('a')
cnt = sum(1 for x in range(ch) if x not in used)
for letter in range(ch, len(S1)):
if letter not in used:
if cnt < delta:
cnt += 1
continue
used.add(letter)
if letter != ch: ign = True
return chr(letter+ord('a'))+f_recursive(n-1, S1, inv-cnt, ign)
def F_inv(S1):
used.clear()
inv = get_inversions(S1)
used.clear()
return f_recursive(len(S1), S1, inv+1, False)
print F_inv("acb")
It can also be made to run in O(n log n) by replacing the innermost loop with a data structure such as a binary indexed tree.
Did you try to swap two neighbor characters in the string? It seems that it can help to solve the problem. If you swap S[i] and S[j], where i < j and S[i] < S[j], then F(S) increases by one, because all other pairs of indices are not affected by this permutation.
If I'm not mistaken, F calculates the number of inversions of the permutation.
I am given a bag B (multiset) of characters with the size m and a string text S of size n. Is it possible to find all substrings that can be created by B (4!=24 combinations) in S in linear time O(n)?
Example:
S = abdcdbcdadcdcbbcadc (n=19)
B = {b, c, c, d} (m=4)
Result: {cdbc (Position 3), cdcb (Position 10)}
The fastest solution I found is to keep a counter for each character and compare it with the Bag in each step, thus the runtime is O(n*m). Algorithm can be shown if needed.
There is a way to do it in O(n), assuming we're only interested in substrings of length m (otherwise it's impossible, because for the bag that has all characters in the string, you'd have to return all substrings of s, which means a O(n^2) result that can't be computed in O(n)).
The algorithm is as follows:
Convert the bag to a histogram:
hist = []
for c in B do:
hist[c] = hist[c] + 1
Initialize a running histogram that we're going to modify (histrunsum is the total count of characters in histrun):
histrun = []
histrunsum = 0
We need two operations: add a character to the histogram and remove it. They operate as follows:
add(c):
if hist[c] > 0 and histrun[c] < hist[c] then:
histrun[c] = histrun[c] + 1
histrunsum = histrunsum + 1
remove(c):
if histrun[c] > 0 then:
histrun[c] = histrun[c] - 1
histrunsum = histrunsum + 1
Essentially, histrun captures the amount of characters that are present in B in current substring. If histrun is equal to hist, our substring has the same characters as B. histrun is equal to hist iff histrunsum is equal to length of B.
Now add first m characters to histrun; if histrunsum is equal to length of B; emit first substring; now, until we reach the end of string, remove the first character of the current substring and add the next character.
add, remove are O(1) since hist and histrun are arrays; checking if hist is equal to histrun is done by comparing histrunsum to length(B), so it's also O(1). Loop iteration count is O(n), the resulting running time is O(n).
Thanks for the answer. The add() and remove() methods have to be changed to make the algorithm work correctly.
add(c):
if hist[c] > 0 and histrun[c] < hist[c] then
histrunsum++
else
histrunsum--
histrun[c] = histrun[c] + 1
remove(c):
if histrun[c] > hist[c] then
histrunsum++
else
histrunsum--
histrun[c] = histrun[c] - 1
Explanation:
histrunsum can be seen as a score of how identical both multisets are.
add(c): when there are less occurrences of a char in the histrun multiset than in the hist multiset, the additional occurrence of that char has to be "rewarded" since the histrun multiset is getting closer to the hist multiset. If there are at least equal or more chars in the histrun set already, and additional char is negative.
remove(c): like add(c), where a removal of a char is weighted positively when it's number in the histrun multiset > hist multiset.
Sample Code (PHP):
function multisetSubstrings($sequence, $mset)
{
$multiSet = array();
$substringLength = 0;
foreach ($mset as $char)
{
$multiSet[$char]++;
$substringLength++;
}
$sum = 0;
$currentSet = array();
$result = array();
for ($i=0;$i<strlen($sequence);$i++)
{
if ($i>=$substringLength)
{
$c = $sequence[$i-$substringLength];
if ($currentSet[$c] > $multiSet[$c])
$sum++;
else
$sum--;
$currentSet[$c]--;
}
$c = $sequence[$i];
if ($currentSet[$c] < $multiSet[$c])
$sum++;
else
$sum--;
$currentSet[$c]++;
echo $sum."<br>";
if ($sum==$substringLength)
$result[] = $i+1-$substringLength;
}
return $result;
}
Use hashing. For each character in the multiset, assign a UNIQUE prime number. Compute the hash for any string by multiplying the prime number associated with a number, as many times as the frequency of that number.
Example : CATTA. Let C = 2, A=3, T = 5. Hash = 2*3*5*5*3 = 450
Hash the multiset ( treat it as a string ). Now go through the input string, and compute the hash of each substring of length k ( where k is the number of characters in the multiset ). Check if this hash matches the multiset hash. If yes, then it is one such occurence.
The hashes can be computed very easily in linear time as follows :
Let multiset = { A, A, B, C }, A=2, B=3, C=5.
Multiset hash = 2*2*3*5 = 60
Let text = CABBAACCA
(i) CABB = 5*2*3*3 = 90
(ii) Now, the next letter is A, and the letter discarded is the first one, C. So the new hash = ( 90/5 )*2 = 36
(iii) Now, A is discarded, and A is also added, so new hash = ( 36/2 ) * 2= 36
(iv) Now B is discarded, and C is added, so hash = ( 36/3 ) * 5 = 60 = multiset hash. Thus we have found one such required occurence - BAAC
This procedure will obviously take O( n ) time.