Consider the following problem:
We are given two arrays A and B such that A and B are sorted
except A has B.length additional 0s appended to its end. For instance, A and B could be the following:
A = [2, 4, 6, 7, 0, 0, 0]
B = [1, 7, 9]
Our goal is to create one sorted list by inserting each entry of B
into A in place. For instance, running the algorithm on the above
example would leave
A = [1, 2, 4, 6, 7, 7, 9]
Is there a clever way to do this in better than O(n^2) time? The only way I could think of is to insert each element of B into A by scanning linearly and performing the appropriate number of shifts, but this leads to the O(n^2) solution.
Some pseudo-code (sorta C-ish), assuming array indexing is 0-based:
pA = A + len(A) - 1;
pC = pA; // last element in A
while (! *pA) --pA; // find the last non-zero entry in A
pB = B + len(B) - 1;
while (pA >= A) && (pB >= B)
if *pA > *pB
*pC = *pA; --pA;
else
*pC = *pB; --pB;
--pC
while (pB >= B) // still some bits in B to copy over
*pC = *pB; --pB; --pC;
Not really tested, and just written off the top of my head, but it should give you the idea... May not have the termination and boundary conditions exactly right.
You can do it in O(n).
Work from the end, moving the largest element towards the end of A. This way you avoid a lot of trouble to do with where to keep the elements while iterating. This is pretty easy to implement:
int indexA = A.Length - B.Length - 1;
int indexB = B.Length - 1;
int insertAt = A.Length;
while (indexA > 0 || indexB > 0)
{
insertAt--;
A[insertAt] = max(B[indexB], A[indexA]);
if (A[indexA] <= B[indexB])
indexB--;
else
indexA--;
}
Related
There is an array with indices [[0, n_0], [1, n_1], ..., [n, n_n]]. For each n_i a function is called. It is necessary to reorder the result from the threads by first component after every thread has terminated. As far as I could find a way to do this, I organized that the index is hard-coded by asking if the index is e.g. 0 and then starting the code separately for the hard-coded index 0. So far this a possible way to do it (even though the code looks as if someone didn't understand what a loop is for).
rest = []
tpl.each do |idx, vn|
if idx == 0
pool.post do
res = funk(vn)
p ['idx 0: ', res]
rest += [[0, res]]
end#pool.post
elsif idx == 1
pool.post do
res = funk(vn)
p ['idx 1: ', res]
rest += [[1, res]]
end#pool.post
end;end
But now there is a strange behaviour:
Index 0 and 1 are calculated accurately, but when the result of 1 is added one line later, the result of the former function is added (again).
["idx 1: ", [4]]
["idx 0: ", [16900]]
rest: [[0, [16900]], [1, [16900], ...]
This is not always the case, so it depends on the order of the appearance of the results.
If e.g. the calculation of index 0 is finished after the calculation of index 1, then idx 1 is missing, or wrong. But other cases of confused results also appear: idx 0 before idx 1, but result of idx 0 is the result of idx 1.
?
It looks like if the threads are not really separated. Can that be enforced, or is there a smarter way of keeping indeces?
One option, I found out, is to synchronize the threads, but that would make the algorithm slower again, so a better solution is:
The results don't get mixed up, if the rest-tuple already has the structure to differentiate the results coming in:
rest = [[], []]
tpl.each do |idx, vn|
if idx == 0
pool.post do
res = funk(vn)
p ['idx 0: ', res]
rest[0] << [0, res]
end#pool.post
elsif idx == 1
pool.post do
res = funk(vn)
p ['idx 1: ', res]
rest[1] << [1, res]
end#pool.post
end;end
I need to find the shortest set of paths to connect each element of Set A with at least one element of Set B. Repetitions in A OR B are allowed (but not both), and no element can be left unconnected. Something like this:
I'm representing the elements as integers, so the "cost" of a connection is just the absolute value of the difference. I also have a cost for crossing paths, so if Set A = [60, 64] and Set B = [63, 67], then (60 -> 67) incurs an additional cost. There can be any number of elements in either set.
I've calculated the table of transitions and costs (distances and crossings), but I can't find the algorithm to find the lowest-cost solution. I keep ending up with either too many connections (i.e., repetitions in both A and B) or greedy solutions that omit elements (e.g., when A and B are non-overlapping). I haven't been able to find examples of precisely this kind of problem online, so I hoped someone here might be able to help, or at least point me in the right direction. I'm not a graph theorist (obviously!), and I'm writing in Swift, so code examples in Swift (or pseudocode) would be much appreciated.
UPDATE: The solution offered by #Daniel is almost working, but it does occasionally add unnecessary duplicates. I think this may be something to do with the sorting of the priorityQueue -- the duplicates always involve identical elements with identical costs. My first thought was to add some kind of "positional encoding" (yes, Transformer-speak) to the costs, so that the costs are offset by their positions (though of course, this doesn't guarantee unique costs). I thought I'd post my Swift version here, in case anyone has any ideas:
public static func voiceLeading(from chA: [Int], to chB: [Int]) -> Set<[Int]> {
var result: Set<[Int]> = Set()
let im = intervalMatrix(chA, chB: chB)
if im.count == 0 { return [[0]] }
let vc = voiceCrossingCostsMatrix(chA, chB: chB, cost: 4)
// NOTE: cm contains the weights
let cm = VectorUtils.absoluteAddMatrix(im, toMatrix: vc)
var A_links: [Int:Int] = [:]
var B_links: [Int:Int] = [:]
var priorityQueue: [Entry] = []
for (i, a) in chA.enumerated() {
for (j, b) in chB.enumerated() {
priorityQueue.append(Entry(a: a, b: b, cost: cm[i][j]))
if A_links[a] != nil {
A_links[a]! += 1
} else {
A_links[a] = 1
}
if B_links[b] != nil {
B_links[b]! += 1
} else {
B_links[b] = 1
}
}
}
priorityQueue.sort { $0.cost > $1.cost }
while priorityQueue.count > 0 {
let entry = priorityQueue[0]
if A_links[entry.a]! > 1 && B_links[entry.b]! > 1 {
A_links[entry.a]! -= 1
B_links[entry.b]! -= 1
} else {
result.insert([entry.a, (entry.b - entry.a)])
}
priorityQueue.remove(at: 0)
}
return result
}
Of course, since the duplicates have identical scores, it shouldn't be a problem to just remove the extras, but it feels a bit hackish...
UPDATE 2: Slightly less hackish (but still a bit!); since the requirement is that my result should have equal cardinality to max(|A|, |B|), I can actually just stop adding entries to my result when I've reached the target cardinality. Seems okay...
UPDATE 3: Resurrecting this old question, I've recently had some problems arise from the fact that the above algorithm doesn't fulfill my requirement |S| == max(|A|, |B|) (where S is the set of pairings). If anyone knows of a simple way of ensuring this it would be much appreciated. (I'll obviously be poking away at possible changes.)
This is an easy task:
Add all edges of the graph in a priority_queue, where the biggest priority is the edge with the biggest weight.
Look each edge e = (u, v, w) in the priority_queue, where u is in A, v is in B and w is the weight.
If removing e from the graph doesn't leave u or v isolated, remove it.
Otherwise, e is part of the answer.
This should be enough for your case:
#include <bits/stdc++.h>
using namespace std;
struct edge {
int u, v, w;
edge(){}
edge(int up, int vp, int wp){u = up; v = vp; w = wp;}
void print(){ cout<<"("<<u<<", "<<v<<")"<<endl; }
bool operator<(const edge& rhs) const {return w < rhs.w;}
};
vector<edge> E; //edge set
priority_queue<edge> pq;
vector<edge> ans;
int grade[5] = {3, 3, 2, 2, 2};
int main(){
E.push_back(edge(0, 2, 1)); E.push_back(edge(0, 3, 1)); E.push_back(edge(0, 4, 4));
E.push_back(edge(1, 2, 5)); E.push_back(edge(1, 3, 2)); E.push_back(edge(1, 4, 0));
for(int i = 0; i < E.size(); i++) pq.push(E[i]);
while(!pq.empty()){
edge e = pq.top();
if(grade[e.u] > 1 && grade[e.v] > 1){
grade[e.u]--; grade[e.v]--;
}
else ans.push_back(e);
pq.pop();
}
for(int i = 0; i < ans.size(); i++) ans[i].print();
return 0;
}
Complexity: O(E lg(E)).
I think this problem is "minimum weighted bipartite matching" (although searching for " maximum weighted bipartite matching" would also be relevant, it's just the opposite)
I read this answer and found an implementation of Quicksort here. It's still unclear to me why Quicksort requires O(log n) extra space.
I understand what a call stack is. I applied the implementation stated above to an array of random numbers and saw n - 1 calls of quickSort.
public static void main(String[] args) {
Random random = new Random();
int num = 8;
int[] array = new int[num];
for (int i = 0; i < num; i++) {
array[i] = random.nextInt(100);
}
System.out.println(Arrays.toString(array));
quickSort(array, 0, array.length - 1);
System.out.println(Arrays.toString(array));
}
static int partition(int arr[], int left, int right) {
int i = left, j = right;
int tmp;
int pivot = arr[(left + right) / 2];
while (i <= j) {
while (arr[i] < pivot)
i++;
while (arr[j] > pivot)
j--;
if (i <= j) {
tmp = arr[i];
arr[i] = arr[j];
arr[j] = tmp;
i++;
j--;
}
}
return i;
}
static void quickSort(int arr[], int left, int right) {
System.out.println("quickSort. left = " + left + " right = " + right);
int index = partition(arr, left, right);
if (left < index - 1)
quickSort(arr, left, index - 1);
if (index < right)
quickSort(arr, index, right);
}
The output I saw:
[83, 65, 68, 91, 43, 45, 58, 82]
quickSort. left = 0 right = 7
quickSort. left = 0 right = 6
quickSort. left = 0 right = 4
quickSort. left = 0 right = 3
quickSort. left = 0 right = 2
quickSort. left = 0 right = 1
quickSort. left = 5 right = 6
[43, 45, 58, 65, 68, 82, 83, 91]
It makes that 7 (n -1) calls. So why does quickSort require O(log n) space for its call stack if the number of calls depends on n, not log n?
I think I understand why the stack size of Quicksort is O(n) in the worst case.
One part of the array (suppose left) to be sorted consists of one element, and the other part (right) consists of n - 1 elements. The size of the left part is always 1, and the size of the right part decrements by 1 every time.
Thus, we initially call Quicksort and then call it n - 1 times for the right part recursively. So extra space for the call stack is O(n). And since the partitioning procedure takes O(n) for every recursive call, the time complexity is O(n2).
As for the average case analysis, now I don't know how to prove O(n * log n) for the time complexity and O(log n) for extra space. But I know that if I divide the input array into two almost equal parts, I'll call Quicksort (log n) / 2 times for the left part. And the right part is sorted using tail recursion which doesn't add to the call stack.
https://en.wikipedia.org/wiki/Quicksort
So extra space needed for Quicksort is O (log n) in this case. The constant factor 1/2 is left out.
Since the partitioning routine is n, the time complexity is O(n * log n).
Please correct me if my assumptions are wrong. I'm ready to read and accept your answer.
I've got a list of some integers, e.g. [1, 2, 3, 4, 5, 10]
And I've another integer (N). For example, N = 19.
I want to check if my integer can be represented as a sum of any amount of numbers in my list:
19 = 10 + 5 + 4
or
19 = 10 + 4 + 3 + 2
Every number from the list can be used only once. N can raise up to 2 thousand or more. Size of the list can reach 200 integers.
Is there a good way to solve this problem?
4 years and a half later, this question is answered by Jonathan.
I want to post two implementations (bruteforce and Jonathan's) in Python and their performance comparison.
def check_sum_bruteforce(numbers, n):
# This bruteforce approach can be improved (for some cases) by
# returning True as soon as the needed sum is found;
sums = []
for number in numbers:
for sum_ in sums[:]:
sums.append(sum_ + number)
sums.append(number)
return n in sums
def check_sum_optimized(numbers, n):
sums1, sums2 = [], []
numbers1 = numbers[:len(numbers) // 2]
numbers2 = numbers[len(numbers) // 2:]
for sums, numbers_ in ((sums1, numbers1), (sums2, numbers2)):
for number in numbers_:
for sum_ in sums[:]:
sums.append(sum_ + number)
sums.append(number)
for sum_ in sums1:
if n - sum_ in sums2:
return True
return False
assert check_sum_bruteforce([1, 2, 3, 4, 5, 10], 19)
assert check_sum_optimized([1, 2, 3, 4, 5, 10], 19)
import timeit
print(
"Bruteforce approach (10000 times):",
timeit.timeit(
'check_sum_bruteforce([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 200)',
number=10000,
globals=globals()
)
)
print(
"Optimized approach by Jonathan (10000 times):",
timeit.timeit(
'check_sum_optimized([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 200)',
number=10000,
globals=globals()
)
)
Output (the float numbers are seconds):
Bruteforce approach (10000 times): 1.830944365834205
Optimized approach by Jonathan (10000 times): 0.34162875449254027
The brute force approach requires generating 2^(array_size)-1 subsets to be summed and compared against target N.
The run time can be dramatically improved by simply splitting the problem in two. Store, in sets, all of the possible sums for one half of the array and the other half separately. It can now be determined by checking for every number n in one set if the complementN-n exists in the other set.
This optimization brings the complexity down to approximately: 2^(array_size/2)-1+2^(array_size/2)-1=2^(array_size/2 + 1)-2
Half of the original.
Here is a c++ implementation using this idea.
#include <bits/stdc++.h>
using namespace std;
bool sum_search(vector<int> myarray, int N) {
//values for splitting the array in two
int right=myarray.size()-1,middle=(myarray.size()-1)/2;
set<int> all_possible_sums1,all_possible_sums2;
//iterate over the first half of the array
for(int i=0;i<middle;i++) {
//buffer set that will hold new possible sums
set<int> buffer_set;
//every value currently in the set is used to make new possible sums
for(set<int>::iterator set_iterator=all_possible_sums1.begin();set_iterator!=all_possible_sums1.end();set_iterator++)
buffer_set.insert(myarray[i]+*set_iterator);
all_possible_sums1.insert(myarray[i]);
//transfer buffer into the main set
for(set<int>::iterator set_iterator=buffer_set.begin();set_iterator!=buffer_set.end();set_iterator++)
all_possible_sums1.insert(*set_iterator);
}
//iterator over the second half of the array
for(int i=middle;i<right+1;i++) {
set<int> buffer_set;
for(set<int>::iterator set_iterator=all_possible_sums2.begin();set_iterator!=all_possible_sums2.end();set_iterator++)
buffer_set.insert(myarray[i]+*set_iterator);
all_possible_sums2.insert(myarray[i]);
for(set<int>::iterator set_iterator=buffer_set.begin();set_iterator!=buffer_set.end();set_iterator++)
all_possible_sums2.insert(*set_iterator);
}
//for every element in the first set, check if the the second set has the complemenent to make N
for(set<int>::iterator set_iterator=all_possible_sums1.begin();set_iterator!=all_possible_sums1.end();set_iterator++)
if(all_possible_sums2.find(N-*set_iterator)!=all_possible_sums2.end())
return true;
return false;
}
Ugly and brute force approach:
a = [1, 2, 3, 4, 5, 10]
b = []
a.size.times do |c|
b << a.combination(c).select{|d| d.reduce(&:+) == 19 }
end
puts b.flatten(1).inspect
probably a simple question but I seem to be suffering from programmer's block. :)
I have three boolean values: A, B, and C. I would like to save the state combination as an unsigned tinyint (max 255) into a database and be able to derive the states from the saved integer.
Even though there are only a limited number of combinations, I would like to avoid hard-coding each state combination to a specific value (something like if A=true and B=true has the value 1).
I tried to assign values to the variables so (A=1, B=2, C=3) and then adding, but I can't differentiate between A and B being true from i.e. only C being true.
I am stumped but pretty sure that it is possible.
Thanks
Binary maths I think. Choose a location that's a power of 2 (1, 2, 4, 8 etch) then you can use the 'bitwise and' operator & to determine the value.
Say A = 1, B = 2 , C= 4
00000111 => A B and C => 7
00000101 => A and C => 5
00000100 => C => 4
then to determine them :
if( val & 4 ) // same as if (C)
if( val & 2 ) // same as if (B)
if( val & 1 ) // same as if (A)
if((val & 4) && (val & 2) ) // same as if (C and B)
No need for a state table.
Edit: to reflect comment
If the tinyint has a maximum value of 255 => you have 8 bits to play with and can store 8 boolean values in there
binary math as others have said
encoding:
myTinyInt = A*1 + B*2 + C*4 (assuming you convert A,B,C to 0 or 1 beforehand)
decoding
bool A = myTinyInt & 1 != 0 (& is the bitwise and operator in many languages)
bool B = myTinyInt & 2 != 0
bool C = myTinyInt & 4 != 0
I'll add that you should find a way to not use magic numbers. You can build masks into constants using the Left Logical/Bit Shift with a constant bit position that is the position of the flag of interest in the bit field. (Wow... that makes almost no sense.) An example in C++ would be:
enum Flags {
kBitMask_A = (1 << 0),
kBitMask_B = (1 << 1),
kBitMask_C = (1 << 2),
};
uint8_t byte = 0; // byte = 0b00000000
byte |= kBitMask_A; // Set A, byte = 0b00000001
byte |= kBitMask_C; // Set C, byte = 0b00000101
if (byte & kBitMask_A) { // Test A, (0b00000101 & 0b00000001) = T
byte &= ~kBitMask_A; // Clear A, byte = 0b00000100
}
In any case, I would recommend looking for Bitset support in your favorite programming language. Many languages will abstract the logical operations away behind normal arithmetic or "test/set" operations.
Need to use binary...
A = 1,
B = 2,
C = 4,
D = 8,
E = 16,
F = 32,
G = 64,
H = 128
This means A + B = 3 but C = 4. You'll never have two conflicting values. I've listed the maximum you can have for a single byte, 8 values or (bits).