Related
I did search and looked at these below links but it didn't help .
Point covering problem
Segments poked (covered) with points - any tricky test cases?
Need effective greedy for covering a line segment
Problem Description:
You are given a set of segments on a line and your goal is to mark as
few points on a line as possible so that each segment contains at least
one marked point
Task.
Given a set of n segments {[a0,b0],[a1,b1]....[an-1,bn-1]} with integer
coordinates on a line, find the minimum number 'm' of points such that
each segment contains at least one point .That is, find a set of
integers X of the minimum size such that for any segment [ai,bi] there
is a point x belongs X such that ai <= x <= bi
Output Description:
Output the minimum number m of points on the first line and the integer
coordinates of m points (separated by spaces) on the second line
Sample Input - I
3
1 3
2 5
3 6
Output - I
1
3
Sample Input - II
4
4 7
1 3
2 5
5 6
Output - II
2
3 6
I didn't understand the question itself. I need the explanation, on how to solve this above problem, but i don't want the code. Examples would be greatly helpful
Maybe this formulation of the problem will be easier to understand. You have n people who can each tolerate a different range of temperatures [ai, bi]. You want to find the minimum number of rooms to make them all happy, i.e. you can set each room to a certain temperature so that each person can find a room within his/her temperature range.
As for how to solve the problem, you said you didn't want code, so I'll just roughly describe an approach. Think about the coldest room you have. If making it one degree warmer won't cause anyone to no longer be able to tolerate that room, you might as well make the increase, since that can only allow more people to use that room. So the first temperature you should set is the warmest one that the most cold-loving person can still tolerate. In other words, it should be the smallest of the bi. Now this room will satisfy some subset of your people, so you can remove them from consideration. Then repeat the process on the remaining people.
Now, to implement this efficiently, you might not want to literally do what I said above. I suggest sorting the people according to bi first, and for the ith person, try to use an existing room to satisfy them. If you can't, try to create a new one with the highest temperature possible to satisfy them, which is bi.
Yes the description is pretty vague and the only meaning that makes sense to me is this:
You got some line
Segment on a line is defined by l,r
Where one parameter is distance from start of line and second is the segments length. Which one is which is hard to tell as the letters are not very usual for such description. My bet is:
l length of segment
r distance of (start?) of segment from start of line
You want to find min set of points
So that each segment has at least one point in it. That mean for 2 overlapped segments you need just one point ...
Surely there are more option how to solve this, the obvious is genere & test with some heuristics like genere combinations only for segments that are overlapped more then once. So I would attack this task in this manner (using assumed terminology from #2):
sort segments by r
add number of overlaps to your segment set data
so the segment will be { r,l,n } and set the n=0 for all segments for now.
scan segments for overlaps
something like
for (i=0;i<segments;i++) // loop all segments
for (j=i+1;j<segments;j++) // loop all latter segments until they are still overlapped
if ( segment[i] and segment [j] are overlapped )
{
segment[i].n++; // update overlap counters
segment[j].n++;
}
else break;
Now if the r-sorted segments are overlapped then
segment[i].r <=segment[j].r
segment[i].r+segment[i].l>=segment[j].r
scan segments handling non overlapped segments
for each segment such that segment[i].n==0 add to the solution point list its point (middle) defined by distance from start of line.
points.add(segment[i].r+0.5*segment[i].l);
And after that remove segment from the list (or tag it as used or what ever you do for speed boost...).
scan segments that are overlapped just once
So if segment[i].n==1 then you need to determine if it is overlapped with i-1 or i+1. So add the mid point of the overlap to the solution points and remove i segment from list. Then decrement the n of the overlapped segment (i+1 or i-1)` and if zero remove it too.
points.add(0.5*( segment[j].r + min(segment[i].r+segment[i].l , segment[j].r+segment[j].l )));
Loop this whole scanning until there is no new point added to the solution.
now you got only multiple overlaps left
From this point I will be a bit vague for 2 reasons:
I do not have this tested and I d not have any test data to validate not to mention I am lazy.
This smells like assignment so there is some work/fun left for you.
From start I would scann all segments and remove all of them which got any point from the solution inside. This step you should perform after any changes in the solution.
Now you can experiment with generating combination of points for each overlapped group of segments and remember the minimal number of points covering all segments in group. (simply by brute force).
There are more heuristics possible like handling all twice overlapped segments (in similar manner as the single overlaps) but in the end you will have to do brute force on the rest of data ...
[edit1] as you added new info
The r,l means distance of left and right from the start of line. So if you want to convert between the other formulation { r',l' } and (l<=r) then
l=r`
r=r`+l`
and back
r`=l
l`=r-l`
Sorry too lazy to rewrite the whole thing ...
Here is the working solution in C, please refer to it partially and try to fix your code before reading the whole. Happy coding :) Spoiler alert
#include <stdio.h>
#include <stdlib.h>
int cmp_func(const void *ptr_a, const void *ptr_b)
{
const long *a = *(double **)ptr_a;
const long *b = *(double **)ptr_b;
if (a[1] == b[1])
return a[0] - b[0];
return a[1] - b[1];
}
int main()
{
int i, j, n, num_val;
long **arr;
scanf("%d", &n);
long values[n];
arr = malloc(n * sizeof(long *));
for (i = 0; i < n; ++i) {
*(arr + i) = malloc(2 * sizeof(long));
scanf("%ld %ld", &arr[i][0], &arr[i][1]);
}
qsort(arr, n, sizeof(long *), cmp_func);
i = j = 0;
num_val = 0;
while (i < n) {
int skip = 0;
values[num_val] = arr[i][1];
for (j = i + 1; j < n; ++j) {
int condition;
condition = arr[i][1] <= arr[j][1] ? arr[j][0] <= arr[i][1] : 0;
if (condition) {
skip++;
} else {
break;
}
}
num_val++;
i += skip + 1;
}
printf("%d\n", num_val);
for (int k = 0; k < num_val; ++k) {
printf("%ld ", values[k]);
}
free(arr);
return 0;
}
Here's the working code in C++ for anyone searching :)
#include <bits/stdc++.h>
#define ll long long
#define double long double
#define vi vector<int>
#define endl "\n"
#define ff first
#define ss second
#define pb push_back
#define all(x) (x).begin(),(x).end()
#define mp make_pair
using namespace std;
bool cmp(const pair<ll,ll> &a, const pair<ll,ll> &b)
{
return (a.second < b.second);
}
vector<ll> MinSig(vector<pair<ll,ll>>&vec)
{
vector<ll> points;
for(int x=0;x<vec.size()-1;)
{
bool found=false;
points.pb(vec[x].ss);
for(int y=x+1;y<vec.size();y++)
{
if(vec[y].ff>vec[x].ss)
{
x=y;
found=true;
break;
}
}
if(!found)
break;
}
return points;
}
int main()
{
ios_base::sync_with_stdio(false);
cin.tie(NULL);
int n;
cin>>n;
vector<pair<ll,ll>>v;
for(int x=0;x<n;x++)
{
ll temp1,temp2;
cin>>temp1>>temp2;
v.pb(mp(temp1,temp2));
}
sort(v.begin(),v.end(),cmp);
vector<ll>res=MinSig(v);
cout<<res.size()<<endl;
for(auto it:res)
cout<<it<<" ";
}
I should resolve 16-Queens Problem in 1 second.
I used backtracking algorithm like below.
This code is enough to resolve N-Queens Problem in 1 second when the N is smaller than 13.
But it takes long time if N is bigger than 13.
How can I improve it?
#include <stdio.h>
#include <stdlib.h>
int n;
int arr[100]={0,};
int solution_count = 0;
int check(int i)
{
int k=1, ret=1;
while (k < i && ret == 1) {
if (arr[i] == arr[k] ||
abs(arr[i]-arr[k]) == abs(i-k))
ret = 0;
k++;
}
return ret;
}
void backtrack(int i)
{
if(check(i)) {
if(i == n) {
solution_count++;
} else {
for(int j=1; j<=n; j++) {
arr[i+1] = j;
backtrack(i+1);
}
}
}
}
int main()
{
scanf("%d", &n);
backtrack(0);
printf("%d", solution_count);
}
Your algorithm is almost fine. A small change will probably give you enough time improvement to produce a solution much faster. In addition, there is a data structure change that should let you reduce the time even further.
First, tweak the algorithm a little: rather than waiting for the check all the way till you place all N queens, check early: every time you are about to place a new queen, check if another queen is occupying the same column or the same diagonal before making the arr[i+1] = j; assignment. This will save you a lot of CPU cycles.
Now you need to speed up checking of the next queen. In order to do that you have to change your data structure so that you could do all your checks without any loops. Here is how to do it:
You have N rows
You have N columns
You have 2N-1 ascending diagonals
You have 2N-1 descending diagonals
Since no two queens can take the same spot in any of the four "dimensions" above, you need an array of boolean values for the last three things; the rows are guaranteed to be different, because the i parameter of backtrack, which represents the row, is guaranteed to be different.
With N up to 16, 2N-1 goes up to 31, so you can use uint32_t for your bit arrays. Now you can check if a column c is taken by applying bitwise and & to the columns bit mask and 1 << c. Same goes for the diagonal bit masks.
Note: Doing a 16 Queen problem in under a second would be rather tricky. A very highly optimized program does it in 23 seconds on an 800 MHz PC. A 3.2 GHz should give you a speed-up of about 4 times, but it would be about 8 seconds to get a solution.
I would change while (k < i && ret == 1) { to while (k < i) {
and instead of ret = 0; do return 0;.
(this will save a check every iteration. It might be that your compiler does this anyway, or some other performance trick, but this might help a bit).
Looking for an efficient algorithm to match sets among a group of sets, ordered by the most overlapping members. 2 identical sets for example are the best match, while no overlapping members are the worst.
So, the algorithm takes input a list of sets and returns matching set pairs ordered by the sets with the most overlapping members.
Would be interested in ideas to do this efficiently. Brute force approach is to try all combinations and sort which obviously is not very performant when the number of sets is very large.
Edit: Use case - Assume a large number of sets already exist. When a new set arrives, the algorithm is run and the output includes matching sets (with at least one element overlap) sorted by the most matching to least (doesn't matter how many items are in the new/incoming set). Hope that clarifies my question.
If you can afford an approximation algorithm with a chance of error, then you should probably consider MinHash.
This algorithm allows estimating the similarity between 2 sets in constant time. For any constructed set, a fixed size signature is computed, and then only the signatures are compared when estimating the similarities. The similarity measure being used is Jaccard distance, which ranges from 0 (disjoint sets) to 1 (identical sets). It is defined as the intersection to union ratio of two given sets.
With this approach, any new set has to be compared against all existing ones (in linear time), and then the results can be merged into the top list (you can use a bounded search tree/heap for this purpose).
Since the number of possible different values is not very large, you get a fairly efficient hashing if you simply set the nth bit in a "large integer" when the nth number is present in your set. You can then look for overlap between sets with a simple bitwise AND followed by a "count set bits" operation. On 64 bit architecture, that means that you can look for the similarity between two numbers (out of 1000 possible values) in about 16 cycles, regardless of the number of values in each cluster. As the cluster gets more sparse, this becomes a less efficient algorithm.
Still - I implemented some of the basic functions you might need in some code that I attach here - not documented but reasonably understandable, I think. In this example I made the numbers small so I can check the result by hand - you might want to change some of the #defines to get larger ranges of values, and obviously you will want some dynamic lists etc to keep up with the growing catalog.
#include <stdio.h>
// biggest number you will come across: want this to be much bigger
#define MAXINT 25
// use the biggest type you have - not int
#define BITSPER (8*sizeof(int))
#define NWORDS (MAXINT/BITSPER + 1)
// max number in a cluster
#define CSIZE 5
typedef struct{
unsigned int num[NWORDS]; // want to use longest type but not for demo
int newmatch;
int rank;
} hmap;
// convert number to binary sequence:
void hashIt(int* t, int n, hmap* h) {
int ii;
for(ii=0;ii<n;ii++) {
int a, b;
a = t[ii]%BITSPER;
b = t[ii]/BITSPER;
h->num[b]|=1<<a;
}
}
// print binary number:
void printBinary(int n) {
unsigned int jj;
jj = 1<<31;
while(jj!=0) {
printf("%c",((n&jj)!=0)?'1':'0');
jj>>=1;
}
printf(" ");
}
// print the array of binary numbers:
void printHash(hmap* h) {
unsigned int ii, jj;
for(ii=0; ii<NWORDS; ii++) {
jj = 1<<31;
printf("0x%08x: ", h->num[ii]);
printBinary(h->num[ii]);
}
//printf("\n");
}
// find the maximum overlap for set m of n
int maxOverlap(hmap* h, int m, int n) {
int ii, jj;
int overlap, maxOverlap = -1;
for(ii = 0; ii<n; ii++) {
if(ii == m) continue; // don't compare with yourself
else {
overlap = 0;
for(jj = 0; jj< NWORDS; jj++) {
// just to see what's going on: take these print statements out
printBinary(h->num[ii]);
printBinary(h->num[m]);
int bc = countBits(h->num[ii] & h->num[m]);
printBinary(h->num[ii] & h->num[m]);
printf("%d bits overlap\n", bc);
overlap += bc;
}
if(overlap > maxOverlap) maxOverlap = overlap;
}
}
return maxOverlap;
}
int countBits (unsigned int b) {
int count;
for (count = 0; b != 0; count++) {
b &= b - 1; // this clears the LSB-most set bit
}
return count;
}
int main(void) {
int cluster[20][CSIZE];
int temp[CSIZE];
int ii,jj;
static hmap H[20]; // make them all 0 initially
for(jj=0; jj<20; jj++){
for(ii=0; ii<CSIZE; ii++) {
temp[ii] = rand()%MAXINT;
}
hashIt(temp, CSIZE, &H[jj]);
}
for(ii=0;ii<20;ii++) {
printHash(&H[ii]);
printf("max overlap: %d\n", maxOverlap(H, ii, 20));
}
}
See if this helps at all...
How do you print numbers of form 2^i * 5^j in increasing order.
For eg:
1, 2, 4, 5, 8, 10, 16, 20
This is actually a very interesting question, especially if you don't want this to be N^2 or NlogN complexity.
What I would do is the following:
Define a data structure containing 2 values (i and j) and the result of the formula.
Define a collection (e.g. std::vector) containing this data structures
Initialize the collection with the value (0,0) (the result is 1 in this case)
Now in a loop do the following:
Look in the collection and take the instance with the smallest value
Remove it from the collection
Print this out
Create 2 new instances based on the instance you just processed
In the first instance increment i
In the second instance increment j
Add both instances to the collection (if they aren't in the collection yet)
Loop until you had enough of it
The performance can be easily tweaked by choosing the right data structure and collection.
E.g. in C++, you could use an std::map, where the key is the result of the formula, and the value is the pair (i,j). Taking the smallest value is then just taking the first instance in the map (*map.begin()).
I quickly wrote the following application to illustrate it (it works!, but contains no further comments, sorry):
#include <math.h>
#include <map>
#include <iostream>
typedef __int64 Integer;
typedef std::pair<Integer,Integer> MyPair;
typedef std::map<Integer,MyPair> MyMap;
Integer result(const MyPair &myPair)
{
return pow((double)2,(double)myPair.first) * pow((double)5,(double)myPair.second);
}
int main()
{
MyMap myMap;
MyPair firstValue(0,0);
myMap[result(firstValue)] = firstValue;
while (true)
{
auto it=myMap.begin();
if (it->first < 0) break; // overflow
MyPair myPair = it->second;
std::cout << it->first << "= 2^" << myPair.first << "*5^" << myPair.second << std::endl;
myMap.erase(it);
MyPair pair1 = myPair;
++pair1.first;
myMap[result(pair1)] = pair1;
MyPair pair2 = myPair;
++pair2.second;
myMap[result(pair2)] = pair2;
}
}
This is well suited to a functional programming style. In F#:
let min (a,b)= if(a<b)then a else b;;
type stream (current, next)=
member this.current = current
member this.next():stream = next();;
let rec merge(a:stream,b:stream)=
if(a.current<b.current) then new stream(a.current, fun()->merge(a.next(),b))
else new stream(b.current, fun()->merge(a,b.next()));;
let rec Squares(start) = new stream(start,fun()->Squares(start*2));;
let rec AllPowers(start) = new stream(start,fun()->merge(Squares(start*2),AllPowers(start*5)));;
let Results = AllPowers(1);;
Works well with Results then being a stream type with current value and a next method.
Walking through it:
I define min for completenes.
I define a stream type to have a current value and a method to return a new string, essentially head and tail of a stream of numbers.
I define the function merge, which takes the smaller of the current values of two streams and then increments that stream. It then recurses to provide the rest of the stream. Essentially, given two streams which are in order, it will produce a new stream which is in order.
I define squares to be a stream increasing in powers of 2.
AllPowers takes the start value and merges the stream resulting from all squares at this number of powers of 5. it with the stream resulting from multiplying it by 5, since these are your only two options. You effectively are left with a tree of results
The result is merging more and more streams, so you merge the following streams
1, 2, 4, 8, 16, 32...
5, 10, 20, 40, 80, 160...
25, 50, 100, 200, 400...
.
.
.
Merging all of these turns out to be fairly efficient with tail recursio and compiler optimisations etc.
These could be printed to the console like this:
let rec PrintAll(s:stream)=
if (s.current > 0) then
do System.Console.WriteLine(s.current)
PrintAll(s.next());;
PrintAll(Results);
let v = System.Console.ReadLine();
Similar things could be done in any language which allows for recursion and passing functions as values (it's only a little more complex if you can't pass functions as variables).
For an O(N) solution, you can use a list of numbers found so far and two indexes: one representing the next number to be multiplied by 2, and the other the next number to be multiplied by 5. Then in each iteration you have two candidate values to choose the smaller one from.
In Python:
numbers = [1]
next_2 = 0
next_5 = 0
for i in xrange(100):
mult_2 = numbers[next_2]*2
mult_5 = numbers[next_5]*5
if mult_2 < mult_5:
next = mult_2
next_2 += 1
else:
next = mult_5
next_5 += 1
# The comparison here is to avoid appending duplicates
if next > numbers[-1]:
numbers.append(next)
print numbers
So we have two loops, one incrementing i and second one incrementing j starting both from zero, right? (multiply symbol is confusing in the title of the question)
You can do something very straightforward:
Add all items in an array
Sort the array
Or you need an other solution with more math analysys?
EDIT: More smart solution by leveraging similarity with Merge Sort problem
If we imagine infinite set of numbers of 2^i and 5^j as two independent streams/lists this problem looks very the same as well known Merge Sort problem.
So solution steps are:
Get two numbers one from the each of streams (of 2 and of 5)
Compare
Return smallest
get next number from the stream of the previously returned smallest
and that's it! ;)
PS: Complexity of Merge Sort always is O(n*log(n))
I visualize this problem as a matrix M where M(i,j) = 2^i * 5^j. This means that both the rows and columns are increasing.
Think about drawing a line through the entries in increasing order, clearly beginning at entry (1,1). As you visit entries, the row and column increasing conditions ensure that the shape formed by those cells will always be an integer partition (in English notation). Keep track of this partition (mu = (m1, m2, m3, ...) where mi is the number of smaller entries in row i -- hence m1 >= m2 >= ...). Then the only entries that you need to compare are those entries which can be added to the partition.
Here's a crude example. Suppose you've visited all the xs (mu = (5,3,3,1)), then you need only check the #s:
x x x x x #
x x x #
x x x
x #
#
Therefore the number of checks is the number of addable cells (equivalently the number of ways to go up in Bruhat order if you're of a mind to think in terms of posets).
Given a partition mu, it's easy to determine what the addable states are. Image an infinite string of 0s following the last positive entry. Then you can increase mi by 1 if and only if m(i-1) > mi.
Back to the example, for mu = (5,3,3,1) we can increase m1 (6,3,3,1) or m2 (5,4,3,1) or m4 (5,3,3,2) or m5 (5,3,3,1,1).
The solution to the problem then finds the correct sequence of partitions (saturated chain). In pseudocode:
mu = [1,0,0,...,0];
while (/* some terminate condition or go on forever */) {
minNext = 0;
nextCell = [];
// look through all addable cells
for (int i=0; i<mu.length; ++i) {
if (i==0 or mu[i-1]>mu[i]) {
// check for new minimum value
if (minNext == 0 or 2^i * 5^(mu[i]+1) < minNext) {
nextCell = i;
minNext = 2^i * 5^(mu[i]+1)
}
}
}
// print next largest entry and update mu
print(minNext);
mu[i]++;
}
I wrote this in Maple stopping after 12 iterations:
1, 2, 4, 5, 8, 10, 16, 20, 25, 32, 40, 50
and the outputted sequence of cells added and got this:
1 2 3 5 7 10
4 6 8 11
9 12
corresponding to this matrix representation:
1, 2, 4, 8, 16, 32...
5, 10, 20, 40, 80, 160...
25, 50, 100, 200, 400...
First of all, (as others mentioned already) this question is very vague!!!
Nevertheless, I am going to give a shot based on your vague equation and the pattern as your expected result. So I am not sure the following will be true for what you are trying to do, however it may give you some idea about java collections!
import java.util.List;
import java.util.ArrayList;
import java.util.SortedSet;
import java.util.TreeSet;
public class IncreasingNumbers {
private static List<Integer> findIncreasingNumbers(int maxIteration) {
SortedSet<Integer> numbers = new TreeSet<Integer>();
SortedSet<Integer> numbers2 = new TreeSet<Integer>();
for (int i=0;i < maxIteration;i++) {
int n1 = (int)Math.pow(2, i);
numbers.add(n1);
for (int j=0;j < maxIteration;j++) {
int n2 = (int)Math.pow(5, i);
numbers.add(n2);
for (Integer n: numbers) {
int n3 = n*n1;
numbers2.add(n3);
}
}
}
numbers.addAll(numbers2);
return new ArrayList<Integer>(numbers);
}
/**
* Based on the following fuzzy question # StackOverflow
* http://stackoverflow.com/questions/7571934/printing-numbers-of-the-form-2i-5j-in-increasing-order
*
*
* Result:
* 1 2 4 5 8 10 16 20 25 32 40 64 80 100 125 128 200 256 400 625 1000 2000 10000
*/
public static void main(String[] args) {
List<Integer> numbers = findIncreasingNumbers(5);
for (Integer i: numbers) {
System.out.print(i + " ");
}
}
}
If you can do it in O(nlogn), here's a simple solution:
Get an empty min-heap
Put 1 in the heap
while (you want to continue)
Get num from heap
print num
put num*2 and num*5 in the heap
There you have it. By min-heap, I mean min-heap
As a mathematician the first thing I always think about when looking at something like this is "will logarithms help?".
In this case it might.
If our series A is increasing then the series log(A) is also increasing. Since all terms of A are of the form 2^i.5^j then all members of the series log(A) are of the form i.log(2) + j.log(5)
We can then look at the series log(A)/log(2) which is also increasing and its elements are of the form i+j.(log(5)/log(2))
If we work out the i and j that generates the full ordered list for this last series (call it B) then that i and j will also generate the series A correctly.
This is just changing the nature of the problem but hopefully to one where it becomes easier to solve. At each step you can either increase i and decrease j or vice versa.
Looking at a few of the early changes you can make (which I will possibly refer to as transforms of i,j or just transorms) gives us some clues of where we are going.
Clearly increasing i by 1 will increase B by 1. However, given that log(5)/log(2) is approx 2.3 then increasing j by 1 while decreasing i by 2 will given an increase of just 0.3 . The problem then is at each stage finding the minimum possible increase in B for changes of i and j.
To do this I just kept a record as I increased of the most efficient transforms of i and j (ie what to add and subtract from each) to get the smallest possible increase in the series. Then applied whichever one was valid (ie making sure i and j don't go negative).
Since at each stage you can either decrease i or decrease j there are effectively two classes of transforms that can be checked individually. A new transform doesn't have to have the best overall score to be included in our future checks, just better than any other in its class.
To test my thougths I wrote a sort of program in LinqPad. Key things to note are that the Dump() method just outputs the object to screen and that the syntax/structure isn't valid for a real c# file. Converting it if you want to run it should be easy though.
Hopefully anything not explicitly explained will be understandable from the code.
void Main()
{
double C = Math.Log(5)/Math.Log(2);
int i = 0;
int j = 0;
int maxi = i;
int maxj = j;
List<int> outputList = new List<int>();
List<Transform> transforms = new List<Transform>();
outputList.Add(1);
while (outputList.Count<500)
{
Transform tr;
if (i==maxi)
{
//We haven't considered i this big before. Lets see if we can find an efficient transform by getting this many i and taking away some j.
maxi++;
tr = new Transform(maxi, (int)(-(maxi-maxi%C)/C), maxi%C);
AddIfWorthwhile(transforms, tr);
}
if (j==maxj)
{
//We haven't considered j this big before. Lets see if we can find an efficient transform by getting this many j and taking away some i.
maxj++;
tr = new Transform((int)(-(maxj*C)), maxj, (maxj*C)%1);
AddIfWorthwhile(transforms, tr);
}
//We have a set of transforms. We first find ones that are valid then order them by score and take the first (smallest) one.
Transform bestTransform = transforms.Where(x=>x.I>=-i && x.J >=-j).OrderBy(x=>x.Score).First();
//Apply transform
i+=bestTransform.I;
j+=bestTransform.J;
//output the next number in out list.
int value = GetValue(i,j);
//This line just gets it to stop when it overflows. I would have expected an exception but maybe LinqPad does magic with them?
if (value<0) break;
outputList.Add(value);
}
outputList.Dump();
}
public int GetValue(int i, int j)
{
return (int)(Math.Pow(2,i)*Math.Pow(5,j));
}
public void AddIfWorthwhile(List<Transform> list, Transform tr)
{
if (list.Where(x=>(x.Score<tr.Score && x.IncreaseI == tr.IncreaseI)).Count()==0)
{
list.Add(tr);
}
}
// Define other methods and classes here
public class Transform
{
public int I;
public int J;
public double Score;
public bool IncreaseI
{
get {return I>0;}
}
public Transform(int i, int j, double score)
{
I=i;
J=j;
Score=score;
}
}
I've not bothered looking at the efficiency of this but I strongly suspect its better than some other solutions because at each stage all I need to do is check my set of transforms - working out how many of these there are compared to "n" is non-trivial. It is clearly related since the further you go the more transforms there are but the number of new transforms becomes vanishingly small at higher numbers so maybe its just O(1). This O stuff always confused me though. ;-)
One advantage over other solutions is that it allows you to calculate i,j without needing to calculate the product allowing me to work out what the sequence would be without needing to calculate the actual number itself.
For what its worth after the first 230 nunmbers (when int runs out of space) I had 9 transforms to check each time. And given its only my total that overflowed I ran if for the first million results and got to i=5191 and j=354. The number of transforms was 23. The size of this number in the list is approximately 10^1810. Runtime to get to this level was approx 5 seconds.
P.S. If you like this answer please feel free to tell your friends since I spent ages on this and a few +1s would be nice compensation. Or in fact just comment to tell me what you think. :)
I'm sure everyone one's might have got the answer by now, but just wanted to give a direction to this solution..
It's a Ctrl C + Ctrl V from
http://www.careercup.com/question?id=16378662
void print(int N)
{
int arr[N];
arr[0] = 1;
int i = 0, j = 0, k = 1;
int numJ, numI;
int num;
for(int count = 1; count < N; )
{
numI = arr[i] * 2;
numJ = arr[j] * 5;
if(numI < numJ)
{
num = numI;
i++;
}
else
{
num = numJ;
j++;
}
if(num > arr[k-1])
{
arr[k] = num;
k++;
count++;
}
}
for(int counter = 0; counter < N; counter++)
{
printf("%d ", arr[counter]);
}
}
The question as put to me was to return an infinite set of solutions. I pondered the use of trees, but felt there was a problem with figuring out when to harvest and prune the tree, given an infinite number of values for i & j. I realized that a sieve algorithm could be used. Starting from zero, determine whether each positive integer had values for i and j. This was facilitated by turning answer = (2^i)*(2^j) around and solving for i instead. That gave me i = log2 (answer/ (5^j)). Here is the code:
class Program
{
static void Main(string[] args)
{
var startTime = DateTime.Now;
int potential = 0;
do
{
if (ExistsIandJ(potential))
Console.WriteLine("{0}", potential);
potential++;
} while (potential < 100000);
Console.WriteLine("Took {0} seconds", DateTime.Now.Subtract(startTime).TotalSeconds);
}
private static bool ExistsIandJ(int potential)
{
// potential = (2^i)*(5^j)
// 1 = (2^i)*(5^j)/potential
// 1/(2^1) = (5^j)/potential or (2^i) = potential / (5^j)
// i = log2 (potential / (5^j))
for (var j = 0; Math.Pow(5,j) <= potential; j++)
{
var i = Math.Log(potential / Math.Pow(5, j), 2);
if (i == Math.Truncate(i))
return true;
}
return false;
}
}
I'm practicing for the upcoming ACM programming competition in a week and I've gotten stumped on this programming problem.
The problem is as follows:
You have a puzzle consisting of a square grid of size 4. Each grid square holds a single coin; each coin is showing either heads (H) and tails (T). One such puzzle is shown here:
H H H H
T T T T
H T H T
T T H T
Any coin that is current showing Tails (T) can be flipped to Heads (H). However, any time we flip a coin, we must also flip the adjacent coins direct above, below and to the left and right in the same row. Thus if we flip the second coin in the second row we must also flip 4 other coins, giving us this arrangment (coins that changed are shown in bold).
H T H H
H H H T
H H H T
T T H T
If a coin is at the edge of the puzzle, so there is no coin on one side or the other, then we flip fewer coins. We do not "wrap around" to the other side. For example, if we flipped the bottom right coin of the arragnement above we would get:
H T H H
H H H T
H H H H
T T T H
Note: Only coins showing (T) tails can be selected for flipping. However, anytime we flip such a coin, adjacent coins are also flipped, regardless of their state.
The goal of the puzzle is to have all coins show heads. While it is possible for some arragnements to not have solutions, all the problems given will have solutions. The answer we are looking for is, for any given 4x4 grid of coins what is the least number of flips in order to make the grid entirely heads.
For Example the grid:
H T H H
T T T H
H T H T
H H T T
The answer to this grid is: 2 flips.
What I have done so far:
I'm storing our grids as two-dimensional array of booleans. Heads = true, tails = false.
I have a flip(int row, int col) method that will flip the adjacent coins according the rules above and I have a isSolved() method that will determine if the puzzle is in a solved state (all heads). So we have our "mechanics" in place.
The part we are having problems with is how should we loop through, going an the least amount of times deep?
Your puzzle is a classic Breadth-First Search candidate. This is because you're looking for a solution with the fewest possible 'moves'.
If you knew the number of moves to the goal, then that would be ideal for a Depth-First Search.
Those Wikipedia articles contain plenty of information about the way the searches work, they even contain code samples in several languages.
Either search can be recursive, if you're sure you won't run out of stack space.
EDIT: I hadn't noticed that you can't use a coin as the primary move unless it's showing tails. That does indeed make order important. I'll leave this answer here, but look into writing another one as well.
No pseudo-code here, but think about this: can you ever imagine yourself flipping a coin twice? What would be the effect?
Alternative, write down some arbitrary board (literally, write it down). Set up some real world coins, and pick two arbitrary ones, X and Y. Do an "X flip", then a "Y flip" then another "X flip". Write down the result. Now reset the board to the starting version, and just do a "Y flip". Compare the results, and think about what's happened. Try it a few times, sometimes with X and Y close together, sometimes not. Become confident in your conclusion.
That line of thought should lead you to a way of determining a finite set of possible solutions. You can test all of them fairly easily.
Hope this hint wasn't too blatant - I'll keep an eye on this question to see if you need more help. It's a nice puzzle.
As for recursion: you could use recursion. Personally, I wouldn't in this case.
EDIT: Actually, on second thoughts I probably would use recursion. It could make life a lot simpler.
Okay, perhaps that wasn't obvious enough. Let's label the coins A-P, like this:
ABCD
EFGH
IJKL
MNOP
Flipping F will always involve the following coins changing state: BEFGJ.
Flipping J will always involve the following coins changing state: FIJKN.
What happens if you flip a coin twice? The two flips cancel each other out, no matter what other flips occur.
In other words, flipping F and then J is the same as flipping J and then F. Flipping F and then J and then F again is the same as just flipping J to start with.
So any solution isn't really a path of "flip A then F then J" - it's "flip <these coins>; don't flip <these coins>". (It's unfortunate that the word "flip" is used for both the primary coin to flip and the secondary coins which change state for a particular move, but never mind - hopefully it's clear what I mean.)
Each coin will either be used as a primary move or not, 0 or 1. There are 16 coins, so 2^16 possibilities. So 0 might represent "don't do anything"; 1 might represent "just A"; 2 might represent "just B"; 3 "A and B" etc.
Test each combination. If (somehow) there's more than one solution, count the number of bits in each solution to find the least number.
Implementation hint: the "current state" can be represented as a 16 bit number as well. Using a particular coin as a primary move will always XOR the current state with a fixed number (for that coin). This makes it really easy to work out the effect of any particular combination of moves.
Okay, here's the solution in C#. It shows how many moves were required for each solution it finds, but it doesn't keep track of which moves those were, or what the least number of moves is. That's a SMOP :)
The input is a list of which coins are showing tails to start with - so for the example in the question, you'd start the program with an argument of "BEFGJLOP". Code:
using System;
public class CoinFlip
{
// All ints could really be ushorts, but ints are easier
// to work with
static readonly int[] MoveTransitions = CalculateMoveTransitions();
static int[] CalculateMoveTransitions()
{
int[] ret = new int[16];
for (int i=0; i < 16; i++)
{
int row = i / 4;
int col = i % 4;
ret[i] = PositionToBit(row, col) +
PositionToBit(row-1, col) +
PositionToBit(row+1, col) +
PositionToBit(row, col-1) +
PositionToBit(row, col+1);
}
return ret;
}
static int PositionToBit(int row, int col)
{
if (row < 0 || row > 3 || col < 0 || col > 3)
{
// Makes edge detection easier
return 0;
}
return 1 << (row * 4 + col);
}
static void Main(string[] args)
{
int initial = 0;
foreach (char c in args[0])
{
initial += 1 << (c-'A');
}
Console.WriteLine("Initial = {0}", initial);
ChangeState(initial, 0, 0);
}
static void ChangeState(int current, int nextCoin, int currentFlips)
{
// Reached the end. Success?
if (nextCoin == 16)
{
if (current == 0)
{
// More work required if we want to display the solution :)
Console.WriteLine("Found solution with {0} flips", currentFlips);
}
}
else
{
// Don't flip this coin
ChangeState(current, nextCoin+1, currentFlips);
// Or do...
ChangeState(current ^ MoveTransitions[nextCoin], nextCoin+1, currentFlips+1);
}
}
}
I would suggest a breadth first search, as someone else already mentioned.
The big secret here is to have multiple copies of the game board. Don't think of "the board."
I suggest creating a data structure that contains a representation of a board, and an ordered list of moves that got to that board from the starting position. A move is the coordinates of the center coin in a set of flips. I'll call an instance of this data structure a "state" below.
My basic algorithm would look something like this:
Create a queue.
Create a state that contains the start position and an empty list of moves.
Put this state into the queue.
Loop forever:
Pull first state off of queue.
For each coin showing tails on the board:
Create a new state by flipping that coin and the appropriate others around it.
Add the coordinates of that coin to the list of moves in the new state.
If the new state shows all heads:
Rejoice, you are done.
Push the new state into the end of the queue.
If you like, you could add a limit to the length of the queue or the length of move lists, to pick a place to give up. You could also keep track of boards that you have already seen in order to detect loops. If the queue empties and you haven't found any solutions, then none exist.
Also, a few of the comments already made seem to ignore the fact that the problem only allows coins that show tails to be in the middle of a move. This means that order very much does matter. If the first move flips a coin from heads to tails, then that coin can be the center of the second move, but it could not have been the center of the first move. Similarly, if the first move flips a coin from tails to heads, then that coin cannot be the center of the second move, even though it could have been the center of the first move.
The grid, read in row-major order, is nothing more than a 16 bit integer. Both the grid given by the problem and the 16 possible moves (or "generators") can be stored as 16 bit integers, thus the problems amounts to find the least possible number of generators which, summed by means of bitwise XOR, gives the grid itself as the result. I wonder if there's a smarter alternative than trying all the 65536 possibilities.
EDIT: Indeed there is a convenient way to do bruteforcing. You can try all the 1-move patterns, then all the 2-moves patterns, and so on. When a n-moves pattern matches the grid, you can stop, exhibit the winning pattern and say that the solution requires at least n moves. Enumeration of all the n-moves patterns is a recursive problem.
EDIT2: You can bruteforce with something along the lines of the following (probably buggy) recursive pseudocode:
// Tries all the n bit patterns with k bits set to 1
tryAllPatterns(unsigned short n, unsigned short k, unsigned short commonAddend=0)
{
if(n == 0)
tryPattern(commonAddend);
else
{
// All the patterns that have the n-th bit set to 1 and k-1 bits
// set to 1 in the remaining
tryAllPatterns(n-1, k-1, (2^(n-1) xor commonAddend) );
// All the patterns that have the n-th bit set to 0 and k bits
// set to 1 in the remaining
tryAllPatterns(n-1, k, commonAddend );
}
}
To elaborate on Federico's suggestion, the problem is about finding a set of the 16 generators that xor'ed together gives the starting position.
But if we consider each generator as a vector of integers modulo 2, this becomes finding a linear combination of vectors, that equal the starting position.
Solving this should just be a matter of gaussian elimination (mod 2).
EDIT:
After thinking a bit more, I think this would work:
Build a binary matrix G of all the generators, and let s be the starting state. We are looking for vectors x satisfying Gx=s (mod 2). After doing gaussian elimination, we either end up with such a vector x or we find that there are no solutions.
The problem is then to find the vector y such that Gy = 0 and x^y has as few bits set as possible, and I think the easiest way to find this would be to try all such y. Since they only depend on G, they can be precomputed.
I admit that a brute-force search would be a lot easier to implement, though. =)
Okay, here's an answer now that I've read the rules properly :)
It's a breadth-first search using a queue of states and the moves taken to get there. It doesn't make any attempt to prevent cycles, but you have to specify a maximum number of iterations to try, so it can't go on forever.
This implementation creates a lot of strings - an immutable linked list of moves would be neater on this front, but I don't have time for that right now.
using System;
using System.Collections.Generic;
public class CoinFlip
{
struct Position
{
readonly string moves;
readonly int state;
public Position(string moves, int state)
{
this.moves = moves;
this.state = state;
}
public string Moves { get { return moves; } }
public int State { get { return state; } }
public IEnumerable<Position> GetNextPositions()
{
for (int move = 0; move < 16; move++)
{
if ((state & (1 << move)) == 0)
{
continue; // Not allowed - it's already heads
}
int newState = state ^ MoveTransitions[move];
yield return new Position(moves + (char)(move+'A'), newState);
}
}
}
// All ints could really be ushorts, but ints are easier
// to work with
static readonly int[] MoveTransitions = CalculateMoveTransitions();
static int[] CalculateMoveTransitions()
{
int[] ret = new int[16];
for (int i=0; i < 16; i++)
{
int row = i / 4;
int col = i % 4;
ret[i] = PositionToBit(row, col) +
PositionToBit(row-1, col) +
PositionToBit(row+1, col) +
PositionToBit(row, col-1) +
PositionToBit(row, col+1);
}
return ret;
}
static int PositionToBit(int row, int col)
{
if (row < 0 || row > 3 || col < 0 || col > 3)
{
return 0;
}
return 1 << (row * 4 + col);
}
static void Main(string[] args)
{
int initial = 0;
foreach (char c in args[0])
{
initial += 1 << (c-'A');
}
int maxDepth = int.Parse(args[1]);
Queue<Position> queue = new Queue<Position>();
queue.Enqueue(new Position("", initial));
while (queue.Count != 0)
{
Position current = queue.Dequeue();
if (current.State == 0)
{
Console.WriteLine("Found solution in {0} moves: {1}",
current.Moves.Length, current.Moves);
return;
}
if (current.Moves.Length == maxDepth)
{
continue;
}
// Shame Queue<T> doesn't have EnqueueRange :(
foreach (Position nextPosition in current.GetNextPositions())
{
queue.Enqueue(nextPosition);
}
}
Console.WriteLine("No solutions");
}
}
If you are practicing for the ACM, I would consider this puzzle also for non-trivial boards, say 1000x1000. Brute force / greedy may still work, but be careful to avoid exponential blow-up.
The is the classic "Lights Out" problem. There is actually an easy O(2^N) brute force solution, where N is either the width or the height, whichever is smaller.
Let's assume the following works on the width, since you can transpose it.
One observation is that you don't need to press the same button twice - it just cancels out.
The key concept is just that you only need to determine if you want to press the button for each item on the first row. Every other button press is uniquely determined by one thing - whether the light above the considered button is on. If you're looking at cell (x,y), and cell (x,y-1) is on, there's only one way to turn it off, by pressing (x,y). Iterate through the rows from top to bottom and if there are no lights left on at the end, you have a solution there. You can then take the min of all the tries.
It's a finite state machine, where each "state" is the 16 bit integer corresponding the the value of each coin.
Each state has 16 outbound transitions, corresponding to the state after you flip each coin.
Once you've mapped out all the states and transitions, you have to find the shortest path in the graph from your beginning state to state 1111 1111 1111 1111,
I sat down and attempted my own solution to this problem (based on the help I received in this thread). I'm using a 2d array of booleans, so it isn't as nice as the people using 16bit integers with bit manipulation.
In any case, here is my solution in Java:
import java.util.*;
class Node
{
public boolean[][] Value;
public Node Parent;
public Node (boolean[][] value, Node parent)
{
this.Value = value;
this.Parent = parent;
}
}
public class CoinFlip
{
public static void main(String[] args)
{
boolean[][] startState = {{true, false, true, true},
{false, false, false, true},
{true, false, true, false},
{true, true, false, false}};
List<boolean[][]> solutionPath = search(startState);
System.out.println("Solution Depth: " + solutionPath.size());
for(int i = 0; i < solutionPath.size(); i++)
{
System.out.println("Transition " + (i+1) + ":");
print2DArray(solutionPath.get(i));
}
}
public static List<boolean[][]> search(boolean[][] startState)
{
Queue<Node> Open = new LinkedList<Node>();
Queue<Node> Closed = new LinkedList<Node>();
Node StartNode = new Node(startState, null);
Open.add(StartNode);
while(!Open.isEmpty())
{
Node nextState = Open.remove();
System.out.println("Considering: ");
print2DArray(nextState.Value);
if (isComplete(nextState.Value))
{
System.out.println("Solution Found!");
return constructPath(nextState);
}
else
{
List<Node> children = generateChildren(nextState);
Closed.add(nextState);
for(Node child : children)
{
if (!Open.contains(child))
Open.add(child);
}
}
}
return new ArrayList<boolean[][]>();
}
public static List<boolean[][]> constructPath(Node node)
{
List<boolean[][]> solutionPath = new ArrayList<boolean[][]>();
while(node.Parent != null)
{
solutionPath.add(node.Value);
node = node.Parent;
}
Collections.reverse(solutionPath);
return solutionPath;
}
public static List<Node> generateChildren(Node parent)
{
System.out.println("Generating Children...");
List<Node> children = new ArrayList<Node>();
boolean[][] coinState = parent.Value;
for(int i = 0; i < coinState.length; i++)
{
for(int j = 0; j < coinState[i].length; j++)
{
if (!coinState[i][j])
{
boolean[][] child = arrayDeepCopy(coinState);
flip(child, i, j);
children.add(new Node(child, parent));
}
}
}
return children;
}
public static boolean[][] arrayDeepCopy(boolean[][] original)
{
boolean[][] r = new boolean[original.length][original[0].length];
for(int i=0; i < original.length; i++)
for (int j=0; j < original[0].length; j++)
r[i][j] = original[i][j];
return r;
}
public static void flip(boolean[][] grid, int i, int j)
{
//System.out.println("Flip("+i+","+j+")");
// if (i,j) is on the grid, and it is tails
if ((i >= 0 && i < grid.length) && (j >= 0 && j <= grid[i].length))
{
// flip (i,j)
grid[i][j] = !grid[i][j];
// flip 1 to the right
if (i+1 >= 0 && i+1 < grid.length) grid[i+1][j] = !grid[i+1][j];
// flip 1 down
if (j+1 >= 0 && j+1 < grid[i].length) grid[i][j+1] = !grid[i][j+1];
// flip 1 to the left
if (i-1 >= 0 && i-1 < grid.length) grid[i-1][j] = !grid[i-1][j];
// flip 1 up
if (j-1 >= 0 && j-1 < grid[i].length) grid[i][j-1] = !grid[i][j-1];
}
}
public static boolean isComplete(boolean[][] coins)
{
boolean complete = true;
for(int i = 0; i < coins.length; i++)
{
for(int j = 0; j < coins[i].length; j++)
{
if (coins[i][j] == false) complete = false;
}
}
return complete;
}
public static void print2DArray(boolean[][] array)
{
for (int row=0; row < array.length; row++)
{
for (int col=0; col < array[row].length; col++)
{
System.out.print((array[row][col] ? "H" : "T") + " ");
}
System.out.println();
}
}
}