I have a very interesting algorithm problem (not image processing!). But I still don't understand. Please help me.
Problem:
There are 10 patterns with 4×4 size (binary). For example,
0001
0011
0111
0001
and there's a 16×16 board (it is initialized to 0).
Now, let's choose one of 10 patterns and put it in a random position on the 16×16 board (position and pattern are selected randomly). For example,
000000000....
000100000....
001100000
001100000
000100000
000000000
000000000
........
After that, each value will be flipped with a 10% probability. For example,
000000000....
000100010....
001000000
001100100
000100000
010000000
000000100
........
Here, the problem is to guess which pattern originally existed(accuracy more than 70% allowed). In other words, out of 100 queries, it has to be successful 70 times or more.
My first approach was to calculate the accuracy for every possible patch and pattern. For example,
int NOISE_IMAGE[16][16];
int PATTERN[10][4][4];
double getScore(int x, int y, int pIdx){
int confusion[2][2] = { 0, };
for (int i = 0; i < 4; i++){
for (int j = 0; j < 4; j++){
confusion[NOISE_IMAGE[x + i][y + j]][PATTERN[pIdx][i][j]]++;
}
}
return (double)(confusion[0][0] + confusion[1][1]) / 16.;
}
void solve(){
for (int pattern = 0; pattern < 10; pattern++){
for (int x = 0; x < 14; x++){
for (int y = 0; y < 14; y++){
double score = getScore(x, y, pattern);
}
}
}
}
However, this approach has disastrous results. I think it's because the more zeros in the pattern, the higher the score.
A successful approach simply computes the difference only in the region where the pattern is 1.
int getScore(int x, int y, int pIdx){
int confusion[2][2] = { 0, };
for (int i = 0; i < 4; i++){
for (int j = 0; j < 4; j++){
confusion[NOISE_IMAGE[x + i][y + j]][PATTERN[pIdx][i][j]]++;
}
}
return confusion[1][1] - confusion[0][1];
}
I don't understand why this formula came up. Why don't we need to consider regions where the pattern is zero?
After more study, I was able to get the following formula:
Let's assume
1 (pattern)
0 (pattern)
1 (noise image)
a
c
0 (noise image)
b
d
Then, given a pattern and a noise image patch (4×4), the probability that a pattern be a noise image patch is as follows.
(9/10)(a+d) * (1/10)(b+c)
In short,
9(a+d)/1016
So, shouldn't it be proportional to a+d? But the answer is proportional to a-b.
My question is, in the above problem, why is the answer proportional to a-d, and why is the correct answer when it is 0 without considering it? please help me..
Because 16x16 board was initialized to 0, unless the number of 1 in the pattern is extremely small, it will be extremely unlikely that "10% flipping" will mislead the location of the pattern.
In other words, "Where the pattern existed" is automatically solved.
Therefore, the question is essentially "I applied 10% flipping to a specific 4x4 pattern. Which is the original pattern?"
I think that, which of the following groups is more effective for this problem will depend on the content of the 10 patterns.
a and b : "1(pattern) must be 1(noise image)"
c and d : "0(pattern) must be 0(noise image)"
If the shapes composed of 1 are characteristic and are not sufficiently similar to each other, the former(a and b) should be evaluated.
In this case, even if some 1 are lost/caused by "flip", it will not affect the shape distinction.
Adding c and d to the evaluation can only increase the likelihood of misidentification caused by "0 to 1 flipping".
(I think your situation is like this.)
If most of the places in the pattern are 1 and only a few of the rest are 0, the story is reversed.
Related
I am solving this problem on CSES.
Given n planets, with exactly 1 teleporter on each planet which teleports us to some other planet (possibly the same), we have to solve q queries. Each query is associated with a start planet, x and a number of teleporters to traverse, k. For each query, we need to tell where we would reach after going through k teleporters.
I have attempted this problem using the binary lifting concept.
For each planet, I first saved the planets we would reach by going through 20, 21, 22,... teleporters.
Now, as per the constraints (esp. for k) provided in the question, we need only store the values till 231.
Then, for each query, starting from the start planet, I traverse through the teleporters using the data in the above created array (in 1) to mimic the binary expansion of k, the number of teleporters to traverse.
For example, if k = 5, i.e. (101)2, and the initial planet is x, I first go (001)2 = 1 planet ahead, using the array, let's say to planet y, and then (100)2 = 4 planets ahead. The planet now reached is the required result to the query.
Unfortunately, I am receiving TLE (time limit exceeded) error in the last test case (test 12).
Here's my code for reference:
#define inp(x) ll x; scanf("%lld", &x)
void solve()
{
// Inputting the values of n, number of planets and q, number of queries.
inp(n);
inp(q);
// Inputting the location of next planet the teleporter on each planet points to, with correction for 0 - based indexing
vector<int> adj(n);
for(int i = 0; i < n; i++)
{
scanf("%d", &(adj[i]));
adj[i]--;
}
// maxN stores the maximum value till which we need to locate the next reachable plane, based on constraints.
// A value of 32 means that we'll only ever need to go at max 2^31 places away from the planet in query.
int maxN = 32;
// This array consists of the next planet we can reach from any planet.
// Specifically, par[i][j] is the planet we get to, on passing through 2^j teleporters starting from planet i.
vector<vector<int>> par(n, vector<int>(maxN, -1));
for(int i = 0; i < n; i++)
{
par[i][0] = adj[i];
}
for(int i = 1; i < maxN; i++)
{
for(int j = 0; j < n; j++)
{
ll p1 = par[j][i-1];
par[j][i] = par[p1][i-1];
}
}
// This task is done for each query.
for(int i = 0; i < q; i++)
{
// x is the initial planet, corrected for 0 - based indexing.
inp(x);
x--;
// k is the number of teleporters to traverse.
inp(k);
// cur is the planet we currently are at.
int cur = x;
// For every i'th bit in k that is 1, the current planet is moved to the planet we reach to by moving through 2^i teleporters from cur.
for(int i = 0; (1 << i) <= k ; i++)
{
if(k & (1 << i))
{
cur = par[cur][i];
}
}
// Once the full binary expansion of k is used up, we are at cur, so (cur + 1) is the result because of the judge's 1 - based indexing.
cout<<(cur + 1)<<endl;
}
}
The code gives the correct output in every test case, but undergoes TLE in the final one (the result in the final one is correct too, just a TLE occurs). According to my observation the complexity of the code is O(32 * q + n), which doesn't seem to exceed the 106 bound for linear time code in 1 second.
Are there any hidden costs in the algorithm I may have missed, or some possible optimization?
Any help appreciated!
It looks to me like your code works (after fixing the scanf), but your par map could have 6.4M entries in it, and precalculating all of those might just get you over the 1s time limit.
Here are a few things to try, in order of complexity:
replace par with a single vector<int> and index it like par[i*32+j]. This will remove a lot of double indirections.
Buffer the output in a std::string and write it in one step at the end, in case there's some buffer flushing going on that you don't know about. I don't think so, but it's easy to try.
Starting at each planet, you enter a cycle in <= n steps. In O(n) time, you can precalculate the distance to the terminal cycle and the size of the terminal cycle for all planets. Using this information you can reduce each k to at most 20000, and that means you only need j <= 16.
I am trying to extract the OEE trend of a manufacturing machine. I already have a dataset of OEE calculated more or less every 30 seconds for each manufacturing machine and stored in a database.
What I want to do is to extract a subset of the dataset (say, last 30 minutes) and state if the OEE has grown, decreased or has been stable (withing a certain threshold). My task is NOT to forecast what will be the next value of OEE, but just to know if has decreased (desired return value: -1), grown (desired return value: +1) or been stable (desired return value: 0) based on the dataset. I am using Java 8 in my project.
Here is an example of dataset:
71.37
71.37
70.91
70.30
70.30
70.42
70.42
69.77
69.77
69.29
68.92
68.92
68.61
68.61
68.91
68.91
68.50
68.71
69.27
69.26
69.89
69.85
69.98
69.93
69.39
68.97
69.03
From this dataset is possible to state that the OEE has been decreasing (of couse based on a threshold), thus the algorithm would return -1.
I have been searching on the web unsuccessfully. I have found this, or this github project, or this stackoverflow question. However, all those are (more or less) complex forecasting algorithm. I am searching for a much easier solution. Any help is apreciated.
You could go for a
sliding average of the last n values.
Or a
sliding median of the last n values.
It highly depends on your application what is appropriate. But both these are very simple to implement and in a lot of cases more than good enough.
As you know from math, one would use d/dt, which more or less is using the step differences.
A trend is should have some weight.
class Trend {
int direction;
double probability;
}
Trend trend(double[] lastData) {
double[] deltas = Arrays.copyOf(lastData, lastData.length - 1);
for (int i = 0; i < deltas.length; ++i) {
deltas[i] -= lastData[i + 1];
}
// Trend based on two parts:
int parts = 2;
int splitN = (deltas.length + 1) / parts;
int i = 0;
int[] trends = new int[parts];
for (int j = 0; j < parts.length; ++j) {
int n = Math.min(splitN, parts.length - i);
double partAvg = DoubleStream.of(deltas).skip(i).limit(n).sum() / n;
trends[j] = tendency(partAvg);
}
Trend result = new Trend();
trend.direction = trends[parts - 1];
double avg = IntStream.of(trends).average().orElse((double)trend.direction);
trend.probability = ((direction - avg) + 1) / 2;
return trends[parts - 1];
}
int tendency(double sum) {
final double EPS = 0.0001;
return sum < -EPS ? -1 : sum > EPS ? 1 : 0;
}
This is not very sophisticated. For more elaborate treatment a math forum might be useful.
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<<" ";
}
To start off, I needed to calculate a number of sums and then find the minimum of those sums, this was done as so, using mpi:
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &numprocs);
MPI_Comm_rank(MPI_COMM_WORLD, &myid);
.
.
.
x = (size)/numprocs;
low = myid * x;
high = low + x;
for(i =low; i < high; i++){
for(j = 0; j < matrixDim; j++){
for(k = 0; k < matrixDim; k+=gap){
for(m = 0; m < matrixDim; m+=gap){
c1 = calculation1(i,j,k,m);
if(c1 > cutoff){
sum += calculation2(modifier1[k][m], modifier2[k][m]);
}
}
}
if(sum < min){
min = sum;
minI = i;
minJ = j;
}
sum = 0;
}
}
MPI_Reduce(&result, &minimum, 1, MPI_FLOAT, MPI_MIN, 0, MPI_COMM_WORLD);
if( 0 == myid)
printf("The min is: %f", minimum);
MPI_Finalize();
However, now instead of finding the minimum sum of the whole 2D matrix, I need to find the minimum sum of every partition in the matrix, a partition will be a square defined by four points, and no matter the matrix size, there will always be 16 squares (the matrix is no smaller than 800 * 800). I'm trying to implement this using a MPI Cartesian topology, however I'm having trouble wrapping my head around the implementation. Any help, or tips would be appreciated.
This is more of an extended comment than an answer ...
Like francesco I'm not sure that I see the need for a cartesian (or any other) topology here. If your problem is as you describe it each MPI process can compute a partition minimum sum without either sending or receiving data from the other processes (apart, probably, from an initial scatter and terminal gather).
Toplogies are generally used for situations where the problem decomposes into pieces and those pieces have some idea of relative neighbourliness: in a cartesian topology a process (or piece of the problem) might have east, west, north and south neighbours for example. I don't see such a concept here, nor any utility in forcing it onto this problem.
I am interested in ways to improve or come up with algorithms that are able to solve the Travelling salesman problem for about n = 100 to 200 cities.
The wikipedia link I gave lists various optimizations, but it does so at a pretty high level, and I don't know how to go about actually implementing them in code.
There are industrial strength solvers out there, such as Concorde, but those are way too complex for what I want, and the classic solutions that flood the searches for TSP all present randomized algorithms or the classic backtracking or dynamic programming algorithms that only work for about 20 cities.
So, does anyone know how to implement a simple (by simple I mean that an implementation doesn't take more than 100-200 lines of code) TSP solver that works in reasonable time (a few seconds) for at least 100 cities? I am only interested in exact solutions.
You may assume that the input will be randomly generated, so I don't care for inputs that are aimed specifically at breaking a certain algorithm.
200 lines and no libraries is a tough constraint. The advanced solvers use branch and bound with the Held–Karp relaxation, and I'm not sure if even the most basic version of that would fit into 200 normal lines. Nevertheless, here's an outline.
Held Karp
One way to write TSP as an integer program is as follows (Dantzig, Fulkerson, Johnson). For all edges e, constant we denotes the length of edge e, and variable xe is 1 if edge e is on the tour and 0 otherwise. For all subsets S of vertices, ∂(S) denotes the edges connecting a vertex in S with a vertex not in S.
minimize sumedges e we xe
subject to
1. for all vertices v, sumedges e in ∂({v}) xe = 2
2. for all nonempty proper subsets S of vertices, sumedges e in ∂(S) xe ≥ 2
3. for all edges e in E, xe in {0, 1}
Condition 1 ensures that the set of edges is a collection of tours. Condition 2 ensures that there's only one. (Otherwise, let S be the set of vertices visited by one of the tours.) The Held–Karp relaxation is obtained by making this change.
3. for all edges e in E, xe in {0, 1}
3. for all edges e in E, 0 ≤ xe ≤ 1
Held–Karp is a linear program but it has an exponential number of constraints. One way to solve it is to introduce Lagrange multipliers and then do subgradient optimization. That boils down to a loop that computes a minimum spanning tree and then updates some vectors, but the details are sort of involved. Besides "Held–Karp" and "subgradient (descent|optimization)", "1-tree" is another useful search term.
(A slower alternative is to write an LP solver and introduce subtour constraints as they are violated by previous optima. This means writing an LP solver and a min-cut procedure, which is also more code, but it might extend better to more exotic TSP constraints.)
Branch and bound
By "partial solution", I mean an partial assignment of variables to 0 or 1, where an edge assigned 1 is definitely in the tour, and an edge assigned 0 is definitely out. Evaluating Held–Karp with these side constraints gives a lower bound on the optimum tour that respects the decisions already made (an extension).
Branch and bound maintains a set of partial solutions, at least one of which extends to an optimal solution. The pseudocode for one variant, depth-first search with best-first backtracking is as follows.
let h be an empty minheap of partial solutions, ordered by Held–Karp value
let bestsolsofar = null
let cursol be the partial solution with no variables assigned
loop
while cursol is not a complete solution and cursol's H–K value is at least as good as the value of bestsolsofar
choose a branching variable v
let sol0 be cursol union {v -> 0}
let sol1 be cursol union {v -> 1}
evaluate sol0 and sol1
let cursol be the better of the two; put the other in h
end while
if cursol is better than bestsolsofar then
let bestsolsofar = cursol
delete all heap nodes worse than cursol
end if
if h is empty then stop; we've found the optimal solution
pop the minimum element of h and store it in cursol
end loop
The idea of branch and bound is that there's a search tree of partial solutions. The point of solving Held–Karp is that the value of the LP is at most the length OPT of the optimal tour but also conjectured to be at least 3/4 OPT (in practice, usually closer to OPT).
The one detail in the pseudocode I've left out is how to choose the branching variable. The goal is usually to make the "hard" decisions first, so fixing a variable whose value is already near 0 or 1 is probably not wise. One option is to choose the closest to 0.5, but there are many, many others.
EDIT
Java implementation. 198 nonblank, noncomment lines. I forgot that 1-trees don't work with assigning variables to 1, so I branch by finding a vertex whose 1-tree has degree >2 and delete each edge in turn. This program accepts TSPLIB instances in EUC_2D format, e.g., eil51.tsp and eil76.tsp and eil101.tsp and lin105.tsp from http://www2.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/tsp/.
// simple exact TSP solver based on branch-and-bound/Held--Karp
import java.io.*;
import java.util.*;
import java.util.regex.*;
public class TSP {
// number of cities
private int n;
// city locations
private double[] x;
private double[] y;
// cost matrix
private double[][] cost;
// matrix of adjusted costs
private double[][] costWithPi;
Node bestNode = new Node();
public static void main(String[] args) throws IOException {
// read the input in TSPLIB format
// assume TYPE: TSP, EDGE_WEIGHT_TYPE: EUC_2D
// no error checking
TSP tsp = new TSP();
tsp.readInput(new InputStreamReader(System.in));
tsp.solve();
}
public void readInput(Reader r) throws IOException {
BufferedReader in = new BufferedReader(r);
Pattern specification = Pattern.compile("\\s*([A-Z_]+)\\s*(:\\s*([0-9]+))?\\s*");
Pattern data = Pattern.compile("\\s*([0-9]+)\\s+([-+.0-9Ee]+)\\s+([-+.0-9Ee]+)\\s*");
String line;
while ((line = in.readLine()) != null) {
Matcher m = specification.matcher(line);
if (!m.matches()) continue;
String keyword = m.group(1);
if (keyword.equals("DIMENSION")) {
n = Integer.parseInt(m.group(3));
cost = new double[n][n];
} else if (keyword.equals("NODE_COORD_SECTION")) {
x = new double[n];
y = new double[n];
for (int k = 0; k < n; k++) {
line = in.readLine();
m = data.matcher(line);
m.matches();
int i = Integer.parseInt(m.group(1)) - 1;
x[i] = Double.parseDouble(m.group(2));
y[i] = Double.parseDouble(m.group(3));
}
// TSPLIB distances are rounded to the nearest integer to avoid the sum of square roots problem
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
double dx = x[i] - x[j];
double dy = y[i] - y[j];
cost[i][j] = Math.rint(Math.sqrt(dx * dx + dy * dy));
}
}
}
}
}
public void solve() {
bestNode.lowerBound = Double.MAX_VALUE;
Node currentNode = new Node();
currentNode.excluded = new boolean[n][n];
costWithPi = new double[n][n];
computeHeldKarp(currentNode);
PriorityQueue<Node> pq = new PriorityQueue<Node>(11, new NodeComparator());
do {
do {
boolean isTour = true;
int i = -1;
for (int j = 0; j < n; j++) {
if (currentNode.degree[j] > 2 && (i < 0 || currentNode.degree[j] < currentNode.degree[i])) i = j;
}
if (i < 0) {
if (currentNode.lowerBound < bestNode.lowerBound) {
bestNode = currentNode;
System.err.printf("%.0f", bestNode.lowerBound);
}
break;
}
System.err.printf(".");
PriorityQueue<Node> children = new PriorityQueue<Node>(11, new NodeComparator());
children.add(exclude(currentNode, i, currentNode.parent[i]));
for (int j = 0; j < n; j++) {
if (currentNode.parent[j] == i) children.add(exclude(currentNode, i, j));
}
currentNode = children.poll();
pq.addAll(children);
} while (currentNode.lowerBound < bestNode.lowerBound);
System.err.printf("%n");
currentNode = pq.poll();
} while (currentNode != null && currentNode.lowerBound < bestNode.lowerBound);
// output suitable for gnuplot
// set style data vector
System.out.printf("# %.0f%n", bestNode.lowerBound);
int j = 0;
do {
int i = bestNode.parent[j];
System.out.printf("%f\t%f\t%f\t%f%n", x[j], y[j], x[i] - x[j], y[i] - y[j]);
j = i;
} while (j != 0);
}
private Node exclude(Node node, int i, int j) {
Node child = new Node();
child.excluded = node.excluded.clone();
child.excluded[i] = node.excluded[i].clone();
child.excluded[j] = node.excluded[j].clone();
child.excluded[i][j] = true;
child.excluded[j][i] = true;
computeHeldKarp(child);
return child;
}
private void computeHeldKarp(Node node) {
node.pi = new double[n];
node.lowerBound = Double.MIN_VALUE;
node.degree = new int[n];
node.parent = new int[n];
double lambda = 0.1;
while (lambda > 1e-06) {
double previousLowerBound = node.lowerBound;
computeOneTree(node);
if (!(node.lowerBound < bestNode.lowerBound)) return;
if (!(node.lowerBound < previousLowerBound)) lambda *= 0.9;
int denom = 0;
for (int i = 1; i < n; i++) {
int d = node.degree[i] - 2;
denom += d * d;
}
if (denom == 0) return;
double t = lambda * node.lowerBound / denom;
for (int i = 1; i < n; i++) node.pi[i] += t * (node.degree[i] - 2);
}
}
private void computeOneTree(Node node) {
// compute adjusted costs
node.lowerBound = 0.0;
Arrays.fill(node.degree, 0);
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) costWithPi[i][j] = node.excluded[i][j] ? Double.MAX_VALUE : cost[i][j] + node.pi[i] + node.pi[j];
}
int firstNeighbor;
int secondNeighbor;
// find the two cheapest edges from 0
if (costWithPi[0][2] < costWithPi[0][1]) {
firstNeighbor = 2;
secondNeighbor = 1;
} else {
firstNeighbor = 1;
secondNeighbor = 2;
}
for (int j = 3; j < n; j++) {
if (costWithPi[0][j] < costWithPi[0][secondNeighbor]) {
if (costWithPi[0][j] < costWithPi[0][firstNeighbor]) {
secondNeighbor = firstNeighbor;
firstNeighbor = j;
} else {
secondNeighbor = j;
}
}
}
addEdge(node, 0, firstNeighbor);
Arrays.fill(node.parent, firstNeighbor);
node.parent[firstNeighbor] = 0;
// compute the minimum spanning tree on nodes 1..n-1
double[] minCost = costWithPi[firstNeighbor].clone();
for (int k = 2; k < n; k++) {
int i;
for (i = 1; i < n; i++) {
if (node.degree[i] == 0) break;
}
for (int j = i + 1; j < n; j++) {
if (node.degree[j] == 0 && minCost[j] < minCost[i]) i = j;
}
addEdge(node, node.parent[i], i);
for (int j = 1; j < n; j++) {
if (node.degree[j] == 0 && costWithPi[i][j] < minCost[j]) {
minCost[j] = costWithPi[i][j];
node.parent[j] = i;
}
}
}
addEdge(node, 0, secondNeighbor);
node.parent[0] = secondNeighbor;
node.lowerBound = Math.rint(node.lowerBound);
}
private void addEdge(Node node, int i, int j) {
double q = node.lowerBound;
node.lowerBound += costWithPi[i][j];
node.degree[i]++;
node.degree[j]++;
}
}
class Node {
public boolean[][] excluded;
// Held--Karp solution
public double[] pi;
public double lowerBound;
public int[] degree;
public int[] parent;
}
class NodeComparator implements Comparator<Node> {
public int compare(Node a, Node b) {
return Double.compare(a.lowerBound, b.lowerBound);
}
}
If your graph satisfy the triangle inequality and you want a guarantee of 3/2 within the optimum I suggest the christofides algorithm. I've wrote an implementation in php at phpclasses.org.
As of 2013, It is possible to solve for 100 cities using only the exact formulation in Cplex. Add degree equations for each vertex, but include subtour-avoiding constraints only as they appear. Most of them are not necessary. Cplex has an example on this.
You should be able to solve for 100 cities. You will have to iterate every time a new subtour is found. I ran an example here and in a couple of minutes and 100 iterations later I got my results.
I took Held-Karp algorithm from concorde library and 25 cities are solved in 0.15 seconds. This performance is perfectly good for me! You can extract the code (writen in ANSI C) of held-karp from concorde library: http://www.math.uwaterloo.ca/tsp/concorde/downloads/downloads.htm. If the download has the extension gz, it should be tgz. You might need to rename it. Then you should make little ajustments to port in in VC++. First take the file heldkarp h and c (rename it cpp) and other about 5 files, make adjustments and it should work calling CCheldkarp_small(...) with edgelen: euclid_ceiling_edgelen.
TSP is an NP-hard problem. (As far as we know) there is no algorithm for NP-hard problems which runs in polynomial time, so you ask for something that doesn't exist.
It's either fast enough to finish in a reasonable time and then it's not exact, or exact but won't finish in your lifetime for 100 cities.
To give a dumb answer: me too. Everyone is interrested in such algorithm, but as others already stated: I does not (yet?) exist. Esp your combination of exact, 200 nodes, few seconds runtime and just 200 lines of code is impossible. You already know that is it NP hard and if you got the slightest impression of asymptotic behaviour you should know that there is no way of achieving this (except you prove that NP=P, and even that I would say thats not possible). Even the exact commercial solvers need for such instances far more than some seconds and as you can imagine they have far more than 200 lines of code (even when you just consider their kernels).
EDIT: The wiki algorithms are the "usual suspects" of the field: Linear Programming and branch-and-bound. Their solutions for the instances with thousands of nodes took Years to solve (they just did it with very very much CPUs parallel, so they can do it faster). Some even use for the branch-and-bound problem specific knowledge for the bounding, so they are no general approaches.
Branch and bound just enumerates all possible paths (e.g. with backtracking) and applies once it has a solution this for to stop a started recursion when it can prove that the result is not better than the already found solution (e.g. if you just visited 2 of your cities and the path is already longer than a found 200 city tour. You can discard all tours that start with that 2 city combination). Here you can invest very much problem specific knowledge in the function that tells you, that the path is not going to be better than the already found solution. The better it is, the less paths you have to look at, the faster is your algorithm.
Linear Programming is an optimization method so solve linear inequality problems. It works in polynomial time (simplex just practically, but that doesnt matter here), but the solution is real. When you have the additional constraint that the solution must be integer, it gets NP-complete. For small instances it is possible, e.g. one method to solve it, then look which variable of the solution violates the integer part and add addition inequalities to change it (this is called cutting-plane, the name cames from the fact that the inequalities define (higher-dimensional) plane, the solution space is a polytop and by adding additional inequalities you cut something with a plane from the polytop). The topic is very complex and even a general simple simplex is hard to understand when you dont want dive deep into the math. There are several good books about, one of the betters is from Chvatal, Linear Programming, but there are several more.
I have a theory, but I've never had the time to pursue it:
The TSP is a bounding problem (single shape where all points lie on the perimeter) where the optimal solution is that solution that has the shortest perimeter.
There are plenty of simple ways to get all the points that lie on a minimum bounding perimeter (imagine a large elastic band stretched around a bunch of nails in a large board.)
My theory is that if you start pushing in on the elastic band so that the length of band increases by the same amount between adjacent points on the perimeter, and each segment remains in the shape of an eliptical arc, the stretched elastic will cross points on the optimal path before crossing points on non-optimal paths. See this page on mathopenref.com on drawing ellipses--particularly steps 5 and 6. Points on the bounding perimeter can be viewed as focal points of the ellipse (F1, F2) in the images below.
What I don't know is if the "bubble stretching" process needs to be reset after each new point is added, or if the existing "bubbles" continue to grow and each new point on the perimeter causes only the localized "bubble" to turn into two line segments. I'll leave that for you to figure out.