## Monday, April 16, 2012

### To solve Google Codejam: Hall of Mirrors I

This is probably be the problem I'll remember the most out of those solved this year. (Don't get me wrong, this is not the hardest problem I solved this year nor it is the hardest to implement, but it was cool and I had some breakthroughs while solving it). I initially had the right idea initially (try angles, simulate movement, count correct) but just the right idea is hardly enough to solve this problem. You need to think things througj and it also helps a lot to understand many things about this problem.

## The "easy" D-small

Something the official contest analysis does not say well is ... something that is supposedly explained in the statement, but you do not really understand it until trying to solve and debugging the third example case. What does it mean to count the number of images of yourself you can see? In reality, this problem asks you to count the number of starting angles for the trajectory of the light such that they will return to the starting point before traveling a distance of at most D units. Once this is understood the official analysis is useful enough.

This is supposedly easier than D-large, and in a way it is, but it is not very easy either. There were at least 15000 coders in the qualification round, but the number of people who solved this problem is around 500. And we all had 24 hours to do it.

I supposedly solved this problem during the match, and I did. But not exactly well. I discovered that there was a way to find the possible distances through a formula. But it seems that I discovered the formula in an improvised way. It turns out that the formula based on the number of bounces I explained before was wrong. Yet the overall solution is correct. Curious thing eh? Well, the thing is that although the formula is wrong, what it does is return the wrong final position (dx,dy) for each combination of vertical and horizontal bounces (bx,by), but it does so in a way that it returns the correct (dx,dy) for a different (bx,by) and that it still finds all needed pairs (dx,dy). As you can see, this mistake did not stop me from solving D-small, but in a way, it made it harder for me to solve D-large, you will see why.

The actual way to solve this problem is, again by first taking the number of horizontal bounces (bx,by). The official contest analysis does a great job explaining both how to think of the formula and why it works. A horizontal bounce can be seen as folding the whole board. You can choose to fold left or right, and thus when bx is negative, you can see it as folding left instead of right. Same about folding up or down. So, you try all pairs (bx,by) and for each of them, you find the appropriate (dx,dy). It is important not to take a pair (dx,dy) that is a multiple or divisor of a pair already picked, so that you do not pick a same direction more than once.

If you are interested in knowing what my mistake was , this is the correct formula:

             int dx, dy;
if (bx & 1) {
dx = W * (bx- sign(bx) ) + 2*( (bx >= 0)?(W-x0):(-x0));
} else {
dx = bx*W;
}
if (by & 1) {
dy = H * (by - sign(by) ) + 2*( (by >= 0)?(H-y0):(-y0));
} else {
dy = by * H;
}


Every pair of horizontal folds, move the point by twice W. If there is an odd amount of folds, then you have to increase (W-x0) or decrease (-x0) , depending if the folds are negative or not. For the y axis, the logic is similar.

## What we can learn from D-small to solve D-large

The solution to D-small is important and the analysis made in the official site is also. Not because we will actually use something like that to solve D-large, but because we need to take some conclusions from it.

First conclusion:: When solving D-small, it is best to multiply all values by 2, so that the starting point is at a lattice point instead of fractioned coordinates. It is also useful in the large problem. Thus from now on, we will assume that D is at most 100, not 50.

Second conclusion:From D-small, you can see that the final direction (dx,dy) will always go towards another point that is also at the center of a square. And the distance between (0,0) and (dx,dy) must be less than D. This actually means that (dx,dy) will always be integer and that the integers are between -100 and 100. More so, since the distance has to be at most 100, then we are only interested in lattice (dx,dy) such that their distance to (0,0) is at most 100. This greatly bounds the number of directions we have to try so the idea to just simulate all light directions is viable. We will from now on call (dx,dy) the direction vector. You know, for every dx units we move in the x-axis we will move dy units in the y axis. Note that dx and dy must be co-prime, so because for example, (2*dx, 2*dy) represents the same direction and should not be counted again.

The direction vector determines the slope of our direction, right. But since abs(dx) and abs(dy) are at most 100. This means two different slopes are never very close to each other. This is very useful, this means that precision is not a big deal. When starting this problem, I was very worried about precision, because if we simulate the movement of light until it returns to a point, we need to know when is light intersecting with a point. An error in calculation can make the point not count as intersected. Generally, using floats/dobules to answer a question that has YES or NO as an answer (And the question, [Does going towards this direction makes you return this very point?] is one of such questions) is very risky because floats are not exact numbers. As I mentioned in the previous post, I was about to try fractions from the GMP library so I had arbitrary precision and avoid this risk, but they were too slow for this problem.

You will see that in most of the operations when simulating the trajectory of the light, the most evil floating point operation we do is division. And division will be performed against dx or dy which both are ints with abs(dx) at most 100. This makes division rather stable. Since slopes cannot be very different, then we can have a generous Epsilon when comparing floats/doubles. In fact, the epsilon I ended up using was 1e-4 !, and it works.

Another thing that shall convince you about precision not being a big problem is that the definition from the statement actually asks us not to consider light bounces when they hit the limits of a segment (mirror). There are some exceptions that we need to consider, like those corners that make the light return in the opposite direction and those corners that "destroy" the light. Though.

## Part 2 - the algorithm

After we extract all those ideas from the small version, we can try to solve the large one. I decided to split this post at this place. So that it does not get too large.

Ilya Persky said...

Thank you for beautiful and detailed explanation! I've stuck with the very description of the problem, namely with "how many images of yourself can you see". I already understand the analysis, but it's still counterintuitive to me that "distinct angles" and "number of images of yourself" is the same thing, and even more counterintuitive thing is that this number doesn't depend of the shape of the maze.

vexorian said...

It does depend on the shape of the maze and your location. Because they affect how long the angle you try takes to bounce back to your location and in D-large the light can get destroyed.

This was actually annoying to me as well. When I was first trying to solve D-small I thought that the light needed to come back to you in exactly the opposite angle, but I was getting very different results in the third example.

cw said...

There is a trick to avoid all the complications around floating point numbers.

I took the same approach you initially had for D-small in the competition, reasoning that there was a limited set of coprime starting vectors you had to try.

Then for each of these I scaled the grid by LCM(vy,vx)/vx in the x direction and similar for the y plus an extra factor of 2 to allow the starting point to fall on a scaled grid line.

The movement vector in your scaled grid then just becomes of the form (1,1) (-1,1) etc. Starting vectors like (1,0) are special cases.

Then for each step of the ray tracing you know the next point of interest will be on a 'real' horizontal or vertical grid line (i.e a grid line in the original grid) and the other co-ordinate will be on one of the scaled grid lines.

I calculated the next vertical and next horizontal intersection 'real' intersection and moved to the nearest, checked the mirror config at that point and reversed the unit direction vectors as appropriate.

Distance travelled so far also becomes an integer calc so everything is exact. Runs in about 5s on my laptop.

Totally agree about visual debugging being incredibly useful for a problem like this, I ended up with loads of scaled grids drawn all over my jotter pad trying to track down index out of range exceptions. SVG is a good tip, your code for you diagrams makes that look like the way forward.

Tilps said...

Rather than scaling, I just used a fraction class based on 2 64 bit numbers - it was fast enough and using continual reduction it never overflows given the constraints of the problem.
I was less concerned about floating point numbers being problematic due to the question of whether they hit an intersection or not - my concern was the final distance calculation cutoff. I was sure that there was a right triangle a,b,c where c was within epsilon above an integer. However I was completely wrong, simple test code shows nothing which would match a relative epsilon of even 0.01%

vexorian said...

Once you know a point is within the path of the light, and the horizontal and vertical mirrors it crosses it is possible to get the squared distance in integral form. (Similar logic to D-small)

Somehow I thought 64 bits would get an overflow even with continual reductions. Uh, know I see that I was considering the effect of lcm too well.