# Distribution of 10 points within a unit square

Related to some packing problems, following problem arose:

Distribute 10 points within a square of sides 1, so that minimal distance between them is maximized.

With the help of random simulation, or any other method, please provide example of such distribution, with graphical representation of square and points, and value of minimal distance between points for such distribution.

(This problem so far to my knowledge couldn't be solved by pure mathematics, so I am coming here for valuable and desperately needed help regarding simulations etc.)

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You could use NMaximize. – b.gatessucks May 11 '14 at 9:37
Possibly related: (2594) – Mr.Wizard May 11 '14 at 12:26

Since this does seem to be a kind of packing problem I searched for prior explorations, and found a list of packings for equal-circles within a square at:

The best known packing for N = 10 is shown as:

The coordinates for the points are:

1  -0.055497063038156969690135505676  -0.351795677434771201331992637257
2   0.240911582092300627645879219810  -0.351795677434771201331992637257
3  -0.351795677434771201331992637257  -0.343720034134076580133895794673
4   0.055387032304313603995977911771  -0.076908787408790806346486820340
5   0.351795677434771201331992637257  -0.076908787408790806346486820339
6  -0.351795677434771201331992637257  -0.047311389003618982797881069187
7  -0.132623434010086730621834242216   0.152242144215576109267055784036
8   0.351795677434771201331992637257   0.219499857721666790989527905147
9  -0.351795677434771201331992637257   0.351795677434771201331992637257
10   0.086548809414597740088324152827   0.351795677434771201331992637257

Plotted:

pts = {{-0.0554971, -0.351796}, {0.240912, -0.351796}, {-0.351796, -0.34372},
{0.055387, -0.0769088}, {0.351796, -0.0769088}, {-0.351796, -0.0473114},
{-0.132623, 0.152242}, {0.351796, 0.2195}, {-0.351796, 0.351796},
{0.0865488, 0.351796}};

m = Min @ pts;

Graphics[{
{LightBlue, Rectangle[{m, m}, -{m, m}]},
{Red, AbsolutePointSize[15], Point @ pts}
}]

The minimal distance between points (full precision, and scaled to a unit square) is: 0.42127954398...

I suspect a better solution will not be forthcoming.

Blatantly copying Simon's code, but messing with optimization parameters I get this:

pts = Array[{x[#], y[#]} &, 10];
mindist2 = Min[#.# & /@ Subtract @@@ Subsets[pts, {2}]];
vars = Flatten[pts];
constraints = Thread[0 <= vars <= 1];
{md2, rules} =
NMaximize[{mindist2, constraints}, vars,
Method -> {"DifferentialEvolution",
"CrossProbability" -> 0.6,
"ScalingFactor" -> 0.68,
"RandomSeed" -> 42}];

minimaldistance = Sqrt[md2]

Graphics[{Yellow, Rectangle[], Red, PointSize[Large], Point[pts /. rules]}]
0.417788

This is a better result, but I have zero confidence that it is optimal. I wonder how else one might approach this.

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A simple numerical maximization using NMaximize as suggested by b.gatessucks:

pts = Array[{x[#], y[#]} &, 10];
mindist2 = Min[#.# & /@ Subtract @@@ Subsets[pts, {2}]];
vars = Flatten[pts];
constraints = Thread[0 <= vars <= 1];
{md2, rules} = NMaximize[{mindist2, constraints}, vars];

minimaldistance = Sqrt[md2]
(* 0.381759 *)

Graphics[{Yellow, Rectangle[], Red, PointSize[Large], Point[pts /. rules]}]

Update

Rahul Narain's comment informs us that to maximize $\min(f_1,...,f_n)$ it is typically more efficient to introduce a new variable $t$ and maximize that with the constraints $t\le f_1,...,t\le f_n$. Modifying the code to work that way, and blatantly copying Mr. Wizard's method optimizations gives an improved result:

pts = Array[{x[#], y[#]} &, 10];
dist2 = #.# & /@ Subtract @@@ Subsets[pts, {2}];
vars = Flatten[pts];
Typically in optimization, if you want to maximize $\min(f_1,\ldots,f_n)$ it is more efficient to maximize a new variable $t$ subject to the constraints $t\le f_1, \ldots, t\le f_n$. – Rahul May 11 '14 at 17:19