20220402, 12:24  #1 
Aug 2002
20477_{8} Posts 
April 2022

20220404, 01:44  #2 
"Kebbaj Reda"
May 2018
Casablanca, Morocco
3^{2}×11 Posts 
For trying n=4
I get this optimal solution: x = [0.825694, 0.0756939 , 0.5 , 0.25] y = [0.0756939, 0.25, 0.5 , 0.825694] f(x,y)= 6.74306 Can anyone confirm if there is other optimal results for n=4 ? Last fiddled with by Kebbaj on 20220404 at 02:09 
20220404, 02:20  #3 
Jan 2017
2×3×5^{2} Posts 

20220404, 02:38  #4 
"Kebbaj Reda"
May 2018
Casablanca, Morocco
3^{2}·11 Posts 
Whos criterion you want to say? f(x,y)=4.?
Precisely for n=4, it seems to me that f has the optimum at 6.74. Am I wrong or I don't understand well the question? Last fiddled with by Kebbaj on 20220404 at 03:01 
20220404, 03:27  #5 
Jan 2017
150_{10} Posts 

20220404, 07:55  #6  
Jul 2015
3·11 Posts 
Quote:
i did not search for optimal just checked that per my understanding it meets the constraints and calculate f(x,y) Last fiddled with by tgan on 20220404 at 07:58 Reason: fix the logic 

20220529, 12:16  #7 
Oct 2017
10000010_{2} Posts 
April 2022
Did anyone else find the excellent solution of Anders Kaseorg?
How do you find something like that? 
20220604, 19:54  #8 
Jan 2017
2×3×5^{2} Posts 
Note that you can permute the order of x_i arbitrarily if you also permute y_i the same way.
Take the example solution given in the problem statement and order the x_i in ascending order: x = [0.3688259, 0.36163773, 0.3562014, 0.45420977, 0.63245526] y = [0.05259805, 0.04540969, 0.03997346, 0.77043701, 0.6324558] Note that the last element in each is about the negative of the other. Also, yx = [ 0.31622785, 0.31622804, 0.31622794, 0.31622724, 1.26491106] except for the last element, y is about x plus a constant. Now if we assume these features (except for last, y_i = x_i + c; y_last = x_last) hold for solutions, what do they imply together with the general problem requirements? The general problem requirements say x.x=1, y.y=1, sum(x)=0, sum(y)=0, x.y=0. Since sum(x)=sum(y)=0, the changes to last and other elements from x to y must also sum to 0. c*(n1)  2*x_last = 0. So c = x_last * 2/(n1). (In the example case, 0.316 = 0.632 * 2/4). sum(x)=0 implies x.[c,c,c,c,c]=0. Thus x.(x+c)=x.x=1. y=x+c except for last element, and x.y must be 0. Thus changing the last element from x_last+c=x_last*(n+1)/(n1) to x_last must change the dot product from 1 to 0. Thus x_last*x_last*(n+1)/(n1) = x_last*(x_last)+1, and x_last = sqrt((n1)/(2n)). (In the example, 0.632 = sqrt(4/10)). So this limits the values of n for which such rational solutions can exist to those for which (n1)/(2n) is a square. Up to a million such values are 1, 2, 9, 50, 289, 1682, 9801, 57122, 332929. It turns out that these are actually sufficient requirements to make a solution optimal. ANY x which fulfills sum(x)=0, x.x=1, x_last=sqrt(n1)/(2n)) gives a solution by setting y=x+c except y_last. To find an optimal solution of say length 50, it's now enough to find a vector of length 50 which fulfills these conditions: sum(x)=0 x_last = sqrt(49/100) = 7/10 x.x = 1 (7/10)^2 = 49/100, so we need to fill the remaining elements of x with values that have these properties: they sum to 7/10, and their squares sum to 51/100. We'll just take 7/10, which leaves sum 0 and squaresum 2/100, and then add 1/10 and 1/10 which doesn't alter sum and fills square sum. (The published solution from Anders Kaseorg is one special case of constructing some values that have the required sum and sum of squares, but there are lots of other possibilities.) So now we have x=[7/10, 1/10, 1/10, 0, 0, ..., 7/10] and y=x+c, where c=x_last * 2/(n1)=7/245, except last element. y = [7/10+7/245, 1/10+7/245, 1/10+7/245, 7/245, 7/245, ..., 7/10] You can verify that these give a rational solution of length 50, which is valid (x.x=y.y=1, sum(x)=sum(y)=0, x.y=0), and fulfills the optimality condition f(x,y)=4. 
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