# optimal Gaussian zorbing

**R – Xi'an's Og**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

**A** zorbing puzzle from the Riddler: *cover the plane with four non-intersecting disks of radius one towards getting the highest probability (under the standard bivariate Normal distribution)*.

As I could not see a simple connection between the disks and the standard Normal, beyond the probability of a disk being given by a non-central chi-square cdf (with two degrees of freedom), I (once again) tried a random search by simulated annealing, which ended up with a configuration like the above, never above 0.777 using a pedestrian R code like

for(t in 1:1e6){# move the disk centres Ap=A+vemp*rnorm(2) Bp=B+vemp*rnorm(2) while(dist(rbind(Ap,Bp))<2)Bp=B+vemp*rnorm(2) Cp=C+vemp*rnorm(2) while(min(dist(rbind(Ap,Bp,Cp)))<2)Cp=C+vemp*rnorm(2) Dp=D+vemp*rnorm(2) while(min(dist(rbind(Ap,Bp,Cp,Dp)))<2)Dp=D+vemp*rnorm(2) #coverage probability pp=pchisq(1,df=2,ncp=Ap%*%Ap)+pchisq(1,df=2,ncp=Bp%*%Bp)+ pchisq(1,df=2,ncp=Cp%*%Cp)+pchisq(1,df=2,ncp=Dp%*%Dp) #simulated annealing step if(log(runif(1))<(pp-p)/sqrt(temp)){ A=Bp;B=Cp;C=Dp;D=Ap;p=pp if (sol$val<p) sol=list(val=pp,pos=rbind(A,B,C,D))} temp=temp*.9999}

I also tried a simpler configuration where all disk centres were equidistant from a reference centre, but this led to a lower “optimal” probability. I was looking forward the discussion of the puzzle, to discover if anything less brute-force was possible! But there was no deeper argument there beyond the elimination of other “natural” configurations (and missing the non-central χ² connection!). Among these options, having two disks tangent at (0,0) were optimal. But the illustration was much nicer:

**leave a comment**for the author, please follow the link and comment on their blog:

**R – Xi'an's Og**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.