11/9/19

# Evaluating Arbitrary Precision Integer Expressions in Julia using Metaprogramming

While watching the Mathologer masterclass on power sums

I came across a challenge to evaluate the following sum
$1^{10}+2^{10}+\cdots+1000^{10}$

This can be easily evaluated via brute force in Julia to yield

function power_cal(n)
a=big(0)
for i=1:n
a+=big(i)^10
end
a
end

julia> power_cal(1000)
91409924241424243424241924242500

Note the I had to use big to makes sure we are using BigInt in the computation. Without that, we would be quickly running in into an overflow issue and we will get a very wrong number.

In the comment section of the video I found a very elegant solution to the above sum, expressed as

(1/11) * 1000^11 + (1/2) * 1000^10 + (5/6) * 1000^9 – 1000^7 + 1000^5-(1/2) * 1000^3 + (5/66) * 1000 = 91409924241424243424241924242500

If I try to plug this into the Julia, I get

julia> (1/11) * 1000^11 + (1/2) * 1000^10 + (5/6) * 1000^9 - 1000^7 + r1000^5-(1/2) * 1000^3 + (5/66) * 1000
-6.740310541071357e18

This negative answer is not surprising at all, because we obviously ran into an overflow. We can, of course, go through that expression and modify all instances of Int64 with BigInt by wrapping it in the big function. But that would be cumbersome to do by hand.

## The power of Metaprogramming

In Julia, metaprogramming allows you to write code that creates code, the idea here to manipulate the abstract syntax tree (AST) of a Julia expression. We start to by “quoting” our original mathematical expressing into a Julia expression. In the at form it is not evaluated yet, however we can always evaluate it via eval.

julia> ex1=:((1/11) * 1000^11 + (1/2) * 1000^10 + (5/6) * 1000^9 - 1000^7 + 1000^5-(1/2) * 1000^3 + (5/66) * 1000)
:((((((1 / 11) * 1000 ^ 11 + (1 / 2) * 1000 ^ 10 + (5 / 6) * 1000 ^ 9) - 1000 ^ 7) + 1000 ^ 5) - (1 / 2) * 1000 ^ 3) + (5 / 66) * 1000)

julia> dump(ex1)
Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol +
2: Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol -
2: Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol +
2: Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol -
2: Expr
3: Expr
3: Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol ^
2: Int64 1000
3: Int64 5
3: Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol *
2: Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol /
2: Int64 1
3: Int64 2
3: Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol ^
2: Int64 1000
3: Int64 3
3: Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol *
2: Expr
head: Symbol call
args: Array{Any}((3,))
1: Symbol /
2: Int64 5
3: Int64 66
3: Int64 1000

julia> eval(ex1)
-6.740310541071357e18

The output of dump show the follow AST in all its glory (…well almost the depth is a bit truncated). Notice that here all our numbers are interpreted as Int64.
Now we walk through the is AST and change all occurrences of Int64 with BigInt by using the big function.

function makeIntBig!(ex::Expr)
args=ex.args
for i in eachindex(args)
if args[i] isa Int64
args[i]=big(args[i])
end
if args[i] isa Expr
makeIntBig!(args[i])
end
end
end

julia> ex2=copy(ex1)
:((((((1 / 11) * 1000 ^ 11 + (1 / 2) * 1000 ^ 10 + (5 / 6) * 1000 ^ 9) - 1000 ^ 7) + 1000 ^ 5) - (1 / 2) * 1000 ^ 3) + (5 / 66) * 1000)

julia> makeIntBig!(ex2)

julia> eval(ex2)
9.14099242414242434242419242425000000000000000000000000000000000000000000000014e+31

We see an improvement here, but the results are not very satisfactory. The divisions yield BigFloat results, which had a tiny bit of floating point errors. Can we do better?
Julia has support for Rational expressions baked in. We can use that improve the results. We just need to search for call expressions the / symbol and replace it by the // symbol. For safety we just have to makes sure the operands are as subtype of Integer.

function makeIntBig!(ex::Expr)
args=ex.args
if ex.head == :call && args[1]==:/ &&
length(args)==3 &&
all(x->typeof(x) <: Integer,args[2:end])
args[1]=://
args[2]=big(args[2])
args[3]=big(args[3])
else
for i in eachindex(args)
if args[i] isa Int64
args[i]=big(args[i])
end
if args[i] isa Expr
makeIntBig!(args[i])
end
end
end
end

julia> ex2=copy(ex1);

julia> makeIntBig!(ex2)

julia> eval(ex2)
91409924241424243424241924242500//1

Now that is much better! We have not lost any precision and we ended us with a Rational expression.

Finally, we can build a macro so the if we run into such expressions in the future and we want to evaluate them, we could just conveniently call it.

macro eval_bigInt(ex)
makeIntBig!(ex)
ex
end

and we can now simply evaluate our original expression as

julia> @eval_bigInt (1/11) * 1000^11 + (1/2) * 1000^10 + (5/6) * 1000^9 - 1000^7 + 1000^5-(1/2) * 1000^3 + (5/66) * 1000
91409924241424243424241924242500//1

07/28/18

# Exploring left truncatable primes

Recently I came across a fascinating Numberphile video on truncatable primes

I immediately thought it would be cool to whip a quick Julia code to get the full enumeration of all left truncatable primes, count the number of branches and also get the largest left truncatable prime.

using Primes

function get_left_primes(s::String)
p_arr=Array{String,1}()
for i=1:9
number_s="$i$s"
if isprime(parse(BigInt, number_s))
push!(p_arr,number_s)
end
end
p_arr
end

function get_all_left_primes(l)
r_l= Array{String,1}()
n_end_points=0
for i in l
new_l=get_left_primes(i)
isempty(new_l) && (n_end_points+=1)
append!(r_l,new_l)
next_new_l,new_n=get_all_left_primes(new_l)
n_end_points+=new_n # counting the chains
append!(r_l,next_new_l)
end
r_l, n
end

The first function just prepends a number (expressed in String for convenience) and checks for it possible primes that can emerge from a single digit prepending. For example:

julia> get_left_primes("17")
2-element Array{String,1}:
"317"
"617"

The second function, just makes extensive use of the first to get all left truncatable primes and also count the number of branches.

julia> all_left_primes, n_branches=get_all_left_primes([""])
(String["2", "3", "5", "7", "13", "23", "43", "53", "73", "83"  …  "6435616333396997", "6633396997", "76633396997", "963396997", "16396997", "96396997", "616396997", "916396997", "396396997", "4396396997"], 1442)

julia> n_branches
1442

julia> all_left_primes
4260-element Array{String,1}:
"2"
"3"
"5"
"7"
"13"
"23"
⋮
"96396997"
"616396997"
"916396997"
"396396997"
"4396396997"

So we the full list of possible left truncatable primes with a length 4260. Also the total number of branches came to 1442.

We now get the largest left truncatable primes with the following one liner:

julia> largest_left_prime=length.(all_left_primes)|>indmax|> x->all_left_primes[x]
"357686312646216567629137"

After this fun exploration, I found an implementation in Julia for just getting the largest left truncatable prime for any base in Rosseta Code.

02/8/18

# When Julia is faster than C

On e-day, I came across this cool tweet from Fermat’s library

So I spend a few minutes coding this into Julia

function euler(n)
m=0
for i=1:n
the_sum=0.0
while true
m+=1
the_sum+=rand()
(the_sum>1.0) && break;
end
end
m/n
end

Timing this on my machine, I got

julia> @time euler(1000000000)
15.959913 seconds (5 allocations: 176 bytes)
2.718219862

Gave a little under 16 seconds.

Tried a c implementation

#include <stdio.h>      /* printf, NULL */
#include <stdlib.h>     /* srand, rand */
#include <time.h>       /* time */

double r2()
{
return (double)rand() / (double)((unsigned)RAND_MAX + 1);
}

double euler(long int n)
{
long int m=0;
long int i;
for(i=0; i<n; i++){
double the_sum=0;
while(1) {
m++;
the_sum+=r2();
if(the_sum>1.0) break;
}
}
return (double)m/(double)n;
}

int main ()
{
printf ("Euler : %2.5f\n", euler(1000000000));

return 0;
}

and compiling with either gcc

gcc  -Ofast euler.c

or clang

clang  -Ofast euler.c

gave a timing twice as long

\$ time ./a.out
Euler : 2.71829

real    0m36.213s
user    0m36.238s
sys 0m0.004s

For the curios, I am using this version of Julia

julia> versioninfo()
Julia Version 0.6.3-pre.0
Commit 93168a6 (2017-12-18 07:11 UTC)
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: Intel(R) Core(TM) i7-4770HQ CPU @ 2.20GHz
WORD_SIZE: 64
BLAS: libopenblas (USE64BITINT DYNAMIC_ARCH NO_AFFINITY Haswell)
LAPACK: libopenblas64_
LIBM: libopenlibm
LLVM: libLLVM-3.9.1 (ORCJIT, haswell)

Now one should not put too much emphasis on such micro benchmarks. However, I found this a very curious examples when a high level language like Julia could be twice as fast a c. The Julia language authors must be doing some amazing mojo.

01/2/18

# Visualizing the Inscribed Circle and Square Puzzle

Recently, I watched a cool mind your decsions video on an inscribed circle and rectangle puzzle. In the video they showed a diagram that was not scale. I wanted to get a sense of how these differently shaped areas will match.

There was a cool ratio between the outer and inner circle radii that is expressed as

$\frac{R}{r}=\sqrt{\frac{\pi-2}{4-\pi}}$.

I used Compose.jl to rapidly do that.

using Compose
set_default_graphic_size(20cm, 20cm)
ϕ=sqrt((pi -2)/(4-pi))
R=10
r=R/ϕ
ctx=context(units=UnitBox(-10, -10, 20, 20))
composition = compose(ctx,
(ctx, rectangle(-r/√2,-r/√2,r*√2,r*√2),fill("white")),
(ctx,circle(0,0,r),fill("blue")),
(ctx,circle(0,0,R),fill("white")),
(ctx,rectangle(-10,-10,20,20),fill("red")))
composition |> SVG("inscribed.svg")

06/29/17

# Solving the Fish Riddle with JuMP

Recently I came across a nice Ted-Ed video presenting a Fish Riddle.

I thought it would be fun to try solving it using Julia’s award winning JuMP package. Before we get started, please watch the above video-you might want to pause at 2:24 if you want to solve it yourself.

To attempt this problem in Julia, you will have to install the JuMP package.

julia> Pkg.add("JuMP")

JuMP provides an algebraic modeling language for dealing with mathematical optimization problems. Basically, that allows you to focus on describing your problem in a simple syntax and it would then take care of transforming that description in a form that can be handled by any number of solvers. Those solvers can deal with several types of optimization problems, and some solvers are more generic than others. It is important to pick the right solver for the problem that you are attempting.

The problem premises are:
1. There are 50 creatures in total. That includes sharks outside the tanks and fish
2. Each SECTOR has anywhere from 1 to 7 sharks, with no two sectors having the same number of sharks.
3. Each tank has an equal number of fish
4. In total, there are 13 or fewer tanks
5. SECTOR ALPHA has 2 sharks and 4 tanks
6. SECTOR BETA has 4 sharsk and 2 tanks
We want to find the number of tanks in sector GAMMA!

Here we identify the problem as mixed integer non-linear program (MINLP). We know that because the problem involves an integer number of fish tanks, sharks, and number of fish inside each tank. It also non-linear (quadratic to be exact) because it involves multiplying two two of the problem variables to get the total number or creatures. Looking at the table of solvers in the JuMP manual. pick the Bonmin solver from AmplNLWriter package. This is an open source solver, so installation should be hassle free.

julia> Pkg.add("AmplNLWriter")

We are now ready to write some code.

using JuMP, AmplNLWriter

# Solve model
m = Model(solver=BonminNLSolver())

# Number of fish in each tank
@variable(m, n>=1, Int)

# Number of sharks in each sector
@variable(m, s[i=1:3], Int)

# Number of tanks in each sector
@variable(m, nt[i=1:3]>=0, Int)

@constraints m begin
# Constraint 2
sharks[i=1:3], 1 <= s[i] <= 7
numfish[i=1:3], 1 <= nt[i]
# Missing uniqueness in restriction
# Constraint 4
sum(nt) <= 13
# Constraint 5
s[1] == 2
nt[1] == 4
# Constraint 6
s[2] == 4
nt[2] == 2
end

# Constraints 1 & 3
@NLconstraint(m, s[1]+s[2]+s[3]+n*(nt[1]+nt[2]+nt[3]) == 50)

# Solve it
status = solve(m)

sharks_in_each_sector=getvalue(s)
fish_in_each_tank=getvalue(n)
tanks_in_each_sector=getvalue(nt)

@printf("We have %d fishes in each tank.\n", fish_in_each_tank)
@printf("We have %d tanks in sector Gamma.\n",tanks_in_each_sector[3])
@printf("We have %d sharks in sector Gamma.\n",sharks_in_each_sector[3])

In that representation we could not capture the restriction that “no two sectors having the same number of sharks”. We end up with the following output:

We have 4 fishes in each tank.
We have 4 tanks in sector Gamma.
We have 4 sharks in sector Gamma.

Since the problem domain is limited, we can possible fix that by adding a constrain that force the number of sharks in sector Gamma to be greater than 4.

@constraint(m,s[3]>=5)

This will result in an answer that that does not violate any of the stated constraints.

We have 3 fishes in each tank.
We have 7 tanks in sector Gamma.
We have 5 sharks in sector Gamma.

However, this seems like a bit of kludge. The proper way go about it is represent the number of sharks in the each sector as binary array, with only one value set to 1.

# Number of sharks in each sector
@variable(m, s[i=1:3,j=1:7], Bin)

We will have to modify our constraint block accordingly

@constraints m begin
# Constraint 2
sharks[i=1:3], sum(s[i,:]) == 1
u_sharks[j=1:7], sum(s[:,j]) <=1 # uniquness
# Constraint 4
sum(nt) <= 13
# Constraint 5
s[1,2] == 1
nt[1] == 4
# Constraint 6
s[2,4] == 1
nt[2] == 2
end

We invent a new variable array st to capture the number of sharks in each sector. This simply obtained by multiplying the binary array by the vector $[1,2,\ldots,7]^\top$

@variable(m,st[i=1:3],Int)
@constraint(m, st.==s*collect(1:7))

We rewrite our last constraint as

# Constraints 1 & 3
@NLconstraint(m, st[1]+st[2]+st[3]+n*(nt[1]+nt[2]+nt[3]) == 50)

After the model has been solved, we extract our output for the number of sharks.

sharks_in_each_sector=getvalue(st)

…and we get the correct output.

This problem might have been an overkill for using a full blown mixed integer non-linear optimizer. It can be solved by a simple table as shown in the video. However, we might not alway find ourselves in such a fortunate position. We could have also use mixed integer quadratic programming solver such as Gurobi which would be more efficient for that sort of problem. Given the small problem size, efficiency hardly matters here.

05/17/17

# Exploring Fibonacci Fractions with Julia

Recently, I came across a fascinating blog and video from Mind your Decisions. It is about how a fraction
$\frac{1}{999,999,999,999,999,999,999,998,,999,999,999,999,999,999,999,999}$
would show the Fibonacci numbers in order when looking at its decimal output.

On a spreadsheet and most standard programming languages, such output can not be attained due to the limited precision for floating point numbers. If you try this on R or Python, you will get an output of 1e-48.
Wolfram alpha,however,allows arbitrary precision.

In Julia by default we get a little better that R and Python

julia> 1/999999999999999999999998999999999999999999999999
1.000000000000000000000001000000000000000000000002000000000000000000000003000002e-48

julia> typeof(ans)
BigFloat

We observe here that we are getting the first few Fibonacci numbers $1, 1, 2, 3$. We need more precision to get more numbers. Julia has arbitrary precision arithmetic baked into the language. We can crank up the precision of the BigFloat type on demand. Of course, the higher the precision, the slower the computation and the greater the memory we use. We do that by setprecision.

julia> setprecision(BigFloat,10000)
10000

Reevaluating, we get

julia> 1/999999999999999999999998999999999999999999999999
1.00000000000000000000000100000000000000000000000200000000000000000000000300000000000000000000000500000000000000000000000800000000000000000000001300000000000000000000002100000000000000000000003400000000000000000000005500000000000000000000008900000000000000000000014400000000000000000000023300000000000000000000037700000000000000000000061000000000000000000000098700000000000000000000159700000000000000000000258400000000000000000000418100000000000000000000676500000000000000000001094600000000000000000001771100000000000000000002865700000000000000000004636800000000000000000007502500000000000000000012139300000000000000000019641800000000000000000031781100000000000000000051422900000000000000000083204000000000000000000134626900000000000000000217830900000000000000000352457800000000000000000570288700000000000000000922746500000000000000001493035200000000000000002415781700000000000000003908816900000000000000006324598600000000000000010233415500000000000000016558014100000000000000026791429600000000000000043349443700000000000000070140873300000000000000113490317000000000000000183631190300000000000000297121507300000000000000480752697600000000000000777874204900000000000001258626902500000000000002036501107400000000000003295128009900000000000005331629117300000000000008626757127200000000000013958386244500000000000022585143371700000000000036543529616200000000000059128672987900000000000095672202604100000000000154800875592000000000000250473078196100000000000405273953788100000000000655747031984200000000001061020985772300000000001716768017756500000000002777789003528800000000004494557021285300000000007272346024814100000000011766903046099400000000019039249070913500000000030806152117012900000000049845401187926400000000080651553304939300000000130496954492865700000000211148507797805000000000341645462290670700000000552793970088475700000000894439432379146400000001447233402467622100000002341672834846768500000003788906237314390600000006130579072161159100000009919485309475549700000016050064381636708800000025969549691112258500000042019614072748967300000067989163763861225800000110008777836610193100000177997941600471418900000288006719437081612000000466004661037553030900000754011380474634642900001220016041512187673800001974027421986822316700003194043463499009990500005168070885485832307200008362114348984842297700013530185234470674604900021892299583455516902600035422484817926191507500057314784401381708410100092737269219307899917600150052053620689608327700242789322839997508245300392841376460687116573000635630699300684624818301028472075761371741391301664102775062056366209602692574850823428107600904356677625885484473810507049252476708912581411411405930102594397055221918455182579303309636633329861112681897706691855248316295261201016328488578177407943098723020343826493703204299739348832404671111147398462369176231164814351698201718008635835925499096664087184867000739850794865805193502836665349891529892378369837405200686395697571872674070550577925589950242511475751264321287522115185546301842246877472357697022053e-48

That is looking much better. However it we be nice if we could extract the Fibonacci numbers that are buried in that long decimal. Using the approach in the original blog. We define a function

y(x)=one(x)-x-x^2

and calculate the decimal

a=1/y(big"1e-24")

Here we use the non-standard string literal big"..." to insure proper interpretation of our input. Using BigFloat(1e-24)) would first construct at floating point with limited precision and then do the conversion. The initial loss of precision will not be recovered in the conversion, and hence the use of big. Now we extract our Fibonacci numbers by this function

function extract_fib(a)
x=string(a)
l=2
fi=BigInt[]
push!(fi,1)
for i=1:div(length(x)-24,24)
j=parse(BigInt,x[l+1+(i-1)*24:l+i*24])
push!(fi,j)
end
fi
end

Here we first convert our very long decimal number of a string and they we exploit the fact the Fibonacci numbers occur in blocks that 24 digits in length. We get out output in an array of BigInt. We want to compare the output with exact Fibonacci numbers, we just do a quick and non-recursive implementation.

function fib(n)
f=Vector{typeof(n)}(n+1)
f[1]=f[2]=1;
for i=3:n+1
f[i]=f[i-1]+f[i-2]
end
f
end

Now we compare…

fib_exact=fib(200);
fib_frac=extract_fib(a);
for i in eachindex(fib_frac)
println(fib_exact[i], " ", fib_exact[i]-fib_frac[i])
end

We get a long sequence, we just focused here on when the discrepancy happens.

...
184551825793033096366333 0
298611126818977066918552 0
483162952612010163284885 0
781774079430987230203437 -1
1264937032042997393488322 999999999999999999999998
2046711111473984623691759 1999999999999999999999997
...

The output shows that just before the extracted Fibonacci number exceeds 24 digits, a discrepancy occurs. I am not quite sure why, but this was a fun exploration. Julia allows me to do mathematical explorations that would take one or even two orders of magnitude of effort to do in any other language.

01/13/17

# Kaperkar’s Constant

I was recently introduced to Kaperkar’s Constant.
It is quite magical. You take any four digit number $A$, sort the digits from highest to lowest to create a new number $A^{\text{high}}$, sort the digits from lowest to highest to get $A^{\text{low}}$, and calculate and new number $A= A^{\text{high}}- A^{\text{low}}$. You repeat this procedure enough times and you end up with $A=6174$.

I made a nifty implementation of that in Julia below.

05/2/15

# Monkeys and Coconuts

Here is my attempt to solve the monkeys and coconuts problem. The story goes
that five sailors were stranded on an island and had decided to gather some
coconuts for their provisions. They put all the coconuts in one large pile and
went to sleep. One sailor got up and fearing that there could be problems when the
time to came to divide the pile, he divided the pile five ways and noticing that
he has an extra coconut, he gave it to a monkey, and then hid his stash. The other
four sailor repeated the same procedure. When they woke up they noticed that
they had a smaller pile and proceeded to divide into five equal piles, this time
around there were no extra coconut left for the monkey. So the question is: what was
the size of the original pile?

We will denote by $x_i$ as the size of the
pile after the $i$th sailor has carried out his procedure. In this system $x_0$
is original size of the pile. So following this procedure we then proceed as
follows:

$x_1=\frac{4(x_0-1)}{5}$
$\cdots$
$x_i=\frac{4(x_{i-1}-1)}{5}$
$\cdots$
$x_5=\frac{4(x_4-1)}{5}$

It is important to note that $x_i \in \mathbb{N}_0$ for $i=0\ldots5$, also $\frac{x_5}{5}\in \mathbb{N}_0$. Alternatively, we think of the reverse procedure and express the above as
$x_4=\frac{5x_5}{4}+1$
$\cdots$
$x_i=\frac{5x_{i+1}}{4}+1$
$\cdots$
$x_0=\frac{5x_1}{4}+1$

Observing that $x_5$ has to be divisible by 4 and 5 (last equation in the first system and first equation in the second), one can brute force the solution(s) by the following Julia code:

Which results in

[2496,1996,1596,1276,1020]
m=51, x₀= 3121
[14996,11996,9596,7676,6140]
m=307, x₀= 18746
[27496,21996,17596,14076,11260]
m=563, x₀= 34371
[39996,31996,25596,20476,16380]
m=819, x₀= 49996


This corresponds nicely to the answers that were obtained by rigorous derivation in the video, however it shows how programming can easily find such solutions by brute force.

If one would like to avoid the negative or blue concocts suggested in the video and also preserve the monkey. Below is an alternative derivation. Working through the first system, one gets:

$x_{5}={{4\,\left({{4\,\left({{4\,\left({{4\,\left({{4\,\left(x_{0}- 1\right)}\over{5}}-1\right)}\over{5}}-1\right)}\over{5}}-1\right) }\over{5}}-1\right)}\over{5}}=\frac{4^5x_0}{5^5}-\frac{8404}{5^5}$

Hence,
$5^5x_5=4^5x_0-8404$.
Realizing the $x_5$ has to be necessarily divisible by 5, we denote the final share that each sailor gets in the last division by $s$. So our Diophantine equation becomes
$5^6s=4^5x_0-8404$.
It will have solutions at $x_0=3121, 18746, 34371 \ldots= 3121+n5^6 \text{ for } n \in \mathbb{N}_0$.

03/3/10

# Mathematical Distractions

While attending a conference and hanging out with a college later in the day he recounted to me a nice mathematical puzzle that he learned while growing up in Algeria.
Puzzle 1:
Three brothers were in a bind, they could not divide their father’s inheritance. Their father has left them with 17 camels and instructions that half of his camels should go to eldest son, a third to the second eldest, and a ninth to the youngest. Knowing that there is no point in having a fraction of camel, they wanted a solution that would be fair and yet that would result in no fractional division of camels. A passerby, seeing their predicament proposed to help.
In order to resolve their problem he donated his camel to the pool of 17 camels, bring thus the total to 18. One brother will hence get 9 camels, the other would get 6, and the youngest would get 2 camels. But 9+6+2=17, therefore the passerby concluded that he would walk away with the left over camel. Problem solved… but how?
Puzzle 2:
Two farmers decided to sit down and share their lunch food. One farmer had on him five loafs of bread and the other had seven loafs. Just as they were about to start, a passerby asked if he could join them. The three sat down and they equally divided the twelve loafs amongst themselves. When they were done, the passerby give them twelve dirhams for the meal. The farmer who contributed five loafs proposed that a fair division of that amount would be for him to take five dirhams and for the other farmer to take the remaining seven. A disagreement ensued. They sought the assistance of judge to resolve their disupte and his resolution was that the farmer who contributed five loafs should only walk away with three dirhams, while the other should have the remaining nine dirhams. How is this fair?