Inequality Study 47th file   Update 2010-07-09
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The Cauchy-Schwarz Master Class   J. Michael Steele   ★★★★★
This file is personal home work. No one
proofread. Cannot promise correctness.
If you suspect any view point wrong,
please ask a math expert near by.
Freeman 2009-06-19-10-46

Please use MSIE browser to read this file.
Did not test other browser. This file is
written under MSIE 6.0

<a name="docA001"> Index begin Index this file
2009-06-08-19-10 start
This file
is Freeman's reading notes. Although Freeman
always keep correct view point. But Freeman's
capability is limited, plus no one proofread
this file. Then you can still find wrong view
point. When read, please put question mark as
often as possible. If you suspect any view 
point wrong, please ask a math expert near by.

<a name="docA002">,<a name="textbook">
This file is a note for reading inequality
book written by Professor J. Michael Steele
The Cauchy-Schwarz Master Class   ★★★★★
Below use 'textbook' as abbreviation.
Freeman also read web pages online, and will 
indicate the source URL at discussion point.

<a name="docA003">
Freeman study mechanical engineering. 
Engineering mathematics do not teach 
inequality. Above book is first inequality
book. First time read, it was very hard.
Although high school time learned
  a*a + b*b >= 2*a*b
But this little knowledge do not help.

<a name="docA004">
This file follow textbook chapter section
order, but not continuous, Freeman skip 
those uncertain sections/problems.

This file first function is to learn 
inequality. Second function is to learn
how to use html code to write math 

<a name="docA005"> Index begin Index this file
On 2009-01-27-10-08 Freeman accessed
the next page
save as sftw.umac.mo_text_math_eqn_good.htm
Above page is the main reference for html
math equation.

In order to let reader to build  html math
equation, previous file tute0007.htm page 
end has math symbol and internal code.

This file third function is to display how
to draw curves in web page. The main engine
is XYGraph v2.3 - Technical Figures

Thank you for read Freeman's inequality page.
2009-06-08-19-47 stop

<a name="docA006">
2009-08-23-15-00 start
□ Exercise 1.14 solution

Exercise 1.14 problem (a) is solved by hint
Exercise 1.14 problem (b) is out of LiuHH's
reach. What is "cardinality of a set B⊂Z3" ?
Without clear understand the definition, 
LiuHH is unable to solve problem (b)
LiuHH major in mechanical engineering.
LiuHH is mathematics admirer and outsider.

<a name="docA007">
To solve problems in
The Cauchy-Schwarz Master Class
LiuHH's goal is to solve 50% of the exercise
problems. In reality, solve 40% is good enough.
Please do not be surprise that LiuHH skip some
text section, skip some problems.
2009-08-23-15-12 stop

<a name="ch13b001"> Index begin Index this file
2010-07-01-11-30 start
■ Problem 13.3 
 [The HLP Representation
  α(<)β implies α=Dβ]
jk0 jkm best rng bjk trap HLP DSM prf 
<a name="ch13b002">
Show that α(<)β implies that 
there exists a doubly stochastic 
matrix D such that 
  α=Dβ ---eqn.BO001 //example 1 2
Hardy, Littlewood and Polya came 
to this result because of their
interests in mathematical 
inequalities. but, ironically,
<a name="ch13b003">
the concept of majorization was
originally introduced by
economists who were interested 
in inequalities of a different
sort -- the inequality of income
which one finds in our society.
<a name="ch13b004">
Today, the role of majorization
in mathematics far outstrips the 
role in economics, but consideration
of income distribution can still
add to our intuition.
2010-07-01-11-45 stop

<a name="ch13b005">
2010-07-01-11-47 start
Now consider two sequences
α seq: 4 3.2  3  2.8  2  ---eqn.BO002
β seq: 6 4.4  3  0.9 0.7 ---eqn.BO003
They have majorization relation.

<a name="ch13b006"> Index begin Index this file
What is majorization relation?
In simple words:
Every element of α seq is an
average of β seq.
Every element of α seq is an
interpolation of β seq.
<a name="ch13b007">
If one element of αv seq need
extrapolation of βv seq.
then αv and βv do NOT have 
majorization relation.

<a name="ch13b008">
There are two methods to exam 
majorization relation between 
α_seq. and β_seq.

<a name="ch13b009">
Majorization definition first
version is interpolation eqn.
It is called //2nd version
"Muirhead's condition"
which is  //numerical example
  α∈H(β)       ---eqn.BN120
  α=D*β        ---eqn.BN121

<a name="ch13b010">
Majorization definition second
version is successive inequality.
It is called //1st version
"partial sum inequality"
which is  //numerical example
           α[1]≦β[1]           ---eqn.BO004
      α[1]+α[2]≦β[1]+β[2]      ---eqn.BO005
 α[1]+α[2]+α[3]≦β[1]+β[2]+β[3] ---eqn.BO006
 ∑[j=1,n-1]α[j]≦∑[j=1,n-1]β[j] ---eqn.BO007
and final equality
  ∑[j=1,n] α[j]=∑[j=1,n] β[j] ---eqn.BO008

<a name="ch13b011"> Index begin Index this file
Numerical example can help reader
to understand better. Next use
α seq: 4 3.2  3  2.8  2  ---eqn.BO002
β seq: 6 4.4  3  0.9 0.7 ---eqn.BO003
to illustrate two Majorization

<a name="ch13b012">
First definition "Muirhead's 
condition" for α_seq. and β_seq
the definition is
  α=D*β        ---eqn.BN121
in specific form, it is .....
(prepare example)
2010-07-01-12-14 stop

<a name="ch13b013">
2010-07-01-15-37 start
Please goto the Majorization 
transformation page (local)
Click [α,ß in] link, next
click [17] example button
click [Run Stoch] button
click [box 7] button

<a name="ch13b014">
"Bx7 stochastic mat" has next 
matrix M17
0.588628762541806  0.044147157190635465  0  0.08461538461538463  0.2826086956521739  
0  0.6571428571428571  0  0.3428571428571429  0  
0  0  1  0  0  
0.17948717948717951  0.2813186813186814  0  0.5391941391941392  0  
0.23188405797101447  0.01739130434782609  0  0.03333333333333334  0.717391304347826  

<a name="ch13b015">
Above is matrix in decimal number
Convert decimal number to quotient
number, get
176/299    66/1495  0   11/130  13/46 
   0        23/35   0   12/35    0
   0         0      1     0      0  
  7/39    128/455   0  736/1365  0
 16/69      2/115   0    1/30   33/46
<a name="ch13b016"> Index begin Index this file
Please verify that quotient matrix
each row sum to one
each column sum to one
All matrix elements are 
2010-07-01-15-53 stop

<a name="ch13b017">
2010-07-01-18-55 start
For this specific case
  α=D*β        ---eqn.BN121
4  =6*176/299 +4.4*66/1495 +3*0 +0.9*11/130  +0.7*13/46 
    // above line ---eqn.BO009
<a name="ch13b018">
3.2=    6*0   +4.4*23/35   +3*0 +0.9*12/35   +0.7*0
    // above line ---eqn.BO010
3  =    6*0   +4.4*0       +3*1 +0.9*0       +0.7*0
    // above line ---eqn.BO011
2.8=  6*7/39  +4.4*128/455 +3*0 +0.9*736/1365+0.7*0
    // above line ---eqn.BO012
2  =  6*16/69 +4.4*2/115   +3*0 +0.9*1/30    +0.7*33/46 
    // above line ---eqn.BO013
<a name="ch13b019">
eqn.BO009 to eqn.BO013 are
Muirhead's condition
Above equations show that each
element of α is an average of 
elements of β .
<a name="ch13b020">
α seq: 4 3.2  3  2.8  2  ---eqn.BO002
β seq: 6 4.4  3  0.9 0.7 ---eqn.BO003
All coefficient (quotient) are in
the domain [0,1].
Coefficients in each line sum to one.
That is average, that is interpolation.

<a name="ch13b021"> Index begin Index this file
Successive inequality for α seq. 
and β seq, eqn.BO004 to eqn.BO008
their numerical values are next
       α seq. ≦ β seq.
            4 ≦ 6            ---eqn.BO004B
      4 + 3.2 ≦ 6 + 4.4      ---eqn.BO005B
  4 + 3.2 + 3 ≦ 6 + 4.4 + 3  ---eqn.BO006B
  4+3.2+3+2.8 ≦ 6+4.4+3+0.9  ---eqn.BO007B
 4+3.2+3+2.8+2=6+4.4+3+0.9+0.7 ---eqn.BO008B
<a name="ch13b022">
that is
α seq. ≦ β seq.
     4 ≦ 6     ---eqn.BO004C
   7.2 ≦ 10.4  ---eqn.BO005C
  10.2 ≦ 13.4  ---eqn.BO006C
  13.0 ≦ 14.3  ---eqn.BO007C
  15   =  15   ---eqn.BO008C

<a name="ch13b023">
Majorization definition 
first version is interpolation, and
second ver. is successive inequality
Are they equal? 
Can they imply each other?

<a name="ch13b024">
Previous Problem 13.2 discuss
Muirhead Implies Majorization
that is (given) coeff. matrix
implies successive inequality,
or given interpolation eqn.
ask to prove successive ineq.
Numerical example for Problem 13.2
give a solid explanation for one
specific case n=4, k=3.
<a name="ch13b025">
//Be alert about the difference !
Current Problem 13.3 discuss
Reverse case: (given) successive 
inequality implies coeff. matrix
or given successive ineq.
ask to prove interpolation eqn.
Problem 13.3: HLP Representation
Problem 13.3: α(<)β implies α=Dβ

<a name="ch13b026"> Index begin Index this file
All above said is ask reader pay
attention to the difference
between Problem 13.2 and 
Problem 13.3 They are reverse
to each other.
2010-07-01-19-56 stop

<a name="ch13b027">
2010-07-01-22-31 start
The task is transform from β seq.
to α seq. Both α seq. and β seq
have same total sum 15. The goal
is to find a doubly stochastic 
matrix D (M17) such that α=D*β 
is true. 
<a name="ch13b028">
We do average two pair data at 
one time.
α seq. is goal and never change. 
β seq. β[j] reduce, β[k] increase.
<a name="ch13b029">
jk0 jkm best rng bjk trap HLP DSM prf 
We use // non-best j,k
α seq: 4  3.2  3  2.8  2  ---eqn.BO002
β seq: 6  4.4  3  0.9 0.7 ---eqn.BO003
index  0   1   2   3   4
as numerical example. 
Assume j=0 (red) and k=3 (blue)
j=0, k=3 is not the best choice.
Before talk best, we mention the
non-best. //why non-best?
<a name="ch13b030">
Both sequences are descending.
α[0]= 4> α[1]=3.2> ...> α[4]=2
β[0]= 6> β[1]=4.4> ...> β[4]=0.7
<a name="ch13b031"> Index begin Index this file
Please pay attention to index=2
α[2]=3=β[2] both α[2] and β[2]
are equal. 
Equal middle elements is NOT
<a name="ch13b032">
Index=0 and Index=1 are higher
value side. Here α[j]<β[j]
(example α[0]=4<6=β[0])
Index=3 and Index=4 are lower
value side. Here α[k]>β[k]
(example α[3]=2.8>0.9=β[3])
<a name="ch13b033"> define j,k
Use 'j' for higher side index.
Use 'k' for  lower side index.
We have
  j<k     (0 < 3) j,k are index
α[j]>α[k] ( 4 >2.8)
β[j]>β[k] ( 6 >0.9)
α[k]>β[k] (2.8>0.9)
α[j]<β[j] ( 4 < 6 )
<a name="ch13b034">
j,k are index. (j,k are pure number)
 α[j] and β[j] are observed data.
(α[j] and β[j] are observed length)
Higher value side determine j only.
 Lower value side determine k only.
In eqn.BO002 and eqn.BO003
j has two choices, j=0 or j=1
k has two choices, k=3 or k=4
//colored example
<a name="ch13b035">
If no equal middle elements, 
what α seq. and β seq look like?
In jsmajor2.htm click example 53
button, get
α53 seq:  5   3   3   2   1   1
β53 seq:  6  2.8 2.8 1.8 1.5 0.1
index     0   1   2   3   4   5
<a name="ch13b036"> Index begin Index this file
There is no equal middle elements
no equal value  left end elements
no equal value right end elements
It is still easy to see
j section is index 0 (α[j]<β[j])
k section is 1 to 5  (α[k]>β[k])

<a name="ch13b037">
Equal value  left end elements and
equal value right end elements 
can be dropped out of analysis.
They contribute nothing. (they
are done) For example, if run
α53 seq and β53 seq
<a name="ch13b038">
First iteration change
β_old   6  2.8  2.8  1.8  1.5  0.1 
β_new   5.8 3   2.8  1.8  1.5  0.1 
α_fix   5   3    3    2    1    1  
<a name="ch13b039">
Fourth iteration
β_new   5   3    3    2   1.5  0.5  
β_old  5.4  3    3    2   1.5  0.1  
Fifth iteration, above purple data
are ignored. We only need average
between red and blue. Leading equal 
value terms dropped out of analysis.
(Leading equal numbers are done)
2010-07-01-23-41 stop

<a name="ch13b040"> Index begin Index this file
2010-07-02-12-02 start
■ Index j,k determination
Next nine lines are link.
non-best j,k example
define j-range k-range m-range 
define best j,k pair
j,k,m-range example
best j,k example
trap if use wrong j,k
Hardy, Littlewood, Polya matrix
define doubly stochastic matrix 
proof HLP matrix is doubly s. m.

<a name="ch13b041">
Liu,Hsinhan write a program (local)
to find doubly stochastic matrix
for two given sequences α and β seq
<a name="ch13b042">
Initially LiuHH code j,k value with
the easiest method (wrong), set
j to left  end of index range,
k to right end of index range.
This setting work for some α,β seq
but must use permutation matrix to
re-order new β seq to descending 
<a name="ch13b043">
Trouble is that some other α,β seq
have majorization relation but get 
negative matrix element, which is 
an error. Gradually, LiuHH realize 
that index j,k determination is 
LiuHH spend more time on the
function jkcheck().
More document is here.

<a name="ch13b044">
Back to non-best j,k example
α seq: 4  3.2  3  2.8  2  ---eqn.BO002
β seq: 6  4.4  3  0.9 0.7 ---eqn.BO003
index  0   1   2   3   4  Why 
j=0, k=3 is not the best choice?
Let us do the calculation.
<a name="ch13b045"> Index begin Index this file
RobinHood transformation average
action let β[0]= 6 reduce 2 to 4
to match α[0]= 4  then 
β[3]=0.9 must add 2 to 2.9
Because β seq total sum be 15.
<a name="ch13b046">
The old/new β seq are
β old: 6   4.4  3  0.9  0.7
β new: 4   4.4  3  2.9  0.7
β sort 4.4  4   3  2.9  0.7
α seq: 4   3.2  3  2.8  2 
From blue β new to red β sort, we
need sort β seq. Sort dislocate
blue 4 and black 4. Which can be 
avoid if we choose best j,k pair.
<a name="ch13b047">
jk0 jkm best rng bjk trap HLP DSM prf 
α and β are in descending order.
smaller index has higher value.
Define j-range be the index range
where α[j]<β[j] //example
Define k-range be the index range
where α[k]>β[k]. (with surprise
α[k]<β[k] see α[4]=1<1.5=β[4])
<a name="ch13b048">
Define m-range be the index range
where α[m]=β[m] for 0≦j<m<k≦n
'm' mean middle. 'n' is dimension.
All three ranges can be undefined.
If α≡β, all three ranges are empty
and problem is done.
<a name="ch13b049">
jk0 jkm best rng bjk trap HLP DSM prf 
What is the best j,k pair?
Choose j-range right end for j
Choose k-range  left end for k
Let j,k be as close as possible.
No need to sort updated β seq.
That is best j,k pair.//j,k range

<a name="ch13b050"> Index begin Index this file
jk0 jkm best rng bjk trap HLP DSM prf 
j,k,m range example
α seq: 4  3.2  3  2.8  2  ---eqn.BO002
β seq: 6  4.4  3  0.9 0.7 ---eqn.BO003
index  0   1   2   3   4
Red  columns j-range α[j]<β[j]
Blue columns k-range α[k]>β[k]
Black column m-range α[m]=β[m]

<a name="ch13b051">
Program jsmajor2.htm 
function jkcheck() use
jl for j-range  left bound (jl=0)
jl=i; //9906232226 ←time stamp

jr for j-range right bound (jr=1)
jr=i; //9906232231

<a name="ch13b052">
kl for k-range  left bound (kl=3)
kl=i; //9906232241

kr for k-range right bound (kr=4)
kr=i; //9906232245

<a name="ch13b053">
Four time stamp lines are code 
line in jsmajor2.htm 
Four (jl=0) etc are values in
example eqn.BO002 and eqn.BO003 
<a name="ch13b054">
In this example, determined
 j-range be [0,1]
 k-range be [3,4] then
Choose j-range right end j=jr=1
Choose k-range  left end k=kl=3
The best choice for j,k in the
example eqn.BO002 and eqn.BO003 
j=1 and k=3 (NOT j=0 and k=3 )

<a name="ch13b055"> Index begin Index this file
jk0 jkm best rng bjk trap HLP DSM prf 
Back to example for best j,k
α seq: 4  3.2  3  2.8  2  ---eqn.BO002
β seq: 6  4.4  3  0.9 0.7 ---eqn.BO003
index  0   1   2   3   4 
We average between red and blue. 

<a name="ch13b056">
RobinHood transformation average
action β[1]=4.4 reduce 1.2 to 3.2
to match α[1]= 3.2  then 
β[3]=0.9 must add 1.2 to 2.1
Because β seq total sum be 15.
<a name="ch13b057">
The old/new β seq are
β old: 6   4.4  3  0.9  0.7
β new: 6   3.2  3  2.1  0.7
β sort 6   3.2  3  2.1  0.7
α seq: 4   3.2  3  2.8  2 
<a name="ch13b058">
From blue β new to red β sort, 
we create an identity matrix as
permutation matrix. which in
effect is no sort.

<a name="ch13b059">
Next iteration, new problem is
α seq: 4   3.2  3  2.8  2 
β sort 6   3.2  3  2.1  0.7
index  0    1   2   3    4  
There are two equal value columns
marked purple color.
<a name="ch13b060"> Index begin Index this file
In this updated problem, determined
 j-range be [0,0]
 k-range be [3,4] then
Choose j-range right end j=0
Choose k-range  left end k=3
average between index=0 and 3.

<a name="ch13b061">
Can you fill in the following
detail steps for LiuHH? I need 
a break. Thank you very much.

Please goto jsmajor2.htm
click [17], click [run stoch]
then peek [Box 3 β history]
for hint.
2010-07-02-13-08 stop

<a name="ch13b062">
jk0 jkm best rng bjk trap HLP DSM prf 
2010-07-02-16-57 start
Best j,k pair is defined.
If violate best j,k definition
there is one case it may trap 
a programmer.
Please goto jsmajor2.htm
click [52] get the following
α52 seq:  5  3  2  1  1 ---eqn.BO014
β52 seq:  6  2  2  2  0 ---eqn.BO015
index     0  1  2  3  4
<a name="ch13b063">
Here the equal middle element is
index 2 and α52[2]=β52[2]=2. Then
m-range is [2,2], ml=mr=2
Base on m-range, define //error
get j,k pair j=1, k=3.  //error
<a name="ch13b064">
Average the following problem
α52 seq:  5  3  2  1  1 
β52 seq:  6  2  2  2  0 
index     0  1  2  3  4 
<a name="ch13b065"> Index begin Index this file
Early α[j]+α[k]=6≠5.3=β[j]+β[k]
Here  α[j]+α[k]=3+1=2+2=β[j]+β[k]
Look like one stone two birds.
One average two data equality.
Is it a better situation?
<a name="ch13b066">
LiuHH trapped in example 52 for
a while. Because 'average' β52
middle '2 2 2' to α52 middle
'3  2  1' ? That is not average!!
That is diverse. 
<a name="ch13b067">
If average '3 2 1' to '2 2 2'
Drop the middle like number '2'
Average '3  1' to '2  2'
the code generate
[2]=[0.5 0.5]*[3]
[2] [0.5 0.5] [1]
which has no trouble.
<a name="ch13b068">
Now need 'average' [2 2] to [3 1]
Find the inverse matrix
such that
[3]=[ s  t ]*[2]
[1] [ u  v ] [2]
<a name="ch13b069"> Index begin Index this file
BUT !! this is impossible !!
Because determinant value of 
matrix [[0.5 0.5], [0.5 0.5]]
is ZERO !! There is no inverse 
matrix. After this difficulty,
<a name="ch13b070">
LiuHH gradually understand that
old j,k determination was wrong.
Start write new code for correct
j,k determination. Please see
comments in program jsmajor2.htm 
function jkcheck()

<a name="ch13b071">
Why it is wrong ? For
α52 seq:  5  3  2  1  1 
β52 seq:  6  2  2  2  0 
index     0  1  2  3  4
j range is [0,0]
k range is [1,4]
set j,k pair j=1, k=3 is wrong
set j,k pair j=0, k=1 is right.
2010-07-02-17-40 stop

<a name="ch13b072"> Index begin Index this file
2010-07-02-18-56 start
■ The simplest case n=2

No matter how complicate the
α,β seq are. Each iteration
average two pairs data. 
This section title is
"The simplest case n=2"
which is actually the center 
part of Problem 13.3.
<a name="ch13b073">
Now we have 
α sequence = [α1, α2] and 
β sequence = [β1, β2] 
We want to find a 2x2 matrix 
such that
[α1]=[m11 m12]*[β1]  ---eqn.BO016
[α2] [m21 m22] [β2]
<a name="ch13b074">
Please remember Problem 13.3 
given successive inequality and 
ask to find matrix D such that
  α=Dβ ---eqn.BO001 //example
For numerical example, take
best j,k from eqn.BO002.
<a name="ch13b075">
That is 
[α1]=[3.2]  ---eqn.BO017
[α2] [2.8]
we have α1>α2
[β1]=[4.4]  ---eqn.BO018
[β2] [0.9]
we have β1>β2
eqn.BO016 become
[3.2]=[m11 m12]*[4.4]  ---eqn.BO019
[2.8] [m21 m22] [0.9]

<a name="ch13b076">
Average value of α1 and α2 is
ρα=(3.2+2.8)/2=3  ---eqn.BO020
Average value of β1 and β2 is
ρβ=(4.4+0.9)/2=2.65 ---eqn.BO021
Textbook choose ρβ as parameter 
ρ and define rho
  ρ=(β1+β2)/2=(4.4+0.9)/2=2.65 ---eqn.BO022
(textbook page 199 line 20)
<a name="ch13b077"> Index begin Index this file
Define tau as radius from ρ
to either β1 or β2
  τ=β1-ρ ---eqn.BO023
Numerical value is
  τ=4.4-2.65=1.75 ---eqn.BO024
<a name="ch13b078">
ρ definition is based on ρβ
(not base on ρα) then 
β1 and β2 are perfect symmetry 
from ρ . From eqn.BO023
  β1=ρ+τ=2.65+1.75=4.4 ---eqn.BO025
  β2=ρ-τ=2.65-1.75=0.9 ---eqn.BO026
Above is β to ρ relation.
<a name="ch13b079">
Next see α to ρ relation.
ρ definition is not based on ρα
ρ is not the average value of 
α1, α2. There is no symmetry 
of α1 and α2 from ρ. Define
sigma as
  σ=max(|α1-ρ|,|α2-ρ|) ---eqn.BO027
(textbook page 199 line -9)
<a name="ch13b080">
Numerical value is
   =0.55 ---eqn.BO028
  ρ+σ=2.65+0.55=3.2=α1 ---eqn.BO029
  ρ-σ=2.65-0.55=2.1≠α2 ---eqn.BO030
<a name="ch13b081"> Index begin Index this file
If we choose one example such
  α1+α2=β1+β2 ---eqn.BO031
then we will have ρ-σ=α2. One
average operation bring two
numbers to equality.

<a name="ch13b082">
But α1+α2=β1+β2 is not required.
The example
α seq: 4  3.2  3  2.8  2  ---eqn.BO002
β seq: 6  4.4  3  0.9 0.7 ---eqn.BO003
index  0   1   2   3   4 
was chosen carefully avoid the
special case α1+α2=β1+β2
2010-07-02-19-54 here

<a name="ch13b083">
Hardy, Littlewood and Polya 
found that 
  D*β = α  ---eqn.BO032
has following matrix form
<a name="ch13b084"> Next is D*β = α
jk0 jkm best rng bjk trap HLP DSM prf
Hardy, Littlewood and Polya Representation




Square D matrix transform old β seq. to new β seq.
Blue is old β more diverse. purple is new β more
average. If α1+α2=β1+β2 then new β seq. is α seq.
---page 199 ---line 2 ---eqn.13.14 definition prove
width of above equation
<a name="ch13b085"> Index begin Index this file
2010-07-02-20-38 here
The following is copied from old 
work, which is to expand eqn.13.14
verify equation left side equal
right side.
<a name="ch13b086">
2010-06-17-17-43 CSMC page 199 eqn.13.14
 (τ+σ)*(ρ+τ)/(2τ) + (τ-σ)*(ρ-τ)/(2τ) 
=[(τ+σ)*(ρ+τ) + (τ-σ)*(ρ-τ)]/(2τ) 
=[(τρ+σρ+ττ+στ) + (τρ-σρ-ττ+στ)]/(2τ) 
=[(τρ+στ) + (τρ+στ)]/(2τ) 
=ρ+σ  ---eqn.BO033
<a name="ch13b087">
  (τ-σ)*(ρ+τ)/(2τ) + (τ+σ)*(ρ-τ)/(2τ) 
=[(τ-σ)*(ρ+τ) + (τ+σ)*(ρ-τ)]/(2τ) 
=[(τρ-σρ+ττ-στ) + (τρ+σρ-ττ-στ)]/(2τ) 
=[(τρ-στ) + (τρ-στ)]/(2τ) 
=ρ-σ  ---eqn.BO034

<a name="ch13b088">
2010-07-02-20-46 here
  α1+α2=β1+β2 ---eqn.BO031
then eqn.13.14 is perfect.
HLP representation eqn.13.14
is one stone two birds. Bring
two β elements = α elements

<a name="ch13b089"> Index begin Index this file
But, α1+α2=β1+β2 is not required.
Many majorization sequences do
not have α1+α2=β1+β2 relation.
In this case, HLP representation 
is one stone one bird. Bring
one β element = α element.

<a name="ch13b090">
We find doubly stochastic matrix
for two given sequences, do two
pairs at each iteration. Then
multiply all transformation
matrix to a final matrix. see
eqn.BO181. That is our answer.

<a name="ch13b091">
There is a theorem say that
doubly stochastic matrix A
doubly stochastic matrix B
the result is still a
doubly stochastic matrix. 
This theorem give our process
a solid foundation.

<a name="ch13b092">
Liu,Hsinhan's work is just fill
the details. Most time correct.
Some time error.
Please read textbook.
Professor J.M. Steele give you
better explanation.
You can find figure 13.1 and 
matrix equation 13.16 in textbook
not here in this page.
2010-07-02-21-09 stop

<a name="ch13b093">
2010-07-02-21-35 start
Compare eqn.13.14 
[α1]=[m11 m12]*[β1]  ---eqn.BO016
[α2] [m21 m22] [β2]
m11=(τ+σ)/(2τ) ---eqn.BO035
m12=(τ-σ)/(2τ) ---eqn.BO036
m21=(τ-σ)/(2τ) ---eqn.BO037
m22=(τ+σ)/(2τ) ---eqn.BO038

<a name="ch13b094"> Index begin Index this file
Question is that eqn.BO016 has
index 1 and 2. If j≠1 and k≠2
what to do?
Actually we should view eqn.BO016
[αj]=[mjj mjk]*[βj]  ---eqn.BO039
[αk] [mkj mkk] [βk]
here, j and k are index.
<a name="ch13b095">
All other non-j and non-k rows
columns are all one for [m,m]
and all zero for [m,n] m≠n.
Textbook page 200 equation 13.16
illustrate this point.

<a name="ch13b096">
Textbook page 200 figure 13.1 is
a diagram for relative magnitude
of the following numbers.
αj ≦ ρ+σ
Please read textbook for better 
2010-07-02-21-48 stop

<a name="ch13b097">
2010-07-03-09-48 start
Program jsmajor2.htm output box 7
give us doubly stochastic matrix.
Whether eqn.13.14 is also a doubly 
stochastic matrix?

<a name="ch13b098"> Index begin Index this file
jk0 jkm best rng bjk trap HLP DSM prf 
A doubly stochastic matrix satisfy
the following three properties.
(1) all elements be non-negative 
(2) all column sum to one
(3) all   row  sum to one
(textbook page 196 line 1 to 5)
A permutation matrix is a doubly 
stochastic matrix.
An identity matrix is a doubly 
stochastic matrix.

<a name="ch13b099">
Let us review eqn.13.14 matrix
elements column sum and row sum.
Four elements only two flavors
  m0=(τ-σ)/(2τ) ---eqn.BO040
  m1=(τ+σ)/(2τ) ---eqn.BO041
m0 is off diagonal [j,k] and [k,j]
m1 is on  diagonal [j,j] and [k,k]
<a name="ch13b100">
Adding result is
  m0+m1=(2τ)/(2τ)=1 ---eqn.BO042
Any row sum or column sum are just
m0+m1. Then row sum to one and
column sum to one. 

<a name="ch13b101">
Matrix elements be non-negative
requirement couple with negative 
data sequence that is a point
need more time to check.

<a name="ch13b102"> Index begin Index this file
Next check positive/negative 
property for m0 and m1. 
To check, need two numbers τ 
and σ. They are defined next
  τ=β1-ρ ---eqn.BO023
  σ=max(|α1-ρ|,|α2-ρ|) ---eqn.BO027
  ρ=(β1+β2)/2  ---eqn.BO022
<a name="ch13b103">
Majorization theory require
transformation matrix elements
all be non-negative, but do 
not require α seq and β seq.
In other words, α seq. β seq
can have negative elements.
<a name="ch13b104">
For α=[α1,α2] β=[β1,β2], 
α is more average, 
β is more diverse.
Assume the following order
β1 ≧ α1 ≧ α2 ≧ β2 ---eqn.BO043
jsmajor2.htm example 14 iter=1
 4 ≧  3 ≧  2 ≧ 1 //all positive
jsmajor2.htm example 15 iter=1
-1 ≧ -3 ≧ -3 ≧-4 //all negative
Main concern is negative data.

<a name="ch13b105">
For positive  4 ≧ 3 ≧ 2 ≧ 1 ---eqn.BO044
  ρ=(β1+β2)/2=(4+1)/2=2.5>0 ---eqn.BO045
  τ= β1-ρ = 4-2.5 =1.5>0 ---eqn.BO046
  σ=max(|α1-ρ|,|α2-ρ|)  ---eqn.BO047
<a name="ch13b106"> Index begin Index this file
  σ=0.5>0 ---eqn.BO048
  m0=(τ-σ)/(2τ)=(1.5-0.5)/(3)>0 ---eqn.BO049
  m1=(τ+σ)/(2τ)=(1.5+0.5)/(3)>0 ---eqn.BO050
For positive α=[α1,α2] β=[β1,β2], 
we find m0>0 and m1>0

<a name="ch13b107">
For negative -1≧-3 ≧-3 ≧-4 ---eqn.BO051
  ρ=(β1+β2)/2=(-1-4)/2=-2.5<0 ---eqn.BO052
  τ= β1-ρ =-1-(-2.5) =1.5>0 ---eqn.BO053
  σ=max(|α1-ρ|,|α2-ρ|)  ---eqn.BO054
<a name="ch13b108">
  σ=0.5>0 ---eqn.BO055
  m0=(τ-σ)/(2τ)=(1.5-0.5)/(3)>0 ---eqn.BO056
  m1=(τ+σ)/(2τ)=(1.5+0.5)/(3)>0 ---eqn.BO057
For negative α=[α1,α2] β=[β1,β2], 
we find m0>0 and m1>0

<a name="ch13b109">
Numerical result tell us that 
matrix elements m0 and m1 are 
positive for both 
all positive α seq. and β seq
all negative α seq. and β seq
2010-07-03-10-46 stop

<a name="ch13b110"> Index begin Index this file
jk0 jkm best rng bjk trap HLP DSM prf 
2010-07-03-15-15 start
■ Prove HLP matrix is doubly 
Numerical check build confidence.
Numerical check is not a proof.
Because there are infinite many
numerical cases.
Symbolic equation is a proof.
Because symbol can be replaced
with any number.

<a name="ch13b111">
ρ is defined to be the average 
of β1 and β2.
  ρ=(β1+β2)/2  ---eqn.BO022
Assumed the following order
β1 ≧ α1 ≧ α2 ≧ β2 ---eqn.BO043
  β1 ≧ ρ ≧ β2 ---eqn.BO058
<a name="ch13b112">
τ is defined as next
  τ=β1-ρ ---eqn.BO023
because β1 ≧ ρ, then
  τ must ≧0 ---eqn.BO059
for any beta sequence.
for negative beta sequence too.
See eqn.BO053

<a name="ch13b113">
Next see σ
  σ=max(|α1-ρ|,|α2-ρ|) ---eqn.BO027
Above definition, let
  σ≧0  ---eqn.BO060
with the order
β1 ≧ α1 ≧ α2 ≧ β2 ---eqn.BO043
ρ is mid point of β1, β2.
<a name="ch13b114">
Insert ρ to eqn.BO043, it could 
β1 ≧ ρ ≧ α1 ≧ α2 ≧ β2 ---eqn.BO061
β1 ≧ α1 ≧ ρ ≧ α2 ≧ β2 ---eqn.BO062
β1 ≧ α1 ≧ α2 ≧ ρ ≧ β2 ---eqn.BO063
<a name="ch13b115"> Index begin Index this file
no matter which one is true,
  σ must be ≦ τ ---eqn.BO064
because τ is greater radius
  and   σ is shorter radius
(ρ is center, α and β are 
Combine eqn.BO059,BO060,BO064
we find 
  0 ≦ σ ≦ τ ---eqn.BO065
<a name="ch13b116">
Finally goto
  m0=(τ-σ)/(2τ) ---eqn.BO040
  m1=(τ+σ)/(2τ) ---eqn.BO041
With eqn.BO065 in hand, 
eqn.BO040 and eqn.BO041 tell 
us that matrix element m0,m1
are both non-negative.
<a name="ch13b117">
Even if α seq. and β seq have
negative elements, m0,m1 still
be non-negative.

<a name="ch13b118">
We just proved that matrix D 
has all non-negative elements.
Review definition of doubly 
stochastic matrix and the easy
condition eqn.BO042
We conclude that D matrix in
eqn.13.14 is a doubly stochastic 

<a name="ch13b119"> Index begin Index this file
Chicken give birth to chicken,
rabbit  give birth to rabbit ,
doubly stochastic give birth to 
doubly stochastic , that is a
matter of course. suspect ?
That is why we get doubly 
stochastic matrix in 
jsmajor2.htm output box 7.
2010-07-03-15-57 stop

2010-07-03-18-18 done first proofread

<a name="ch13b120">
2010-07-03-18-20 start record
following is a small program
help you calculate.
Given α1, α2; β1, β2
find D matrix and new β.

<a name="ch13b121">
Please copy next code, paste to
[Box3, input JS command] of
calculator page, then click 
[Test box3 command, output to box4]
Box 4 has output result.

//<a name="ch13b122"> Index begin Index this file

//you can change value above
//do not change code below.
//<a name="ch13b123">
mat=m1+', '+m0+'; '+m0+', '+m1
//<a name="ch13b124">
newbet12=newBeta1+', '+newBeta2
oldbeta=beta1+', '+beta2
mat //matrix
newbet12 //new beta
oldbeta  //old beta
//2010-07-03-18-25 done record 

2010-07-03-22-22 done second proofread
2010-07-03-23-05 done spelling check

<a name="ch13b124a">
2010-07-08-11-58 start
α seq: 3 3 0
β seq: 4 1 1
do not have majorization relation.
The program jsmajor2.htm do not 
carry out calculation. Why it is 
wrong? Above code will give you 
<a name="ch13b124b">
First set
<a name="ch13b124c">
mat //matrix
0.6666666666666666, 0.3333333333333333; 0.3333333333333333, 0.6666666666666666
newbet12 //new beta
3, 2
oldbeta  //old beta
4, 1
which is ok.
<a name="ch13b124d">
New problem become
α seq: 3 3 0
β seq: 3 2 1
Second set
<a name="ch13b124e">
mat //matrix
2, -1; -1, 2
newbet12 //new beta
3, 0
oldbeta  //old beta
2, 1
<a name="ch13b124f">
The matrix [2, -1; -1, 2] or
[  2  -1]
[ -1   2]
is wrong. 
It is extrapolation.
 BUT  interpolation is required.
2010-07-08-12-09 stop

<a name="ch13b125"> Index begin Index this file
2010-07-05-08-58 start
■ Stand on HLP shoulder

We see eqn.13.14 is the center 
equation to Problem 13.3. The 
HLP Representation. That is 
Hardy, Littlewood and Polya
<a name="ch13b126">
Can we ride on HLP shoulder and 
find out the doubly stochastic 
matrix D ? Let us solve the
following problem.
<a name="ch13b127">
Given α and β two sequences 
with majorization relation
  α(<)β ---eqn.BO066
Given two index
  j<k  ---eqn.BO067
for two pairs data 
  α1=α[j]<β[j]=β1 ---eqn.BO068
  α2=α[k]>β[k]=β2 ---eqn.BO069
<a name="ch13b128">
  ρ=(β1+β2)/2 ---eqn.BO022
  τ=β1-ρ ---eqn.BO023
  σ=max(|α1-ρ|,|α2-ρ|) ---eqn.BO027
then we have
  β1=ρ+τ ---eqn.BO025
  β2=ρ-τ ---eqn.BO026
  ρ+σ=α1 ---eqn.BO029
  ρ-σ≠α2 ---eqn.BO030

<a name="ch13b129"> Index begin Index this file
Ask to find matrix D in the
following form
  D=[w  x] ---eqn.BO070
    [y  z]
which satisfy next equation
  Dβ=α ---eqn.BO001
that is
  [w  x]*[ρ+τ]=[ρ+σ] ---eqn.BO072
  [y  z] [ρ-τ] [ρ-σ]
Matrix in eqn.BO072 is unknown.
//matrix was given in eqn.13.14
<a name="ch13b130">
Expand eqn.BO072, find
  w*(ρ+τ)+x*(ρ-τ)=ρ+σ ---eqn.BO073
  y*(ρ+τ)+z*(ρ-τ)=ρ-σ ---eqn.BO074
There are four unknowns w,x,y,z
two equations. We need two more
constraint to solve this problem.
<a name="ch13b131">
  w+x=1 ---eqn.BO075
  y+z=1 ---eqn.BO076
Find w,x,y,z.

<a name="ch13b132">
Above is problem statement.
Next solve for w,x,y,z.
eqn.BO072 is re-write eqn.13.14
Only change is put 2x2 matrix 
to an unknown state. All other
variables are given.
<a name="ch13b133"> Index begin Index this file
Expansion of eqn.BO072 get
  w*(ρ+τ)+x*(ρ-τ)=ρ+σ ---eqn.BO073
  y*(ρ+τ)+z*(ρ-τ)=ρ-σ ---eqn.BO074
Use eqn.BO075 and eqn.BO076 to
reduce unknown from w,x,y,z
four unknown to x,z two unknown
  (1-x)*(ρ+τ)+x*(ρ-τ)=ρ+σ ---eqn.BO077
  (1-z)*(ρ+τ)+z*(ρ-τ)=ρ-σ ---eqn.BO078
<a name="ch13b134">
Re-group get
  (ρ+τ)-x*(ρ+τ)+x*(ρ-τ)=ρ+σ ---eqn.BO079
  (ρ+τ)-z*(ρ+τ)+z*(ρ-τ)=ρ-σ ---eqn.BO080
  (ρ+τ)-x*(2τ)=ρ+σ ---eqn.BO081
  (ρ+τ)-z*(2τ)=ρ-σ ---eqn.BO082
<a name="ch13b135">
  (ρ+τ)-(ρ+σ)=x*(2τ) ---eqn.BO083
  (ρ+τ)-(ρ-σ)=z*(2τ) ---eqn.BO084
  (τ-σ)=x*(2τ) ---eqn.BO085
  (τ+σ)=z*(2τ) ---eqn.BO086
  (τ-σ)/(2τ)=x ---eqn.BO087
  (τ+σ)/(2τ)=z ---eqn.BO088
Above is solution for x,z.
<a name="ch13b136">
  x+z=1 ---eqn.BO089
This is a lucky coincidence.
D matrix is
  D=[w  x] ---eqn.BO070
    [y  z]
now become
  D=[w   (τ-σ)/(2τ)] ---eqn.BO090
    [y   (τ+σ)/(2τ)]
<a name="ch13b137">
eqn.BO089 tell us that 
D matrix column_#2 elements 
sum to one.
  w+x=1 ---eqn.BO075
tell us that 
Elements of D matrix row_#1
sum to one.
<a name="ch13b138"> Index begin Index this file
eqn.BO089 and eqn.BO075 has x
as common element.
Both sum to one. Then
  w=z=(τ+σ)/(2τ) ---eqn.BO091
  y=x=(τ-σ)/(2τ) ---eqn.BO092

<a name="ch13b139">
Finally D matrix is
  D=[(τ+σ)/(2τ)   (τ-σ)/(2τ)] ---eqn.BO093
    [(τ-σ)/(2τ)   (τ+σ)/(2τ)]
Compare eqn.BO093 with matrix 
in eqn.13.14. We found 
Hardy, Littlewood, Polya 
Representation matrix !!
2010-07-05-09-51 here

<a name="ch13b140">
Above calculation is an easy
job for us. Because we start
on right track.
We start with eqn.BO072 and
eqn.BO075, eqn.BO076.
<a name="ch13b141">
Three masters Hardy, 
Littlewood, Polya joint work 
for this problem !?
It must NOT be an easy task 
for problem developer. 

<a name="ch13b142">
Thank you Professor Hardy.
Thank you Professor Littlewood.
Thank you Professor Polya.
Thank you Professor J. Michael Steele.
2010-07-05-09-57 stop

<a name="ch13b143"> Index begin Index this file
2010-07-05-10-12 start
■ Majorization ?! Interpolation !!

Can you see what is the meaning
of next two equations?
  w*(ρ+τ)+x*(ρ-τ)=ρ+σ ---eqn.BO073
  y*(ρ+τ)+z*(ρ-τ)=ρ-σ ---eqn.BO074
<a name="ch13b144">
Let us re-write them as next
  z*a+x*b=c ---eqn.BO094
  x*a+z*b=d ---eqn.BO095
Here (a,b) and (c,d) are two
pairs data. x and z are 
interpolation coefficients.
  x+z=1 ---eqn.BO089
<a name="ch13b145">
  0 ≦ σ ≦ τ ---eqn.BO065
eqn.BO091 and eqn.BO092 tell us
that both
  x≧0 and z≧0 ---eqn.BO096
Then eqn.BO094 and eqn.BO095 are
interpolation of (c,d) on (a,b)
<a name="ch13b146">
For example, assume
  a=2   ---eqn.BO097 (four lines)
we find
  c=z*a+x*b=6.8 ---eqn.BO098
  d=x*a+z*b=5.2 ---eqn.BO099

<a name="ch13b147"> Index begin Index this file
On the other hand, (a,b) bound 
is (2,10) what do we get if 
extrapolate 15 on (2,10) ?
Solve for
  15=m*2+n*10 ---eqn.BO100
   1=m+n      ---eqn.BO101
the answer is
  m = -5/8 ---eqn.BO102
  n = 13/8 ---eqn.BO103
<a name="ch13b148">
extrapolation coefficients sum 
to one, because eqn.BO101 require
to be one.
extrapolation coefficients are
out of [0,1] domain !!

<a name="ch13b149">
eqn.BO073, eqn.BO074 with
eqn.BO089 and eqn.BO096 tell
us that 
all elements in (ρ+σ, ρ-σ) are 
interpolation of (ρ+τ, ρ-τ)
<a name="ch13b150">
The fancy word is that
(ρ+τ, ρ-τ) majorize (ρ+σ, ρ-σ)
(ρ+σ,ρ-σ) majorized by (ρ+τ,ρ-τ)

Majorization ?! Interpolation !!
2010-07-05-10-53 stop

<a name="ch13b151"> Index begin Index this file
2010-07-05-15-35 start
■ Problem 13.4 (Schur's
  Majorization Inequality)

<a name="ch13b152">
Show that if
  φ:(a,b)→Real ---eqn.BO104
is a convex function, then the
  f:(a,b)n→Real ---eqn.BO105
defined by the sum
  =∑[k=1,n]φ(xk) ---eqn.13.17
is Schur convex.
<a name="ch13b153">
Thus, for α,β∈(a,b)n with
  α(<)β ---eqn.BO106
one has
 ≦∑[k=1,n]φ(βk) ---eqn.13.18
2010-07-05-15-47 here

<a name="ch13b154">
Please pay attention to the 
following points.
(1) φ:(a,b)→Real  say
    φ(t) is a ONE variable 
    function. variable t in (a,b)
    Function value φ(t) in Real
<a name="ch13b155">
(2) f:(a,b)n→Real say
    f(x) is a n variable
    function. variable x has
    n components. Each component
    is in (a,b). Then x∈(a,b)n
    f(x) has one output
    which is in whole real axis.
<a name="ch13b156"> Index begin Index this file
(3) f(x1,x2,...,xn)=∑[k=1,n]φ(xk)
    say that f(x) is a summation
    of φ(t). 
    Alert, not product of φ(t).
<a name="ch13b157">
(4) f(x1,x2,...,xn) is defined
    for a generic sequence x
    When compare magnitude, need
    TWO sequences α, β with
    majorization relation α(<)β.
    Compare f(α) with f(β).
2010-07-05-16-06 here

<a name="ch13b158">
Example from textbook.
Walker's inequality eqn.13.3
Two sequences are (a,b,c) and
  (x,y,z)=(b+c-a,c+a-b,a+b-c) ---eqn.BO107
Require a,b,c,x,y,z all be 
positive number.
<a name="ch13b159">
In this example φ(t) is
  φ(t)=1/t ---eqn.BO108
  f(a,b,c)=1/a + 1/b + 1/c ---eqn.BO109
which is same as eqn.BN030
We know that φ(t)=1/t is convex
for t>0 (Curve shape like ╰╯)
tute0046.htm#ch13a076 say that
  (a,b,c) (<) (x,y,z) ---eqn.BN026
<a name="ch13b160">
Then Problem 13.4 (Schur's
  Majorization Inequality)
assure that (see eqn.13.3)
  f(a,b,c)≦f(x,y,z) ---eqn.BO110
Up to here, it is just one 
example. (not proof)

Prove Schur's Majorization 
Inequality below.
2010-07-05-16-28 stop

<a name="ch13b161"> Index begin Index this file
2010-07-05-16-59 start
Schur's Majorization Inequality
cover Jensen's Inequality.

Review Jensen's Inequality
If function domain is convex set
If equation is convex function
Jensen's Inequality says:
  AM in domain ≦ AM in range

<a name="ch13b162">
Use Walker's inequality eqn.13.3
as example. Change a little bit.
Instead of (a,b,c) and
two sequence. 
<a name="ch13b163">
Now use (a,b,c) and
  (d,d,d)=((a+b+c)/3, ---eqn.BO111
(d,d,d) is majorized by (a,b,c)
Please see tute0046.htm#ch13a035
  φ(t)=1/t ---eqn.BO108
is convex on y in (0, infinity)

<a name="ch13b164">
Schur's Majorization Inequality
tell us that for
  f(d,d,d)=1/d + 1/d + 1/d
          =3/(a+b+c) + 3/(a+b+c)
  f(d,d,d)=9/(a+b+c) ---eqn.BO112
is majorized by
  f(a,b,c)=1/a + 1/b + 1/c ---eqn.BO113
<a name="ch13b165"> Index begin Index this file
that is
  f(d,d,d) ≦ f(a,b,c) ---eqn.BO114
  9/(a+b+c) ≦ 1/a + 1/b + 1/c ---eqn.BO115

<a name="ch13b166">
On the other hand,
Jensen's Inequality says:
  AM in domain ≦ AM in range
For function
  φ(t)=1/t ---eqn.BO108
and sequence (a,b,c)
AM of (a,b,c) is
  x_AM=(a+b+c)/3 ---eqn.BO116
AM in domain is
  f(x_AM)=1/x_AM=3/(a+b+c) ---eqn.BO117

<a name="ch13b167">
AM in range is
that is
  (1/a +1/b +1/c)/3 ---eqn.BO118

<a name="ch13b168">
Jensen's Inequality says:
  AM in domain ≦ AM in range
give us
  3/(a+b+c) ≦ (1/a +1/b +1/c)/3 ---eqn.BO119
Move greater than side 
denominator 3 to less than side
  9/(a+b+c) ---eqn.BO120
  ≦ (1/a +1/b +1/c)

<a name="ch13b169"> Index begin Index this file
Schur's Majorization Inequality
result eqn.BO115 and
Jensen Inequality result eqn.BO120
are the same !!
2010-07-05-17-29 stop

<a name="ch13b170">
2010-07-05-18-33 start
If a function is convex. If this
function is differentiable. then
convex and Schur convex are the 
same. because symmetric analytic 
convex function satisfy Schur's 
Criterion eqn.13.4

<a name="ch13b171">
Exercise 6.19 say Convex function 
slope monotone increase.
The default not-told part is that
"independent variable increase".
The whole picture is
when independent variable increase
convex function slope monotone 
<a name="ch13b172">
Above is for one variable in n
Use one example, let
  x=[x1,x2,...,xn] ---eqn.BO121
  φ(t)=1/t  ---eqn.BO122
  f(x)=∑[i=1,n]φ(xi) ---eqn.BO123
  f(x)=1/x1 + 1/x2 + ...
     + 1/xn ---eqn.BO124
<a name="ch13b173"> Index begin Index this file
For all n variables, the variable 
pairs (xj, xk) and slope pair 
(d[f(x)]/d[xj], d[f(x)]/d[xk])
are similar ordered.
<a name="ch13b174">
Schur's differential
         -d[f(x)]/d[xk]) ●●change
  = ---eqn.BO125 //page 202 line 9

<a name="ch13b175">
For selected example,
Schur's differential
  = ---eqn.BO126
is non-negative.
2010-07-05-19-27 here

<a name="ch13b176">
= 0.75

<a name="ch13b177">
Schur's differential
is non-negative.
= (xj-xk)2*(xj+xk)/(xj2xk2) ---eqn.BO127
are squares and positive term
sum, which is non-negative.
2010-07-05-19-37 stop

<a name="ch13b178"> Index begin Index this file
2010-07-05-21-11 start
■ Schur's Criterion Limitation

Schur's Criterion eqn.13.4
calculate between two independent 
variables, that is manipulate on 
two different coordinate axis.
How can we draw ineqaulity result
from two axis calculation?
<a name="ch13b179">
For example, assume
  g(y,z)=y*y+1/z ---eqn.BO128
Schur's Criterion become
  (y-z)*{∂[g(y,z)]/∂y - ∂[g(y,z)]/∂z}
 =(y-z)*{2*y - (-1)/z/z}
 =(y-z)*(2*y + 1/z/z) ---eqn.BO129
There is no rule promise this
production be non-negative.
y=2,z=3; ans=-4.111111111111111
y=5,z=3; ans=20.22222222222222
Can you answer this question?

<a name="ch13b180">
Schur's Criterion Limitation
is a point worth our attention.

No rule promise eqn.BO129 
production be non-negative.
That is true. 
<a name="ch13b181">
  g(y,z)=y*y+1/z ---eqn.BO128
is NOT a Schur convex function.
y*y is convex, true.
1/z is convex, true.
g(y,z) is addition of two convex
functions. That is also true.

<a name="ch13b182"> Index begin Index this file
Schur require that ONE function
  φ:(a,b)→Real ---eqn.BO104
be convex, and the multi-variable
  f(x1,x2,...,xn)=∑[k=1,n]φ(xk) ---eqn.13.17
be summation of function φ. Each φ 
sit on different coordinate axis.
Measured with this requirement,
  g(y,z)=y*y+1/z ---eqn.BO128
is NOT a Schur convex function.
//h1(y,z)=y*y+z*z IS Schur convex 
//h2(y,z)=1/y+1/z IS Schur convex 

<a name="ch13b183">
Return to Problem 13.1
Schur's Criterion.
Problem 13.1 did not require the
definition of eqn.13.17. If we
put eqn.BO128 into eqn.13.4
there is trouble. Possibly 
Problem 13.1 should include 
eqn.13.17 as part of definition.

<a name="ch13b184">
Schur's Criterion has a relative
small definition domain. 
Many equation not qualified and 
many sequences not qualified.
When we apply Schur's Criterion 
we must be very careful.

<a name="ch13b185">
Schur's Criterion Limitation is 
(similar notes)
(1) ONE function
    φ(t):(a,b)→Real ---eqn.BO104
    and the multi-variable
  f(x1,x2,...,xn)=∑[k=1,n]φ(xk) ---eqn.13.17
    is a SUMMATION of φ(t) and
    change t to x1,x2,...,xn
  g(y,z)=y*y+1/z ---eqn.BO128
    is not qualified.
<a name="ch13b186">
(2) f(x) be symmetry to all
    of its variables.
    This requirement is actually
    a result of (1).
<a name="ch13b187"> Index begin Index this file
(3) One function defined and two
    sequences compared. These two
    sequences have majorization

<a name="ch13b188">
  g(y,z)=y*y+1/z ---eqn.BO128
    is not qualified.
  h(y,z)=1/y+1/z ---eqn.BO130
    is qualified.
Because we have ONE function 
φ(t)=1/t and multi-variable
function be summation of φ(t).
<a name="ch13b189">
That is
  h(y,z)=φ(y)+φ(z) ---eqn.BO131
If φ(t)=y*y or φ(t)=exp(t) or 
whatever, φ(y) and φ(z) are 
identical twin. Calculation
between φ(y) and φ(z) is 

<a name="ch13b190">
Liu,Hsinhan had question about
Schur's Criterion eqn.13.4.
After think, conclude
  g(y,z)=y*y+1/z ---eqn.BO128
is not qualified and
  h(y,z)=1/y+1/z ---eqn.BO130
is possible.

<a name="ch13b191">
Do you agree? You can say no,
you can present a better 
explanation. This page is just
a study notes. This page may 
contain errors!
2010-07-05-22-11 stop

<a name="ch13b192"> Index begin Index this file
2010-07-05-22-36 start
■ Irrational beat rational
LiuHH wrote a small program
fraction to quotient
Click [t] button (test), program 
generate a x=0.5945165945165945
and report x box filled 412/693
Click [fill M5] build integers.
Then click [x*M5], box M6 show
<a name="ch13b193">
Find minimum fraction.
Min: i=692,inNumb[i]=693
<a name="ch13b194">
This program convert fraction to 
quotient. During work, try change
from x=0.5945165945165945
 to  x=0.5945265945165945
Just alter one digit. Program
<a name="ch13b195">
Find minimum fraction.
minAtI=-1; Please extend n2

<a name="ch13b196">
LiuHH think:
There are infinite many rational
There are infinite many irrational
Which one has more count?
What is the count ratio?

<a name="ch13b197"> Index begin Index this file
For example
Alter any '3' to for example '2'
the report is

<a name="ch13b198">
Each altered digit string is
NOT a rational number any more
because rational number has 
repeat pattern. I just alter 
one digit, there is no repeat,
so the new string is irrational.
repeat "594516" infinity times.

<a name="ch13b199">
One rational number 1/3 has 
infinite many '3'. I can alter
infinite many different ways.
The ratio of rational 1/3 to
correspond irrational is one
to infinity !!
Any rational number be the same.
On the real axis, the ratio of 
rational to irrational is one 
to infinity !!

<a name="ch13b200">
Draw a line between 0 and 1.
Put pencil tip arbitrary on line
The probability of hitting a 
rational is nearly zero.
The probability of hitting an 
irrational is nearly one.

This is not a proof. Just a
break time think.
2010-07-05-22-55 stop

<a name="ch13b201"> Index begin Index this file
2010-07-06-10-28 start
■ A Direct Approach to Schur's 
  Majorization Inequality
eqn.BO125 use differentiation
φ'(xj) and φ'(xk) to evaluate 
Schur's Majorization Inequality.
The following we do same thing
but not use differentiation.

<a name="ch13b202">
Schur's Majorization Inequality
 ≦∑[k=1,n]φ(βk) ---eqn.13.18
Given α, β the next relation
  α(<)β ---eqn.BO106
Problem 13.3 promise that
we can find a doubly stochastic 
matrix D such that
  α=D*β        ---eqn.BN121
<a name="ch13b203">
Write eqn.BN121 in longhand for
each j,1,2,...,n, we have
  αj=∑[k=1,n]djkk ---eqn.BO132
  dj1+dj2+...+djn=1 ---eqn.BO133
(textbook page 202 line -11)
100% numerical example in matrix 
form eqn.BN122
100% numerical expanded example
eqn.BO009 to eqn.BO013
 50% numerical example eqn.BN148

<a name="ch13b204">
eqn.BO106 is a given condition.
Problem 13.3 proved result tell
us that α=D*β is also GIVEN.
For example, eqn.BN148 is given.
<a name="ch13b205">
From eqn.BN148 take α3 as example
  α3 = β1*1/9+β2*16/90+β3*64/90
      +β4*0 ---eqn.BO134
α3 is observed data.
β1 to β4 are also observed data.
α3 is an interpolation of β1 to 
β4. (1/9,16/90,64/90 >0, sum=1)
<a name="ch13b206"> Index begin Index this file
Next, apply Jensen's inequality
to eqn.BO134. Jensen take α3 as
an input, as an independent 
<a name="ch13b207">
Jensen's inequality say
For convex function, we have
  AM_DOMAIN ≦ AM_RANGE ---eqn.BO135
See eqn.6.2
For current problem, we assume
that φ(t) is a convex function.
<a name="ch13b208">
Jensen's inequality tell us
  φ(x_AM) ≦ AM of φ(xi) ---eqn.BO136
Put eqn.BO134 into eqn.BO136 get
  +φ(β3)*64/90+φ(β4)*0 ---eqn.BO137
 Red line is AM_DOMAIN
Blue line is AM_RANGE
<a name="ch13b209">
Alert, red line is same as φ(α3)
Alert, coefficient sum to one
  1/9+16/90+64/90+0 = 1 ---eqn.BO138
all four coefficients≧0
We can call red line and blue
line as "mean" only if coefficient 
sum to one and all coefficients≧0.
Both red and blue use SAME
coefficient set.

<a name="ch13b210">
Inequality eqn.BO137 is a
numerical example. Symbolic
expression is textbook page 202
line -9 equation
  φ(αj)∑[k=1,n]djk*φ(βk) ---eqn.BO139
compare //above φ(data), below data
    αj = ∑[k=1,n]djkk ---eqn.BO132
Observed data αj and βk have 
equality relation.
Function φ(t) is convex. then
φ(αj) and φ(βk) have inequality

<a name="ch13b211"> Index begin Index this file
Inequality eqn.BO139 is our mid
point. Next, sum j from j=1 to j=n
That is write down n inequalities
like eqn.BO139 and sum them. The
result is
<a name="ch13b212">
{ φ(βk)
djk }
---page 202 ---line 26 ---eqn.BO140
width of above equation
<a name="ch13b213">
2010-07-06-12-18 here
eqn.BO140 red inequality is
n copy of eqn.BO139, which is
a result of Jensen's inequality.
eqn.BO140 red equality is switch
j,k summation to k,j summation.
eqn.BO140 blue equality use the
property that doubly stochastic 
matrix column sum to one.
2010-07-06-12-27 stop

<a name="ch13b214">
2010-07-06-16-10 start
The net result of eqn.BO140 is
 ≦∑[k=1,n]φ(βk) ---eqn.BO141
We start from given
  α(<)β ---eqn.BO106
and arrived eqn.BO141, it is 
target eqn.13.18 exactly.
This Direct Approach to Schur's 
Majorization Inequality did not
use function differentiation.

<a name="ch13b215"> Index begin Index this file
2010-07-06-16-18 here
■ Problem 13.5 
  A Day-to-Day Example
(Textbook page 203 top)

Given that x,y,z in (0,1) such
  max(x,y,z)≦(x+y+z)/2<1 ---eqn.13.19
Show that one has the bound
<a name="ch13b216">

) (

) (

) {

---page 203 ---line 8 ---eqn.13.20
width of above equation
<a name="ch13b217">
2010-07-06-16-30 here
If do not view from majorization
This problem is hard to solve.
eqn.13.19 is given. What it tell
(x,y,z) is a sequence.
(x+y+z)/2 is sequence sum divide
by 2, not divide by 3, then
(x+y+z)/2 is not Arithmetic Mean

<a name="ch13b218">
Can we build second sequence from
the given (x,y,z). How to build?
From the expression (x+y+z)/2 let
us define
  s=(x+y+z)/2 ---eqn.BO142
<a name="ch13b219">
Second sequence can be (s,s,0)
Both (x,y,z) and (s,s,0) have
same total sum x+y+z.
Two seq. have same total sum
that is majorization relation
necessary condition.

<a name="ch13b220"> Index begin Index this file
We assume that
  x≧y≧z ---eqn.BO143
This assumption still let us 
keep problem's generality.
Next exam (x,y,z) and (s,s,0) 
for majorization relation.

<a name="ch13b221">
First one term comparison is
  x ≦?≧ s ---eqn.BO144
How do we know it ?
Given condition eqn.13.19 
  max(x,y,z)≦(x+y+z)/2 ---eqn.13.19
let us have 
  x ≦ s ---eqn.BO145
where s=(x+y+z)/2
//(x+y+z)/2<1 in eqn.13.19 let
//eqn.13.20 denominator be 
//non zero.

<a name="ch13b222">
First two term comparison is
  x+y ≦?≧ s+s ---eqn.BO146
  s+s=2*[(x+y+z)/2]=x+y+z ---eqn.BO147
eqn.BO146 become
  x+y ≦?≧ x+y+z ---eqn.BO148
Given x,y,z in (0,1) be positive
  x+y ≦ x+y+z ---eqn.BO149
is true.

<a name="ch13b223">
Final equality is already checked
Both (x,y,z) and (s,s,0) have
same total sum x+y+z.

<a name="ch13b224">
We conclude that
Majorization relation exist.
it is
  (x,y,z) (<) (s,s,0)  ---eqn.BO150

<a name="ch13b225"> Index begin Index this file
Schur's Majorization Inequality
 ≦∑[k=1,n]φ(βk) ---eqn.13.18
is a equation of summation.
Problem 13.5 target equation
eqn.13.20 is a equation of
multiplication. They are totally
different. What to do?
2010-07-06-16-56 stop

<a name="ch13b226">
2010-07-06-18-27 start
We can modify the target equation
from [(1+x)/(1-x)]*[(1+y)/(1-y)]*[(1+z)/(1-z)]
 to [(1+x)/(1-x)]+[(1+y)/(1-y)]+[(1+z)/(1-z)]
<a name="ch13b227">
Certainly, this change is wrong.
We know that
  log(ABC)=log(A)+log(B)+log(C) ---eqn.BO151
If we take log of eqn.13.20, then
multiplication ABC become addition

<a name="ch13b228">
Next question is 
inequality of ABC and
inequality of log(A)+log(B)+log(C)
are they the same?
Answer is yes, they are the same.
<a name="ch13b229">
The reason is that log() function
is a monotone increase function.
Draw log(x) curve and take two
points x1 and x2. If 
  x1<x2 ---eqn.BO152
  log(x1)<log(x2) ---eqn.BO153
We can add log(), we can drop 
log(), INEQUALITY is unchanged.

<a name="ch13b230"> Index begin Index this file
exp(x) is monotone increase 
function. Although exp(x) is
concave. But take exp() for
one expression, the inequality
direction is the same.

<a name="ch13b231">
On the other hand, if define
  g(x)=1/x ---eqn.BO154
  x1<x2 ---eqn.BO152
get reversed inequality
  1/x1>1/x2 ---eqn.BO155
Because g(x)=1/x is monotone 
2010-07-06-18-53 here

<a name="ch13b232">
Now define
  f(x,y,z)= log{[(1+x)/(1-x)]
               } ---eqn.BO156
that is
<a name="ch13b233">
  f(x,y,z)= log[(1+x)/(1-x)]
           +log[(1+z)/(1-z)] ---eqn.BO157

<a name="ch13b234">
Compare eqn.BO157 with eqn.13.17
find we need define
  φ(t)=log[(1+t)/(1-t)] ---eqn.BO158
Is φ(t) a convex function?
<a name="ch13b235"> Index begin Index this file
   + (1+t)*(-1)*(-1)/(1-t)/(1-t)]
<a name="ch13b236">
First derivative is
 =2/(1-t*t) ---eqn.BO158

The expression [*(-1)*(-1)]
one [*(-1)] come from 1/(1-t) has
power -1
Second [*(-1)] come from (1-t)
has coefficient -1.
2010-07-06-19-49 here

<a name="ch13b237">
Continue for second derivative

<a name="ch13b238">
Second derivative is
 =4*t/(1-t*t)/(1-t*t) ---eqn.BO159

<a name="ch13b239">
Problem 13.5 given that x,y,z in (0,1) 
then t is in (0,1)
Second derivative is positive in
(0,1). Therefore
  φ(t)=log[(1+t)/(1-t)] ---eqn.BO158
is convex on (0,1).
eqn.BO157 f(x,y,z) is Schur convex.

<a name="ch13b240"> Index begin Index this file
We conclude that
  (x,y,z) (<) (s,s,0)  ---eqn.BO150
is true. Apply 
Schur's Majorization Inequality 
eqn.13.18 find the next relation.
<a name="ch13b241"> start from no log and multiplication
eqn.BO160 applied log() change multiplication to addition.
log (

) + log (

) + log (

log (

) + log (

) + log (

similar to ---page 203 ---line 8 ---eqn.BO160
width of above equation
<a name="ch13b242">
2010-07-06-20-19 here
In eqn.BO160, carry out 
  log(ABC)=log(A)+log(B)+log(C) ---eqn.BO151
and DROP log() from equation
get //how can I drop log()?
<a name="ch13b243">
//next equation is ---eqn.BO161
 *[(1+z)/(1-z)] //seq. (x,y,z)
 *[(1+0)/(1-0)] //seq. (s,s,0)
eqn.BO161 is our target equation
Problem 13.5 is done.
2010-07-06-20-48 stop

<a name="ch13b244"> Index begin Index this file
2010-07-07-10-57 start
■ Overdetermined system
In D matrix 
  D=[w  x] ---eqn.BO070
    [y  z]
we found
  x+z=1 ---eqn.BO089
This is a lucky coincidence.
Why 'lucky coincidence' ?

<a name="ch13b245">
Textbook page 198, line -1 say
"The system is overdetermined,
 but it does have a solution."
Why 'overdetermined' ?

<a name="ch13b246">
Matrix D has four unknowns, w,
x,y,z. We need four constraint 
to solve for four unknowns.
Constraints sre
  w*(ρ+τ)+x*(ρ-τ)=ρ+σ ---eqn.BO073
  y*(ρ+τ)+z*(ρ-τ)=ρ-σ ---eqn.BO074
<a name="ch13b247">
  w+x=1 ---eqn.BO075
  y+z=1 ---eqn.BO076
In above four equations 
  ρ,τ,σ are given constants
w,x,y,z are unknowns.

<a name="ch13b248"> Index begin Index this file
We solve four unknowns and four
constraint problem, get 
  (τ-σ)/(2τ)=x ---eqn.BO087
  (τ+σ)/(2τ)=z ---eqn.BO088
verify found
  x+z=1 ---eqn.BO089
<a name="ch13b249">
We are given w+x=1 and y+z=1
 the result  x+z=1 is a surprise
Because x+z is not constrained.
x+z can be ANY value. Now x+z=1
let us have "column sum to one" 
doubly stochastic matrix 
requirement for free !!
<a name="ch13b250">
x+z=1 is equivalent to 
FIFTH constraint. We have a
four unknown system. Fifth
constraint is overdetermined.
Fifth is consistent with 
previous four, so we are lucky.

<a name="ch13b251">
Prof. J. Michael Steele say
"overdetermined, but ..." and 
LiuHH say "lucky coincidence"
Is it necessary to be one ?!
Under what condition column
sum x+z would NOT be one?

<a name="ch13b252">
Let us start from
  [w  x]*[ρ+τ]=[ρ+σ] ---eqn.BO072
  [y  z] [ρ-τ] [ρ-σ]
Now insert coefficients a,b,c,d
find out if use non-one a,b,c,d.
what do we get ? Treat  a,b,c,d 
as adjustable constants.
2010-07-07-11-24 here

<a name="ch13b253"> Index begin Index this file
2010-07-07-11-41 start
Write eqn.BO072 as
  [w  x]*[aρ+bτ]=[cρ+dσ] ---eqn.BO162
  [y  z] [aρ-bτ] [cρ-dσ]
If set a=b=c=d=1 then no change.
Expand eqn.BO162, find
  w*(aρ+bτ)+x*(aρ-bτ)=cρ+dσ ---eqn.BO163
  y*(aρ+bτ)+z*(aρ-bτ)=cρ-dσ ---eqn.BO164
Solve for w,x,y,z.

<a name="ch13b254">
Use eqn.BO075 and eqn.BO076 to
reduce unknown from w,x,y,z
four unknown to x,z two unknown
  (1-x)*(aρ+bτ)+x*(aρ-bτ)=cρ+dσ ---eqn.BO165
  (1-z)*(aρ+bτ)+z*(aρ-bτ)=cρ-dσ ---eqn.BO166
<a name="ch13b255">
Re-group get
  (aρ+bτ)-x*(aρ+bτ)+x*(aρ-bτ)=cρ+dσ ---eqn.BO167
  (aρ+bτ)-z*(aρ+bτ)+z*(aρ-bτ)=cρ-dσ ---eqn.BO168
  (aρ+bτ)-x*(2bτ)=cρ+dσ ---eqn.BO169
  (aρ+bτ)-z*(2bτ)=cρ-dσ ---eqn.BO170
<a name="ch13b256">
  (aρ+bτ)-(cρ+dσ)=x*(2bτ) ---eqn.BO171
  (aρ+bτ)-(cρ-dσ)=z*(2bτ) ---eqn.BO172
  (aρ-cρ)+(bτ-dσ)=x*(2bτ) ---eqn.BO173
  (aρ-cρ)+(bτ+dσ)=z*(2bτ) ---eqn.BO174
<a name="ch13b257"> Index begin Index this file
  [(aρ-cρ)+(bτ-dσ)]/(2bτ)=x ---eqn.BO175
  [(aρ-cρ)+(bτ+dσ)]/(2bτ)=z ---eqn.BO176

Above is solution for x,z.
<a name="ch13b258">
verify column sum x+z
     +{[(aρ-cρ)+(bτ+dσ)]/(2bτ)} ---eqn.BO177
  x+z={[(aρ-cρ)+(bτ-dσ)] ---eqn.BO178
     + [(aρ-cρ)+(bτ+dσ)]}/(2bτ)
  x+z={2*(aρ-cρ)+ 2*bτ}/(2bτ) ---eqn.BO179
<a name="ch13b259">
  x+z={aρ-cρ + bτ}/(bτ) ---eqn.BO180
if a≠c, then
  x+z={aρ-cρ + bτ}/(bτ)
   NOT = 1
if a=c, then x+z=1

<a name="ch13b260">
We started from eqn.BO072
all ρ,τ,σ coefficients be one.
This condition give us column
sum to one result.
"Overdetermined" become 
2010-07-07-12-01 stop

<a name="ch13b261"> Index begin Index this file
2010-07-07-13-12 start
Textbook page 204 figure 13.2 
illustrate the relations between
 α=T1T2...Tnβ ---eqn.BO181
Please see Proof of eqn.BO181
<a name="ch13b262">
//similar equation 
//α seq=P2*R2*P1*R1*ß seq ---eqn.maj01
//P2=permutation matrix, second step
//R2=RobinHood average mat. 2nd step
Figure 13.2 is not in this study
note page. Please read textbook.
2010-07-07-13-28 stop

2010-07-07-19-12 done first  proofread
2010-07-08-13-59 done second proofread

<a name="ch13b263"> Index begin Index this file
2010-07-08-15-33 start
■ Doubly stochastic matrix
  multiply DS get DS

Problem 13.3 given condition is
α and β two sequences have
majorization relation
  α(<)β ---eqn.BO066
ask to prove there exists a 
doubly stochastic matrix D 
such that //example 1 2
  α=Dβ ---eqn.BO001

<a name="ch13b264">
The build of matrix D need several
iterations. i-th iteration find a
matrix Di, the final answer is
  α=Dβ=DnDn-1...D2D1β ---eqn.BO182
Doubly stochastic matrix Di
doubly stochastic matrix Dj
get matrix Dk. We need to know 
whether matrix Dk is
doubly stochastic or not?

<a name="ch13b265">
Link to symbolic n=3, n=general 
Start with 3x3 numerical example.
α seq.= 8 6 4 ---eqn.BO183
β seq.= 9 7 2 ---eqn.BO184
get doubly stochastic 
matrix m =
[5/6  1/30  2/15] ---eqn.BO185
[ 0   4/5   1/5 ]
[1/6  1/6   2/3 ]

<a name="ch13b266">
α seq.= 0.5  0.5  0 ---eqn.BO186
β seq.=  2   0   -1 ---eqn.BO187
get doubly stochastic 
0.45  0.15  0.4
0.25  0.75  0  
0.3  0.1  0.6  
that is
matrix n =
[9/20  3/20  2/5] ---eqn.BO188
[1/4   3/4    0 ]
[3/10  1/10  3/5]

<a name="ch13b267">
matrix m multiply matrix n =
[5/6  1/30  2/15] [9/20  3/20  2/5]
[ 0   4/5   1/5 ]*[1/4   3/4    0 ]
[1/6  1/6   2/3 ] [3/10  1/10  3/5]
= matrix s =
[s11  s12  s13] ---eqn.BO189
[s21  s22  s23]
[s31  s32  s33]
<a name="ch13b268"> Index begin Index this file
s11=5/6*9/20 + 1/30*1/4 + 2/15*3/10
   =127/300 ---eqn.BO190
s12=5/6*3/20 + 1/30*3/4 + 2/15*1/10
   = 49/300 ---eqn.BO191
s13=5/6*2/5  + 1/30*0   + 2/15*3/5
   =31/75 ---eqn.BO192

<a name="ch13b269">
s21= 0 *9/20 + 4/5 *1/4 + 1/5 *3/10
   =13/50 ---eqn.BO193
s22= 0 *3/20 + 4/5 *3/4 + 1/5 *1/10
   =31/50 ---eqn.BO194
s23= 0 *2/5  + 4/5 *0   + 1/5 *3/5
   =3/25 ---eqn.BO195

<a name="ch13b270">
s31=1/6*9/20 + 1/6 *1/4 + 2/3 *3/10
   =19/60 ---eqn.BO196
s32=1/6*3/20 + 1/6 *3/4 + 2/3 *1/10
   =13/60 ---eqn.BO197
s33=1/6*2/5  + 1/6 *0   + 2/3 *3/5
   =7/15 ---eqn.BO198

<a name="ch13b271">
matrix m multiply matrix n =
[5/6  1/30  2/15] [9/20  3/20  2/5]
[ 0   4/5   1/5 ]*[1/4   3/4    0 ]
[1/6  1/6   2/3 ] [3/10  1/10  3/5]
= matrix s = ---eqn.BO199
[127/300  13/50  19/60]
[ 49/300  31/50  13/60]
[ 31/75    3/25   7/15]

<a name="ch13b272">
Now matrix s is a product of two
doubly stochastic matrix m and n
Is matrix s doubly stochastic ?
It is clear that all matrix s 
elements are positive. Need to
check matrix s column/row sums.
(define doubly stochastic)

<a name="ch13b273"> Index begin Index this file
matrix s column 1 sum = ---eqn.BO200
 127/300 + 49/300 + 31/75 =1
matrix s column 2 sum = ---eqn.BO201
  13/50  + 31/50  +  3/25 =1
matrix s column 3 sum = ---eqn.BO202
  19/60  + 13/60  +  7/15 =1

<a name="ch13b274">
matrix s row 1 sum = ---eqn.BO203
 127/300 + 13/50  + 19/60 =1
matrix s row 2 sum = ---eqn.BO204
  49/300 + 31/50  + 13/60 =1
matrix s row 3 sum = ---eqn.BO205
  31/75  +  3/25  +  7/15 =1

<a name="ch13b275">
One numerical example confirmed
Doubly stochastic matrix m
doubly stochastic matrix n
doubly stochastic matrix s.

<a name="ch13b276">
Link to symbolic n=general, numerical
Next use symbolic equation to 
check n=3 special case.
Doubly stochastic matrix m=
[m11  m12  m13] ---eqn.BO206
[m21  m22  m23]
[m31  m32  m33]

<a name="ch13b277">
doubly stochastic matrix n=
[n11  n12  n13] ---eqn.BO207
[n21  n22  n23]
[n31  n32  n33]

<a name="ch13b278"> Index begin Index this file
Both matrix m and n 
each  row   sum to one
each column sum to one
Both matrix elements be

<a name="ch13b279">
matrix m multiply matrix n =
[m11  m12  m13] [n11  n12  n13]
[m21  m22  m23]*[n21  n22  n23]
[m31  m32  m33] [n31  n32  n33]
<a name="ch13b280">
= matrix s = ---eqn.BO208
[s11  s12  s13]
[s21  s22  s23]
[s31  s32  s33]
<a name="ch13b281">
s11=m11*n11+m12*n21+m13*n31 ---eqn.BO209
s12=m11*n12+m12*n22+m13*n32 ---eqn.BO210
s13=m11*n13+m12*n23+m13*n33 ---eqn.BO211

s21=m21*n11+m22*n21+m23*n31 ---eqn.BO212
s22=m21*n12+m22*n22+m23*n32 ---eqn.BO213
s23=m21*n13+m22*n23+m23*n33 ---eqn.BO214

<a name="ch13b282"> ATTENTION
s31=m31*n11+m32*n21+m33*n31 ---eqn.BO215
s32=m31*n12+m32*n22+m33*n32 ---eqn.BO216
s33=m31*n13+m32*n23+m33*n33 ---eqn.BO217
matrix s = matrix m * matrix n 
Watch and learn the pattern.
sij=mi1*n1j+mi2*n2j+mi3*n3j ---eqn.BO317
sum   k=1     k=2     k=3
sum middle third index k 
from k=1 to k=n. All be the same.
Symbolic equation: eqn.BO233

<a name="ch13b283"> Index begin Index this file
s12 is selected for color 
illustration, help freshman
reader to know the matrix 
product rule.
eqn.BO317 is important too.

<a name="ch13b284">
Check matrix s column 1 sum
s11+s21+s31= ---eqn.BO218
 (m11+m21+m31)*n11 ---eqn.BO219
<a name="ch13b285">
The given condition is
matrix m 
each  row   sum to one
each column sum to one
then we have
  m11+m21+m31=1 ---eqn.BO220
  m12+m22+m32=1 ---eqn.BO221
  m13+m23+m33=1 ---eqn.BO222
<a name="ch13b286">
eqn.BO219 become
matrix s column 1 sum
s11+s21+s31= ---eqn.BO223
=  n11+  n21+  n31
Still, given matrix n 
each  row   sum to one
each column sum to one
then we have
  n11+  n21+  n31 = 1 ---eqn.BO224
matrix s column 1 sum
s11+s21+s31=1 ---eqn.BO225

<a name="ch13b287">
Same reason apply to matrix s 
column 2 sum
column 3 sum
row 1 sum
row 2 sum
row 3 sum
therefore matrix s, the result
of matrix m * matrix n, is again
a doubly stochastic matrix.

This is rank=3 special case.
2010-07-08-16-50 here

<a name="ch13b288"> Index begin Index this file
Link to symbolic n=3, numerical
Above is n=3 special case.
Below is n=any general case.
Given rank n 
doubly stochastic matrix A
and rank n 
doubly stochastic matrix B
Prove that C=AB is also 
doubly stochastic matrix

<a name="ch13b289">
Given condition let us have
matrix A column sum be one
  ∑[i=1,n]aij=1 ---eqn.BO226
  for j=1,...,n
matrix A row sum be one
  ∑[j=1,n]aij=1 ---eqn.BO227
  for i=1,...,n

<a name="ch13b290">
matrix B column sum be one
  ∑[i=1,n]bij=1 ---eqn.BO228
  for j=1,...,n
matrix B row sum be one
  ∑[j=1,n]bij=1 ---eqn.BO229
  for i=1,...,n

<a name="ch13b291">
and given
matrix A elements be nonnegative
  aij≧0 ---eqn.BO230
  for i,j=1,...,n
matrix B elements be nonnegative
  bij≧0 ---eqn.BO231
  for i,j=1,...,n

<a name="ch13b292">
Matrix production
  C=AB  ---eqn.BO232
for each elelent cij has
  cij=∑[k=1,n]aik*bkj ---eqn.BO233
See ATTENTION for k summation

<a name="ch13b293"> Index begin Index this file
matrix C column sum be 
  ∑[i=1,n]cij ---eqn.BO234
 = //switch i_sum with k_sum
   //bkj no i, sum i first
  ∑[k=1,n]{∑[i=1,n]aik}*bkj ---eqn.BO235
 = // matrix A column sum be one
   // matrix B column sum be one
 = 1  ---eqn.BO236
This is general for j=1,...,n
Above proved
matrix C column sum be ONE.

Next is
<a name="ch13b294">
matrix C row sum be 
  ∑[j=1,n]cij ---eqn.BO237
 = //switch j_sum with k_sum
   //aik no j, sum j first
  ∑[k=1,n]{∑[j=1,n]bkj}*aik ---eqn.BO238
 = // matrix B row sum be one
   // matrix A row sum be one
 = 1  ---eqn.BO239
This is general for i=1,...,n
Above proved
matrix C row sum be ONE.
2010-07-08-17-20 here

<a name="ch13b295">
Matrix C elements are defined
in eqn.BO233. Since it is 
addition and multiplication
of nonnegative numbers aik
and bkj, then cij must be
nonnegative number.

<a name="ch13b296">
For doubly stochastic matrix 
A and B, their production
matrix C=AB is also a 
doubly stochastic matrix.
(how about D=ABC ?)
//doubly stochastic definition
2010-07-08-17-27 stop

<a name="ch13b297"> Index begin Index this file
2010-07-08-18-57 start
■ Convex combination of doubly 
stochastic is doubly stochastic

<a name="ch13b298">
Let matrix A,B,C be same rank n
doubly stochastic matrices.
Above discussed that matrix 
  D=ABC ---eqn.BO240
is again doubly stochastic matrix.

<a name="ch13b299">
Below discuss that matrix addition
  E=A+B+C ---eqn.BO241
is or is not doubly stochastic.

<a name="ch13b300">
The definition of matrix addition
is termwise addition. That is
[m11  m12  m13] [n11  n12  n13]
[m21  m22  m23]+[n21  n22  n23]
[m31  m32  m33] [n31  n32  n33]
= ---eqn.BO242
[m11+n11  m12+n12  m13+n13]
[m21+n21  m22+n22  m23+n23]
[m31+n31  m32+n32  m33+n33]

<a name="ch13b301">
If matrix M and N are doubly 
stochastic, then the addition
result eqn.BO242 can NOT be
doubly stochastic. Because
column_one sum of M+N is
<a name="ch13b302"> Index begin Index this file
which is
its value is 2. because
(m11+m21+m31) is matrix M 
column_one sum, value is one.
(n11+n21+n31) is matrix N
column_one sum, value is one.
Adding two one get two. Then 
<a name="ch13b303">
adding three doubly stochastic
  E=A+B+C ---eqn.BO241
has column sum three.
has   row  sum three too.

<a name="ch13b304">
Now assumn 
  p+q=1 ---eqn.BO243
  p≧0  and  q≧0 ---eqn.BO244

<a name="ch13b305">
The weighted matrix sum
  [m11  m12  m13]   [n11  n12  n13]
p*[m21  m22  m23]+q*[n21  n22  n23]
  [m31  m32  m33]   [n31  n32  n33]
= ---eqn.BO245
[p*m11+q*n11  p*m12+q*n12  p*m13+q*n13]
[p*m21+q*n21  p*m22+q*n22  p*m23+q*n23]
[p*m31+q*n31  p*m32+q*n32  p*m33+q*n33]
IS a doubly stochastic matrix.
<a name="ch13b306">
Because its column_one sum is
 =p+q = 1 ---eqn.BO246

<a name="ch13b307"> Index begin Index this file
In general case. 
If we have n doubly stochastic 
 {At: t=1,n} ---eqn.BO247
If we have n non-negative 
 {μt: t=1,n, μt≧0} ---eqn.BO248
such that
 ∑[t=1,n]μi=1 ---eqn.BO249
<a name="ch13b308">
Then the convex combination of 
doubly stochastic matrices
  B=∑[t=1,n]μtAt ---eqn.BO250
is doubly stochastic matrix.

<a name="ch13b309">
Ths column sum of 
  B=∑[t=1,n]μtAt ---eqn.BO251
  colSumj=∑[i=1,n]Bij ---eqn.BO252
  //t-th matrix column sum=1
  =∑[t=1,n]μt{1} ---eqn.BO253
  //from eqn.BO249
  = 1
Similarly, row sum equal to one.

<a name="ch13b310">
Given n doubly stochastic 
 {At: t=1,n} ---eqn.BO247
then all of their elements are
<a name="ch13b311">
The coefficient μt be nonnegative
and sum to one. (eqn.BO248)
The termwise summation
  μt{∑[i=1,n]aij,t} ---eqn.BO254
must be nonnegative.

<a name="ch13b312">
Conclude that 
Convex combination of doubly 
stochastic is doubly stochastic.
//doubly stochastic definition
2010-07-08-19-45 stop

<a name="ch13b313"> Index begin Index this file
2010-07-08-21-48 start
■ What is 'Convex combination'?

Fill with enough air, a basketball 
is a full shape ball. No where
indent inward. This shape is 
called convex.
<a name="ch13b314">
Another key point is interpolation.
Given supporting points a=2, b=10. 
such that other points can be
interpolated relative to [a,b].
For example point 
  c=5=x*2+y*10=(5/8)*2+(3/8)*10 ---eqn.BO255
<a name="ch13b315">
Interpolation coefficients x 
and y have the property
  x+y=1      ---eqn.BO256
  x*2+y*10=5 ---eqn.BO257
if change 5 to any value in [2,10]
x and y are always between [0,1].
<a name="ch13b316">
If point 12 'interpolate' relative
to [a,b]=[2,10] we find x=-1/4 and
y=5/4. Both x,y are out of [0,1]
range. Actually point 12 relative
to [2,10] has extrapolation,
not 'interpolation'.

<a name="ch13b317">
'Convex combination' is 
interpolation under a convex
supporting (convex reference,
convex hull etc.)
<a name="ch13b318">
If a basketball leak air with
indentation. A point inside the
ball interpolate relative to
the indented surface, the
interpolation coefficients
may/may not be in [0,1]

<a name="ch13b319"> Index begin Index this file
What is 'Convex combination'?
Speak mathematically
Given a convex set data sequence
(data can be length/mass/time etc)
  d1,d2,...,dn ---eqn.BO258
Given a weight sequence 
(weights must be pure numbers)
  w1,w2,...,wn ---eqn.BO259
<a name="ch13b320">
weight sequence has the following
(1) nonnegativity. 
    wi≧0 for i=1 to i=n ---eqn.BO260
(2) sum to one
    w1+w2+...+wn=1 ---eqn.BO261
//if wi<0 that is extrapolation
//if data di<0 and all wi≧0
//that is still interpolation.

<a name="ch13b321">
  c=∑[i=1,n]widi ---eqn.BO262
is a Convex combination of 
  d1,d2,...,dn ---eqn.BO258
<a name="ch13b322">
Key point is that 
if wi≧0 for i=1 to i=n 
be true, then eqn.BO262 is 
a Convex combination. 
Any one wk<0 or wk>1 that 
is NOT Convex combination.
(eqn.BO261 ∑[i=1,n]wi=1
 still necessary)

<a name="ch13b323">
On the other hand, if given
'Convex combination', then 
that is given eqn.BO260 and

<a name="ch13b324"> Index begin Index this file
Above explanation for
What is 'Convex combination'?
may not be clear (even may not
be correct) Please ask a 
mathematics expert for better
2010-07-08-22-56 stop

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