Systolic Implementation - Electronics and Communication Engineering Video Lecture - Electronics and Communication Engineering (ECE) | Best Video for Electronics and Communication Engineering (ECE)

One thing, I just forgot to mention, you are
considering rotations, elementary rotations;
there is a vector x, y and we move it to x
prime, y prime. This angle is theta. Then,
we
can work out x prime y prime, actually is,
if you do not know how to work out, you call
this angle alpha. This alpha plus theta length
is p. This new x prime is p cosine, within
bracket, theta plus alpha, and cosine theta
plus alpha, there will be minus sign, Isn’t
it?
That is why; if you work it out that way,
it will be cosine theta, then minus; in my
one of
my earlier lectures I skipped this minus sign
here; cos theta minus sin theta, this is
important.
Cos theta minus sin theta and then sin theta
cos theta. This, I can write down because
this
is unitary. If I move 1 row from orthogonally
taken into the other row, this times x, y
that
is c minus s, scxy, but as I am following
this move, you know that theta, that we consider
typically, this is the opposite direction
like, there is a vector here while you do
rotate it.
So, that it coincides with this axis, so that
I am rotating this plane in the opposite
direction.
This theta is usually, this is a convention
theta. With that theta, cos theta will remain
as it
is. Because cos theta cos minus theta, they
are same. Only this will become plus sin
theta, this is minus sin theta cos theta.
So, the convention will be cs minus sc and
angle is
in the clockwise direction. Yes, clockwise
direction. Just keep this in mind.
Let us just go back to that what we are doing.
That is we had xn, m columns, and we are
minimizing this quantity. Epsilon square n,
that is a norm square of this, you know very
well, this is the usual norm; the Euclidian
norm; just sum of squares. Lambda has been
pulling in here. This was minimizing and this
was giving rise to en, it was giving rise
to
dn minus Xncn norm square, but it is also
equivalent to unitary matrix, Qn, we choose
an
appropriate unitary matrix square matrix.
This is same as, this what we did at last
time
and what did you do? Qn, just Q, this vector,
this works on this. So, that this reality
thing
is equal to the some upper triangular matrix
and then zeros. Existence of such things;
we
have already proved we have discussed using
(( )) and all that that is always global.
Rn
will be perfectly upper triangular matrix
with non 0 diagonal entries; that is invertible,
if
you have got Qn, you have got this matrix
having linearly independent columns.
So, QR will be perfect unitary and this will
have on the non zero diagonal entries and
in
particular all that. So, this into cn and
this vector we considered; we considered the
upper
half. The upper half will be given a name;
un is m cross 1 and lower half, whatever be
the dimension, this what we gave and therefore,
this led to what, optimal filter; c cap n,
this will minimize it, that will simply, if
you take the upper half and solve this equation
Rncn, equal to this. Because, total norm square,
as you know, will be norm square of
plane, this ln plus the upper part, isn’t
it? Ln minus 0 from here, that is ln minus
Rncn, so
total norm square is the summation of, the
sum of norm square of ln and norm square of
this, un minus Rncn.
Since, Rn is invertible, you can make, and
ln is independent of cn. So, only thing you
can
do is, you can make un minus Rncn equal to
0. That will be the minimum possible and
obviously, that will give the best filter.
Because, that is the orthogonal projection
and
projection is unique. This minimizes normally,
that will indeed corresponds to the
orthogonal projection.
See; find out c cap n as R inverse n, your
un of course, you do not have to compute R
inverse n. Rn is triangular upper triangular.
So, Rn into c cap n is equal to this. You
can
easily solve by back substitution. One value
you find out, and then backward, you can do
it in clock. That will give rise to an array,
actually, pipeline manner. You can move it
backward; back substitution. You find out
and give it to somebody and take up the new
data and again, solve it in that way.
It will not really amount to big computation
having big complexity and all that. We will
see that and your epsilon square n was simply
this. Then, we did how to obtain this Qn
we suggested Givens rotation and all. I am
not getting into a recall of Givens rotation.
I
hope you recall Givens rotation, because that
was quite graphic.
Now, suppose, we know Qn minus 1, then how
to get Qn; we considered that also. That
is, first you develop Q bar n minus 1 as 0
transpose, 0 col vector 1. If you apply Q
bar n
minus 1 on this guy, this will be what, you
know this, is nothing, but lambda to the
power half into delta, isn’t it? It is a
diagonal matrix only; 1 square root lambda,
square
of that square of that and so if I take the
1 ou,t take out square root lambda separately,
this is this. This matrix times this means
what, upper half Xn is what, Xn is x n minus
1
followed by 1 row consists of this x 1 n dot,
dot, dot xmn.
If you do this, this into this, this part
multiplies; this comes on top and this comes
as a
row. On that this matrix, this Qn minus 1
will work on lambda to the power of half to
this into this only. So, that will give rise
to a upper triangular matrix and chunk of
zeros;
that means, and multiplied by this. So, that
will give rise to lambda to the power of half,
Rn minus 1, then zeros. This is m cross m.
These zeros will be n minus 1 minus m into
m. Totally, then is up to this is n minus
1, out of which, m is gone and this will be
your x
1 row, isn’t it?
Now, I apply as I told Givens rotation. Actually,
you see is Q is never required; first
understand that Q is never required; it is
required in analysis. If you give me odd n
minus
1, I simply form at the new set of data, I
have 1 slice, 1 row, whichever we stored that.
This Rn minus 1, just lambda factor you bring
in and you have to do Givens rotation. In
the Givens rotation, you have to find out
those cs factors because, by Givens rotation,
I
will annihilate this. It will become 0, but
the c and s factors, I have to apply on the
other
elements now; second column; third column;
fourth column. But those cs factors not
difficult to be computed
First, I annihilate this. I applied Qa bar
n minus 1 into this, I got this. Then I give
Givens
rotation. This is 0 and the first, this matrix
has only 1 entry; here in the first and the
last.
Just, sum of the square root of the square
of this, plus square of this; square of this
plus
square with length that will come here. This
will be zero. This will give rise to a
transformation for c and s, appropriately,
apply that on the first row element and last
row
element of each other column. These last row
elements will get modified; first row
elements will get modified.
Then, you consider the second column. It is
diagonal entry; second entry. That and the
last one, there again, you rotate applying
Givens rotation. So, the last part is annihilated,
but that will not affect this 0 and this 0,
because this 0 and this 0 is origin. So, you
understand that was the thing. Suppose, you
had 1 vector here, you rotate the plane, so
that your axis corresponds; I mean this corresponds
to here; this has come back here,
suppose. Then, if this has come back here
means, only x coordinate yz both 0; 0 0, 0
0.
There if you rotate the 2, this is untouched
and these are 0 0. So, if these are not both
0 0,
things would have got changed. But since that
is origin only, even if you rotate this
plane, the origin does not get affected. It
gets affected, you see, in the first column
these
two zeros do not get affected.
But other columns, third column, fourth column
onwards, the second row entries and last
row entries do get affected, because they
are not zeros. You see the difference. If
you
consider, if I even though I am annihilating,
may be, I consider an example. These are
data; these are zeros; first you had your
data here. First, you are annihilating this.
Length
of this vector is coming here, actually. And
this fellow is getting altered, I do not mind.
Next, you are considering this. You are annihilating
this by appropriate Givens rotation.
That will not affect this firstly. Because,
they had the, I mean, this axis. This axis
will be
under that corresponding, you know, there
will be 1 in this matrix. For this row there
will
be 1. This will not get affected, but second
row and last row will get affected.
There comes the question, as far as, the first
column is concerned, there the second and
last will not get affected because this already
0 0, means origin, isn’t it? I am rotating
this
plane say, in this case yz plane, but in the
case of first column, no effect. Because they
are already 0 and 0. If you pre multiply a
matrix, which targets second row element and
last row element, in this case, you will target
0 here 0 here, so reality will be 0.
Physically, you are rotating this plane, origin
remains at origin only. But that does not
apply to the third, fourth and other rows;
here, this fellow and this fellow will get
altered.
This fellow and this fellow will be altered,
I do not mind. Listen this. Then again, I
consider this third guy; third guy and this
last guy; last guy will be annihilated. That
will
not affect. That will affect and then I apply
the transformation. So, fourth column, fifth
column, they are these third row and last
row; they will and those elements will get
affected, but not the zeros applied in the
first 2, whereas, same logic so on and so
forth.
This Givens rotation thing, we are doing sequentially.
See the mode of operation; you are
first doing here. Suppose that you are targeting
the first column. What is the
computation? You are finding out few things;
1. square root of square of this, plus square
of this length of this vector. That is one
computation you find out because, that will
be
the new coordinate here and 0 here, but apart
from that, c and s you have to find out. C
and s, at least, you know, base by hypotenuse
and sin, what is that called? I mean
hypotenuse that way you see this is one computation.
Once that is done, then you propagate c and
s to this column, this column, this column
and all these columns, so that, first and
last guy are altered; all of them. This is
one line
of computation. Next time, you consider this
again, you do the same operation as you did
here same kind of process here; same length
computation; same c and s computation and
then you propagate to the right hand side.
As you do that, this guy which was handling
this and computing the new length and c and
s, it can take a new column. If a new
column comes, something, it can do that.
Anyway, I will come to that. I am just trying
to give the computation flow. I will give
detail here after a while. This is the nature
of computation. Computations are very much
similar; what will you do for first column,
similar operation do here, and propagate the
c
and s, that column was between these two,
you target, find out the length. Using the
length, you find out this, I mean this c and
s and propagate. So, apply this Givens
rotation; that means, your Qn is nothing,
but Q bar n minus 1 pre multiply, you do that
and then 1 rotation corresponds to first column.
Anyway, you can call it G 1 n. You understand
this rotation matrix. There will be a c,
there will be a s, there will be a minus s,
there will be c and all other diagonal entries
will
be 1, and elsewhere 0s. Typical rotation matrix,
we discussed and where c, where s; that
will depend on who, with particular column
you are, like in this case, I mean that four
boundaries c, 0 0 0, s and last column row
also minus s, 0 0 0, c. Otherwise, it will
be a
diagonal matrix and so on and so forth. So,
that is G 1 n, because I am making it
depending on n I wrote, and then Gmn. Physically
mind, I am doing like this, but I am
never interested in finding out Q. No explicit
computation of Qn is necessary. It is
necessary in our analysis, you are not understanding.
Computation does not need this.
You give me Rn minus 1 and new data, I will
rotate them by those computations.
And mind, Qn, I am not only applying on this.
Qn, I am applying on this also, isn’t it?
Qn, I am applying on this also. What is the
affect here? Let us find out, this also I
have to
find out. You are doing Qn on this by that
method; the previous Rn minus 1 is giving
you
new set of data, just rotate. No need to compute
Qn. Here also, I have to do the same
thing, what computation? Is it not?
Qn, again let us see. First, Q bar n minus
1 and this rotation matrix. Q bar n minus
1
times this. What is Q bar n minus 1? Q bar
n minus 1 is this. That working on this will
be
what; this lambda n to the power half. As
I told, you can always write like this. Like,
these times dn and Qn minus 1 before that.
So, these working on dn minus 1 like, 0 times
the last. Forget the last entry, these working
on dn minus 1. On that, Qn minus 1 works.
There will be Qn, sorry, there will be Qn
minus 1. This one, I forgot to write, Qn minus
1
and these working on, very simple, these working
on last column into dn. This actually
sorry, this is Qn minus 1 working on, no just
a minute. Let me write the correct
expression. You do not mind if I move to new
page. This is your, let me write down step
by step. So, this on this, on that Qn minus
1. These working on these; lambda to the
power half is a factor, you take out first,
this guy on this and Qn minus 1 on that; what
will that be? That will be this vector; un
minus 1 followed by ln minus 1. You are not
following this. What was the un and ln?
If Qn walks on this vector, I take out the
first upper m plus 1 part, I call it un lower
part
ln. If it is n minus 1, if it is n minus 1
and if it is n minus 1, what is this? That
is what it
is. Only thing is lambda to the power of half
factor, has to be brought in now, isn’t
it?
The last guy dn, this part is your, because
this is m cross m, always following this.
This
is your ln. This is not e th variance, sorry,
I have done only these. I have to apply that
Givens rotation thing, that is the final matrix.
I am only applying Qn minus 1. I am only applying
these. But this is not Qn. Qn has to
be applied. So, this is not the thing. But
what is our Qn? Qn is, this is called Tn,
say let
us say sequence of rotation and then that
Q bar thing. That means, this vector, now
you
have to find out I can find out, this is Tn
times this guy. Remember, I am not bothered
about Q bar n minus 1, please see here also.
I am not bothered about Q bar n minus 1 or
Qn and all that, I have to find out these.
You gave me what is n minus 1 and what l
minus 1 is, that you gave me, like Rn minus
1, you gave me this fellow and this fellow.
But what I have done simply; I have brought
in the lambda factor and appended the new
data for desired response at the bottom. From
this side, I am finding out the sequence of
rotations, this side, those upper triangular,
I am triangulizing this data matrix. I know
what they are. And the moment, I know these
things, this will be what? Just a rotation
matrix, isn’t it. One particular value will
be c and s and correspondingly minus s and
c.
So, it will pick up just a pair of entries
from this vector. This vector, if you apply
G 1 n
on that, it will pick up a pair of entries
In case of G 1 n, we know what it is; c 0
0 0 0 s; identity matrix minus s 0 0 0 c.
C will
work on the first guy and s on the last guy.
And also that will come to the first position.
Remaining position is intact and minus s times
first guy; c times last guy; summation that
will come in the last position. So, just these
also will be rotated; these vectors also will
be rotated, isn’t it, by which rotation
angle, that which came from the data side
should be
rotated once by these, then rotated once by
these, rotated once by these, like that. So,
I
have to just do these rotation operations.
No explicit computation of qn is required.
We
will come to these computational things.
Suppose, let us consider m equal to 3 and
now also, consider adaptive filtering problem,
though it is not necessary here. For the time
being, we assume that we are carrying out
with our initial assumption, which our index
of operation n is greater than or equal to
M.
So, that there is no linear dependence problem,
that I have to take care of, mind you.
Otherwise, things are not complete. How to
take care of those indices, those early
indices, where number of in n was less than
m, then Qr does not take this, where number
of columns is more than the dimension of the
space. You cannot have Q matrix. You
understand that. So, how to take care of that
case, that we will see; that is another story.
But suppose, our index n is, we have all that.
In that case, I will just give you a partial
idea of the computation, then again, I will
come to that initialization thing, where n
is
less than m. And there, I will consider adaptive
filter problem, where these various
columns of the data matrix will not be independent
like x 1 n, x 2 n, x 3 n. It will be like
what we did in the lattice case. First column;
next will be z inverse of that; next will
be z
inverse 2 of that; z inverse 3 of that; that
way, which is purely filtering problem.
At pre windowed case, that is 0; I mean, first
column as it is, then next you will have 0
followed by data, then 0 0 followed by data;
that kind of structure we will assume. And
remember, that 0 0 structure is an upper triangular
matrix, isn’t it? That too make use of
that, we will be using this. But there is
no direct relevance, but there is upper triangular
structure. We will make use of that, which
is why pre windowed thing will be taken up.
Just to give you an idea beforehand, there
is computation. Suppose, this is a general
case
I am considering. But for the adaptive filter
case, we will be simplifying it, x 1 0. For
the
time being let us assume what starting index
is n equal to 1 not 0. Like, in the lattice
case
we started at n equal to 0 and went onwards.
Suppose, n equal to 1 is the starting index,
like that time I said initial condition at
minus 1 remember, in the lattice case, I get
the
values at minus 1. Here there is a condition
to get 0. This book also does the same. So,
I
just picked that from here, so first guy x
1, second dot dot dot dot x 1 n, dot dot dot;
this
is a current index. What it will do, do the
businesses at current index please. Do not
go to
the starting index then that dependence independence
will come up.
Suppose, this index is sufficiently large
when doing it here, I am just giving you a
brief
idea beforehand. This is not final. Also,
this will not be final; there are some other
linear
sections that I have to consider. I will consider
tomorrow. But suppose it is so it will
always assume the cs things. I will consider
n equal to 3 only otherwise, I cannot draw
an
infinite array, m equal to 3. What happens,
this kind of round; this circle; this is one
kind
of processor; this is another kind of processor.
This processor will compute, will take up
that particular row, where the 0, where the
data has to be eliminated and therefore, c
and
s can be computed, like first column; there
you generate cs and then spread it to
remaining columns; remaining 2 columns.
So, this will just do rotation; this will
do computation. After that, you target second
column. Here, will be the one more thing,
we will see all these. It will become a triangle
array here. This will propagate, whatever
I have here, this goes through. Same thing
comes. Those who have done my course on VLSI
telecom, they know this. Sometimes,
data propagates through position. These are
just broadcast, not broadcast, but they go
through; same c same s is transmitted. I will
give the function of these two processors
separately. That is how you draw systolic,
as you remember, if the order of processing
unit you have, you draw it. It functions separately,
you remember, dg and all that we
used to do.
It is not that formal way of doing systolic
(( )), where you are doing dg and
transformation; this is just directly using
intuition in things. You did not do any course;
you should have done. That also was mathematics.
Here it is d 1 dot dot dot dn. So,
remember these guys, since I have told this
for you, the same processor is used; same
type of processor. What it will do? It will
do some kind of c and s computation. It
propagate them through this; operations do
not change; processor is same; same
processor.
This is not complete. There will be just 1
section for the triangularization, but this
is not
enough. But, let us just see the computation
thing. What these guys do; this guy does;
I
am drawing its action. It has some storage
also; it is storing some value x. What is
this x;
we will very soon see this. Suppose, x taking,
I will be using the x many times.
Remember, the x is just related with this
particular processor. Same x, I mean, this
x will
not be the same thing and all just you find
the x here. Suppose, it is un. It will generate
two quantities, c and s. How? So, let us do
some kind of analysis now.
It is like this; x and uin. What it will do?
It will first compute x prime as what, this
is the
data you have to multiply by that lambda thing.
This will be, suppose, the triangle part
was there. So, lambda to the power half into
Rn minus 1 and then this data. That is the
situation. So, this is multiplied and then
square square added, length is found out.
Firstly,
you should take, if you suppose this data
is (( )) and this data is 0. Then, life is
very
simple. If this data is 0, it is already annihilated,
is it not?
Then, your c should be 1; s should be 0; I
suppose. C working on this, and s working
on
this; c is 1; s is 0. You do not have to really
go through all this computation, in that case.
In that case, your life is safe; I mean you
do not do so much of computation. You are
following me or not. If this is 0, this can
be 0 or non 0. If it is 0 that is, if obviously,
c
equal to 1; s equal to 0; isn’t it? This,
you rotate by 0 degree; cos theta 1; rotate
by 0
degree or otherwise, we will do it in the
matrix form; just 1 times this; s times this;
s
times this; 1 times this; s is 0 0 times this;
1 times this; you get 0 comes back here. This
goes back here. This is very simple case.
When it is not, you find out x prime. This
is the length, then in fact, let me say that
you
consider these instead of x. This goes for
x; not x prime. This goes for x. Unless it
was x
next will be replaced by this guy. Unless
in the location the x was there, now x is
to be
replaced, but before that I am just saying
this x. Now, but I will come to that later,
just
compute this, I will name it later.
Look at x. Certain things I did not say regarding
rotation, that is, that will come. Look at
x. x is what? x could be here, say, x corresponds
to x axis and this corresponds to z axis
or say, y axis; another x is at 0 and they
are not present here. If this is here and
u here,
this is very simple case; theta in the opposite
direction, isn’t it? Theta in the opposite
direction; c will be positive; s also will
be, cs minus sc, that s will be positive.
Cos theta
minus sin theta and that give rise to that
thing; theta negative means, it gives rise
to,
which is all given in the beginning. So, s
was coming to be positive. That is the case.
But it could be the other way also. Then,
what is the new value of the coordinate?
Coordinate will be rotated. So, this length
itself with a positive sign will be the new
value here; this is one possibility. That
is, if x is positive, then either you are
here or you
could be here. If you are here or here, x
is negative. We will consider that later.
If x is
positive, either here; you rotate this way
or you are here; rotate this way, but either
case,
this much length with a positive sign will
be your x coordinate. You are either rotating
this way or this way, but this much length
with a positive sign. That means, if x is
positive, then just this with a positive sign,
will be your new x coordinate.
On the other hand, what happens to s here?
S, we are rotating in the opposite direction
in
positive theta; counter clockwise. So, sin
s will be minus then when theta was this way;
I
had plus s coming, when theta was this way;
I will have minus s. Remember that. I am
not targeting s at the moment I am targeting
the c. On the other hand, it could be here
and
again, I could be here or I could be here.
If I am here, I rotate this way, but by how
much
angle; this angle only. And what is the x
value? Negative, it comes out to be negative
of
that length. Square root is a thing, is a
length, I find it is positive. So, I have
to give a
sign to that. Sign should be negative, if
x is here. On the other hand, if it is here,
even
then I rotate, but sin remains negative.
That means, new x prime will be. How to take
the sin of x fellow? X by mod x, you all
know this. This is the convention, this analysis
will be different. I am taking this way or
that way because; this comes in the mind first;
this way. No, what you saying is, instead
of rotating this way, you rotate in this way.
That could be done, I am sure. This is just
a
convention, I mean, there is nothing fixed
about this. Make no mistake; there is nothing
fixed about this. But, you have to follow
a convention and stick to that. I am following
a
convention, given to basis guy, who is taking.
I am just following it; I am not ruling out
those things. But the advantage, this theta
cos or sin computation, becomes very simple.
I am telling you why. If you just rotate only,
because theta is always accurate. If you just
rotate by this much; or this much; or this
much; or this much; theta is accurate
computation becomes, otherwise, you have to
consider 180 plus theta, 180 minus theta,
and all those things will come up. That is
the thing. That is why; you are very smart
guys
you will understand more than what we understand
here. So, this times, what was earlier
x, that will be replaced by this fellow. Now
c and s, c is, if you are rotating this way
or
rotating this way, or if you are rotating
this way, or this way, c is always positive.
Isn’t
it? Because c is cos, is it not?
Our convention was this way, but even, if
the angle is in the opposite direction, cos
of
that is positive. Similarly, here counter
clockwise and counter clockwise, both are
c.
Problem comes only with s. S again, both thing;
whether in this direction and this is
positive or negative, or this is in this direction;
this positive or negative. s is more
complicated. But again, you know that stage
elementary trigonometry, all of us, not me.
Certainly, you people are very smart on this.
So, c will be what simply, I mean, do not
bother about sin and all, just take the mod
of the base, mod of the hypotenuse, the ratio,
isn’t it? Because, c is always positive.
That means, c will be your mod x divided by
this quantity. In fact, it is not just x.
There
is a lambda factor. Mod x divided by, you
can take the mod of this, or mode of the same
way. This is your c. Remember, computation
is in the processor, you need a squaring
operation; you need a square root operation.
These are the computations. Then you will
have done research, they modify this data;
I am not going in to, where squaring operation
and square root operations are not required,
by again mathematical, I mean tricks and all
that. This is just simplified thing, but those
are beyond this course. Just giving an idea
about QR factorization and that thing, it
is very interesting thing.
Especially, when your number of input is very
large, suppose 1000, then these arrays,
because otherwise, if you get 1000 line thing
coming, every moment of time, then so
much of computation.
But if it is systolic and pipe line, there
will be all pipe line, which you all know
about
that. I will come to the pipe line lattice.
They know very well, I mean that you can apply
cutset and (( )) and all that, that you know,
is it not? Or you have forgotten. Cutset
retiming and all you know very well; I can
just apply cut set here; cut set here; cut
set
here; re-sequence the inputs and all that.
About that I mean, to them, I will tell. It
is not
difficult because, there is no feed back here.
So, cutset are, even if I do not use the term
cut set, it does not matter.
Which page, where was I? Here. Now, comes
the more difficult thing that is; s was,
when I was considering this much, then s was
coming and what was the s? Just sin theta.
When angle was in the counter clock direction,
it was just sin theta s was coming; no
minus s. That s was this much by this much.
When here, that is, if x is positive, this
is
also positive. Then, take the ratio of the,
I mean, this by hypotenuse. That will be the
sin.
When this is negative, this is positive, then
sin theta instead of s, it will be minus s.
Isn’t
it. So, that time, you take the ratio of these
two; minus will come.
When here, you have, this is negative and
this positive. That time in the clock wise
direction, plusses will come. But, when this
is negative and this is negative, just a minute
I made a mistake. When this is negative and
this is positive, you are moving in this
direction; counter clock wise. So, minus s,
if I make mistake please correct me. And the
other way, both are negative in this direction,
plus s. So, you have to check the sin of
both this part; x part and that uin, which
is coming; uin is this along this axis. I
am just
giving you from the book, s should be, let
us see.
This is x by this means, if the x is in this
direction, this is plus. In this direction,
minus
simply. In fact, I should take this ratio,
mod uin. Let us consider this; mod uin square
by
this; what will be that be? This has the sin.
So, mod, that is a mod for magnitude and if
it
is in this direction plus, if it is in this
direction minus, that will come up here in
this ratio.
This will be plus, if x is plus. This is minus,
if x is minus. This ratio, mod uin, forget
about all sin, mod uin by hypotenuse; that
will give that magnitude of the sin.
Now you check, you will get this thing, that
when both are positive, both of that and
function kind things will come up, isn’t
it? Just you check, x by mod x, this can be
the
plus 1 or minus 1, and mod uin, this mod uin
square, you write as this; as mod uin mod
uin. And this by uin, you write as, this into
uin, this quantity, is plus 1 plus minus 1;
this
quantity also, x by mod x also, plus minus
1. So, both the signs will be taken care of
here, as per (( )). But here, we have to take
the sin of uin, just uin. So, I tell, it looks
like,
anyway, it is very simple otherwise, I mean,
there is nothing conceptual here. C and s
are
computed. Let us come back to array; what
this array is doing, mind you, this is not
complete story.
This is just part of that entire optimal computation,
just that diagonal matrix of the R
matrix will come over here. That is only thing
I am saying, I need to complete this by
tomorrow, actually it is not one day affair.
So, c and s found out. You have data x 0 0
0; this is upper triangle matrix given to
you.
New data has come up; x 1 n and then x 2 n;
what you did, this guy did, it annihilated
this fellow; replace x by that x prime and
this appropriate sign and all this is here,
stored,
because next time, another data will come,
next time square of that x prime multiplied
by
lambda plus this square, next value x 1 n
plus 1 square root, again same operation will
be
done. So, it is stored for using the next
law cycle and still the cycle and all that.
That is
stored and in our mind, you know this guy
is 0. We do not have do anything to
implement that, this we know in our computation.
Only thing, new value, I am interested in
new the Rn. So, new value is known. This cs
goes to all these fellows. These guys, these
people had, I mean, have their things stored.
Like, suppose this, forget about this, to
be x. Suppose, this you consider to be x.
What it
will do? This column gets c and s information
and so it will find out cx. What will be the
new things? Cx, cs and s times this; 149 and
minus s time this and c time this. Lambda
to
the power half has to be here, on these lambda
to the power half, because Rn minus 1
into lambda to the power half. So, always
the lambda to power half, instead the multiply
and activated all the elements here. That
means what it will do?
It will replace this x by; this is the processor,
as I was writing. Let me just quickly
complete this processor thing; else x prime.
This is just repetition of what I wrote. So
along, you tell me what you want, I am not
thinking, I will just follow you; x by x prime
into uin by mod x. Well that will do. I am
not checking, I have no time for checking.
Both the signs are required. I am not so sure.
This, I am writing based on the input of this
guy. So, tomorrow I will see, if this is wrong,
I will have his marks deducted from mid
sem. This is the processor and other thing
is this processor. I have to define this
processor.
These processors, they take cs. They take
new data; new data means x 2 n say, for this
guy; x 2 n, there is the new data say, xin.
Here x 2 n, x 3 n, like that x 2 n. And you
know
this guy. So, what it will do; this will be
replaced by, x will be replaced by what, sorry,
and something comes out. What comes out? Something
we call this say, u out, its
purpose will be seen, and something goes in
this direction, this is c s; cc, ss; xin comes
in
and u out goes out. What does it do? It was
storing some data x, that will be modified
by
new x. What was that x; this fellow. That
x will be replaced by, I am blindly writing,
this
x will be replaced by what; c times lambda
to the power half x, isn’t it? c times lambda
to the power half x, and s times this new
fellow xin, that will go for new x here and
what
will be happened here? This guy will be called,
u out, whether you should call u out n.
But, I am just dropping the index, u out n,
is just minus s times lambda to the power
half
xin, plus c times, sorry, minus this is x
plus c times. This, we will consider in the
next
class tomorrow.
Thank you very much