# Classical Physics Prof. V. Balakrishnan Department of Physics Indian Institution of Technology, Madras. Lecture No. # 13

Save this PDF as:

Size: px
Start display at page:

Download "Classical Physics Prof. V. Balakrishnan Department of Physics Indian Institution of Technology, Madras. Lecture No. # 13"

## Transcription

1 Classical Physics Prof. V. Balakrishnan Department of Physics Indian Institution of Technology, Madras Lecture No. # 13 Now, let me formalize the idea of symmetry, what I mean by symmetry, what we mean by the constant of the motion and so on. And this is now going to take us into a very general relation between the concepts of symmetry on the one hand, in variance on the other and conservation loss. All these three are sort of jumbled up in our minds and they are closely linked to each other, and we will try to formalize this relationship. The first step in this regard was taken long ago by the mathematician M. E. Noether, the beginning of the 20 th century is one of the greatest algebraist of the last century. And she discovered this theorem in a much more general context, than the one I am going to give it. And the one I am going to state here applies to the simple case of Lagrangian mechanics, but the theorem itself is applicable in much greater generality. So, let me write this down. (Refer Slide Time: 02:01) Noether s theorem and it is going to formalize the connection between symmetry invariance and conservation, conservation principle. And of course, reading from conservation

2 principle is going to be is going to have conserved quantities; such as total angle of momentum or total momentum, total energy and so on. We have all follow from special cases of this general relationship, which leads from a symmetry right up to a cons conservation law. On the other side, Noether s theorem going to colors, how this happens, the idea is the following. (Refer Slide Time: 03:25)

3 Suppose you have to give an example a two dimension motion a particle moving in two dimensions and let us suppose this particle moves under the influence of a force. So, you have two dimensional motion on a plane and the Hamiltonian, in this case is q 1 q 2 p 1 p 2. And let suppose these are Cartesian components for instance, let suppose that this is equal to as usual plus a potential, which would in general depend on q 1 and q 2 and p squared it stands for p 1 squared plus p 2 squared. Now of course, if this potential has circular symmetry, which is a two-dimensional analog of rotational symmetry. Then, we know that the system is invariant these Hamiltonian is invariant under rotations in the plane. So, if this is a function of q 1 square plus q 2 square to the power half say, the function only of the distance from the origin when if I make a rotation of the coordinate axis in the q 1 q 2 planes; the new Hamiltonian is not going to look different from the old Hamiltonian; it simply not going to change at all. You would expect, that the physics of the problem will therefore, not change, what will remain invariant. If I take for example, the Euler Lagrange equation and solve them, what would be invariant under this set of transformation that is instead of choosing; these coordinates q 1 and q 2. I choose some coordinates q 1 prime and q 2 prime, which is tilted which are tilted, these axis are tilted with respect to the original axis by some angle alpha I make this transformation and choose q 1 prime q 2 prime; and cor correspondingly p 1 prime p 2 prime as my coordinate as my generalized coordinates and momenta, what is going to remain invariant? Hamiltonian does not change, so what would remain invariant the differential equations would not change in form, they would look exactly the same in the new coordinates in the new variables as they were in the old variables. And what is consequence of that, the equations of motions do not change, in the old coordinates or in the new coordinates they look exactly the same. Therefore, what happens can I assert that the solution does not change, looks exactly the same as before; can I add that well apart from adding to constants and so on. Can I assert that a given solution does not change, can I do that, can I can I do that? Well, we also must be careful about initial condition and so on.

4 So, the numerical values in the unprime coordinates and in the prime coordinate variables are different of course. So, can you assert that the thing does not change at all what does not change, what does not change? Is it not possible for example, suppose take a very, very simple example. (Refer Slide Time: 06:49) Suppose, I have an equation x square is equal to 4 look at this as 1 example and I want you to impress upon you what the meaning of it I have an equation x squared equal to 4. This equation is invariant under a change of coordinates, which takes x to x prime, but x prime equal to minus x. So, what has remained changed this implies x equal to plus 2 minus 2 you have 2 solutions, so what has remain unchanged. Now, I look I work, I look at this equation in the primed coordinates what does it look like. So, say let x prime equal to minus x, then this equation gets transform to x prime squared equal to 4, what are the solutions here. Suppose, I look at this solution x equal plus 2, does it remain unchanged, what happens to it, it becomes minus x prime equal to minus 2. So, this solution corresponds to that and that solution corresponds to this. So, what is remained unchanged, the set of solutions is unchanged. Once you understood that you understood a great deal of the secrets of the universe. And then in invariance it is the full set of solution; that is unchanged, the solution

5 space is unchanged individual solutions would go from one solution to another, but, within the same set. This is the meaning of invariance dynamical invariance obviously, so it is the full set of equations that is going to be unchanged. A given solution will look like some other solution after you transform but, the full set is unchanged. So, therefore, we now began to understand what we mean by dynamical symmetry? The dynamical symmetry of a given system is a set of transformations of the face space variables, such that the equations of motions do not change and as a consequence the set of solutions do not change, does not change. So, that is what I would mean by symmetry of the system. Now, let us formalize it let us write this down the following way, I will do it first in the Lagrangian framework and then in the Hamiltonian framework, where it will become much clear. (Refer Slide Time: 09:26) So, let us suppose you have a Lagrangian L of q q dot ignore the t for a movement it is not allowing; and I make change of variables from q to something else, I would like to parameterize this, I would like to make a set of continuous change of variable. For example, when I rotated the coordinate system I could have chosen a whole lot of alpha s, infinity of alpha s, any number of angles right all between 0 on 2 pi. So, I would

6 like to parameterize the transformation by a certain parameter or a set of parameters. And let us call that set of parameters alpha and therefore, let me call Q of alpha the coordinate in a frame, which is rotated by an angle alpha as alpha changes this Q of 0 alpha becomes something else. And Q of 0 is my original Q start with an original core set of coordinates and that is why there is no rotational angle at all. Then I start going to sets of new coordinates each time parameterize by certain alpha and let me call that Q of alpha. The Lagrangian here goes into the Lagrangian Q of alpha Q dot of alpha and let me call this Lagrangian L prime L prime is equal to L, when alpha is equal to 0 by definition. Now I ask alright how would be equations of motions remain unchanged, if L prime is equal to L, they would not change right. (Refer Slide Time: 11:14) So, let us say invariance implies L prime equal to L which in turn implies that d L of Q of alpha Q dot of alpha over d alpha must be 0. It means this Lagrangian cannot depend on alpha, even though you made a change of coordinates. And think of this potential problem think of the potential is a function of Q 1 squared plus Q 2 squared. If I now make a rotation Q 1 prime is Q 1 cos alpha plus Q 2 sine alpha and similarly for Q 2 etcetera. And now you can easily see that Q 1 prime squared plus Q 2 prime squared is the same as Q 1 square plus Q 2 square and therefore, the Lagrangian does not depend on this alpha at all. But, the consequence of this immediately say s delta L over delta Q d Q over d alpha,

7 because L depends on alpha through Q of alpha plus delta L over delta Q dot dq dot over d alpha must be 0. Because L is not directly a function of alpha it depends on alpha through Q of alpha and Q dot of alpha this must be 0. But, that immediately tells us that d over d t delta L over delta Q do; that is what the Euler Lagrangian equation is for this d over d t delta L over delta Q dot, d Q over d alpha plus delta L over delta Q dot times d over d alpha of d Q over d t. (Refer Slide Time: 13:20) But alpha has nothing to do with t, this is just a set of coordinates which I am using a transformation right. And if you look at the trajectory for example, at some instant of time if this point here is q, which is equal to Q of 0. If I go to a set of changed coordinates the numerical values of these quantities would change and the new trajectory would perhaps look like this. And this point is map to this point, if I change the alpha and let suppose this is an infinite decimal change. So, this is Q of delta alpha a very small alpha rotation for example, then what is this equal to this is Q of delta alpha at time t, at some instant of time t this is at time t. And of course, this Q changes in this fashion of a function of time at the particular instant of time. I look at this coordinate or set of coordinates in the original frame of reference in the original coordinates and in the transform coordinates. And if it is in infinite decimal transformation of this kind,

8 it is quit evident that this is equal to delta Q over delta alpha d alpha for delta alpha. So, let us write its better d Q over d alpha delta alpha. So, this alpha has nothing to do with t, it just changes of frames of reference or coordinates. Therefore, you can write 0 equal to this equal to d over d t delta L over delta Q dot d Q over d alpha plus delta L over delta Q dot d over d t of d Q over d alpha, which of course. Immediately is d over d t of delta L over delta Q dot d Q over d alpha and that is 0. So, what does that imply, the quantity in the bracket is a constant of the motion, this implies immediately that delta L over delta Q dot d Q over d alpha equal to constant. It is conserve and therefore, I can evaluate it at any instant of time including t equal to 0 in the original coordinates but, in the original coordinates, what does this become what is delta L. So, this becomes delta L over delta Q dot and what is that equal to this quantity here, it is the original p. So, this is equal to p d Q over d alpha at alpha equal to 0. I can evaluate it at any instant of time including t equal to 0 and therefore, even the slope d Q over d alpha and a Q at alpha equal to, at t equal to 0. So, that gives me a prescription for finding the constant of motion, corresponding to invariance I can generalize this to many variables. Now not just the single alpha, but a whole set of alphas if I rotate in three dimension for instance I would have 3 rotation angles. And then I would have three constants of the motions corresponding to three angular momentum; we will work this out in terms of some simple exercises but, this is the idea of Noether s theorem, is this the most general way you can write this theorem is there something else you can do which immediately generalizes this theorem. Well, remember that I said that a Lagrangian is not unique, you can add the total derivative of function of the generalize coordinates and time to it. And nothing is going to happen total time derivative could be added to it. Therefore, it is not necessary that invariance implies that L prime equal to L. So, how could L and L prime differ and still get you have get away with the fact that the equations do not change, you can add a d f over d t to it right. So, you work through the whole thing again by saying L prime is not equal to this is not equal to 0 but, the difference L prime minus L to first order in delta alpha equal d f over d t you plug that whole thing back again. And then you discover there is an extra contribution

9 here, which would depend on f. And that is the most general invariance you could have this is the content of Noether s theorem, what is absolutely crucial for it is that the set of transformation must be continuous. If differentiated with respect to the parameters and they must be connected to the identity. In other words you must have a situation, where you have the original coordinates and then you make a transformation, which takes of continuously from no transformation at all. So, this is a connected set of transformation parity is not one is not such a connected set of connected transformation. It is not a continuous transformation because, we go from a right handed coordinated system to a left handed coordinate system you cannot do. So, continuously it is a discontinuous transformation but, rotation is a continuous transformation, a shift of the origin is a continuous transformation, a shift of the original time is a continuous transformation. Is there any other continuous transformation you are familiar with you can scale things out. I can always multiply the coordinates by constants, I can share the coordinates I can do all sorts of linear transformations; is there anything else, any other physical example of a continuous transformation, which is nothing to do with the coordinates. Well, they are all on the coordinates, these are all transformations induced on the coordinates but, there are other transformations. For example, if you go back to the example of a charge particle in an electromagnetic field, you can make a continuous set of gauge transformations. It could be continuous right you could start with an infinite decimal and then keep going and that would again be a transformation, which would lead to a conservation principle; in this case it leads to the conservation of charge. So, at last to we begin to understand where the conservation of charge comes from it comes from the gauge invariance of the electromagnetic field equation after all equation electromagnetic. So, this is really the principle that underlies what the conservation law is trying to tell you. Now, having done this we work out an example with regard to this application of this Noether s theorem yes. You can evaluate it at any point, the point is at any point the whole idea is I would like to write it in terms of a quantity which I know which is p. But of course, this p I need not have

10 started with my original coordinate system like this. I could have started like this at some finite value of alpha, I call that my initial p and q. So, it does not matter, the whole point is both terms in this bracket have to be evaluated at the same value of alpha, you cannot do this at different values. So, if I did at alpha equal to 0, the other term also had better be done at alpha equal to zero that is the only point. Now, you could ask alright, so much for the Lagrangian situation to summarize, what is happened is we found if the Lagrangian is invariant under a continuous set of transformations. When Noether s theorem tells you there exist a corresponding conserve quantity, there are as many conserve quantities. As there are parameters for the transformations in three-dimensional rotation, there are three Euler angles because of parameters. And therefore, you have three conserve quantities, if you shift the origin in three dimensions, once again there are three constants by which you can shift the origin, 1 for the x, 1 for the y, 1 for the z direction. Therefore there are three constants once again and they would turn out to be the components of the linear momentum, for rotations it is the angular momentum; you can even change the origin of time. And if the equation of remain motion remain unchanged then it means that there is a constant of the motion, in a radian constant of the motion and that is the Hamiltonian itself. So, each time you see that conserve quantities arise as a consequence of this principle, there is a certain symmetry in the system, which leads to an invariance of the equations of motions. Therefore the set of solutions which leads to a conservation principle, now let us go and ask in the Hamiltonian framework what does this look like, what would this mean, what would the constant of the motion imply. And that is even more interesting let me do that, little bit of preliminary work is needed.

11 (Refer Slide Time: 23:20) So, let us look at it in the Hamiltonian, this framework I have a Hamiltonian function, function of generalized coordinates and generalized momenta. I am only going at look at autonomous systems for the time being. And I know that the equations of motion are such that q dot is delta H over delta p i p i dot is minus delta H over delta q i. And I also know that there exists canonical Poisson bracket relation, this ensures main standard structure Hamiltonian structure.

12 Now, I make a transformation, which leaves Hamilton s equations of motions an invariant. Otherwise, you do not have a symmetry at all to start with what set of transformations leaves Hamiltonian equations unchanged canonical transformations. So, it is quite clear, that whatever symmetry transformations. We are talking about must to start with the canonical transformations that may not be enough you have to start with canonical transformations. So, this thing here must go over into something which looks like Q i dot equal to delta k over delta Pi delta Pi. And moreover you must have Q i P j equal to delta i j this ensures that it is canonical but, for Hamilton s equations remain unchanged the Hamiltonian must remain unchanged, so that the set of solutions look exactly the same. So, you need something more, you need those canonical transformations for which k is the same as H. Otherwise it is not going to be a symmetry of course, into some other Hamiltonian and that is not a symmetry actually. So, dynamical symmetry transformation is one, where Hamilton s equations not only remain unchanged in form. And the new Q s are canonical coordinates but, the new Q s and P s are canonical variables but Hamilt the Hamiltonian itself does not change in form. The functional form k must be the same as the functional form H. But, even without that this is what a canonical transformation is now can we say something more about it what kind of transformations a canonical transformations. (Refer Slide Time: 26:33)

13 Let us look at that we need to understand that, how to we do this well let suppose x equal to the q s and p s q 1 to q n and p 1 to p n. And let us suppose you make a canonical transformation to a new set a variables let us call it see which is capital Q capital P. Once again I work in this 2 n-dimensional face space and I have these Poisson bracket relations. Now, what do these bracket relations imply for example, this relation what does it mean says Q i P j equal to delta i j implies this is equal to a summation from k equal to 1 to n delta Q i over delta q k delta P j over delta P k. It implies this relation, because that is what is a meaning of Poisson bracket? So, it says that this set of relations must be true, when you calculate this Poisson bracket of the new Q s and P s you going to have to get delta i j, but that is a definition of the Poisson bracket. And it says this quantity must be equal to delta I j, it further says Q i Q j equal to 0. So, you also going to have Q i Q j equal to 0 and P i P j must be equal to 0. So, that equal to a similar relation with Q j here and Q j there and similarly P i and P j there. So, you have all these three sets of relations must be valid; otherwise it is not a canonical transformation. That looks very messy and you could ask what does it mean, what does it finally, mean is there a simple way of understanding it. (Refer Slide Time: 28:40) The answer is yes there is recall that we have defined this matrix J, which was equal to 0 unit matrix minus unit matrix 0. And I put an n here to show these are n by n matrices we

14 define this matrix J and then I said Hamilton s equations take on a very simple form. And they form that this set of equations took on here was x dot equal to J gradient of H an extremely simple form that we took on. So, this full set of equations can be summarized in just that single line and of course, when you make a canonical transformation. And go to Q and P, then once again this thing here would says that c dot equal to j gradient of k, but the gradient in the new variables; so gradient with respect to the components of c and with respect to the components of x. So, these are very simple structures, now you can ask we also saw that the Poisson bracket of a with b could be written as a gradient of a transpose j the gradient of b. So, you can ask can this set of equations be written an a simple form and the answer is the yes you can take the Jacobian matrix delta c over delta x. This stands for 2 n by 2 n matrix in which each component of c is differentiated with the corresponding component with the components of x here and you have a 2 n by 2 n matrix. Let us see Jacobean matrix for the transformation, that matrix transpose J and that matrix once again must be equal to J. And that is what the content of this set of equations is little bit of work and you can write down the entire set of canonical transformations any canonical transformations is canonical if and only if this Jacobean matrix J, the Jacobean transpose J, the matrix equal to J itself. Suppose, you did not have this suppose you did not have this, then it says matrix transpose matrix equal to the unit matrix what you call such a matrix. Transpose r, transpose r equal to I. Well you do not call it unitary you call it orthogonal if it is real then of course, a unitary real ma real matrix which is unitary is orthogonal. It says matrix transpose equal to inverse of the matrix that is an orthogonal matrix, this is not quite orthogonal. Because, there is a J sitting there always, I call it pseudo orthogonal it is like an orthogonality condition. We should by now expect this J to appear everywhere to pop up everywhere in Hamiltonian mechanics it is acting like some kind of metric in the space of Q s and P s. So, this is the generalization of the condition of orthogonality you call it pseudo orthogonality, there is a name for all such matrices.

15 (Refer Slide Time: 32:11) So, let us look at all matrices if a matrix M transpose J M equal to J, then M is called the symplectic matrix and of course, this J has got marvelous properties, because recall. So, any matrix that satisfies any 2 by 2 n by 2 n matrix face spaces always even dimensional in Hamiltonian mechanics at this level. So, any even dimensional any 2 n by 2 n matrix, which satisfied this condition is called as symplectic matrix. And now it is a simple matter to prove that every simple symplectic matrix has an inwards that is obvious from here actually, what is the inverse of n well all you got to do is to use this once again and it is going to be related to be M transpose. Just as if it would have orthogonal M transpose would be M inverse. Now this is not quit true it is related to M M inverses related to M transpose with J s on either side and so on. So, symplectic matrix has an inverse and we know canonical transformation must have an inverse transformation. So, it is better have an inverse, when the next thing you can show is that the set of symplectic matrix is from a group. The product of two symplectic matrices is also a symplectic matrix. Physically what does that imply for canonical transformation? I make one canonical transformation and then make a second canonical transformation it is equivalent to a single canonical transformation.

16 So, there is an inverse and there is a composition law and these transformations form a group. So, the set of canonical transformation for an n degree of freedom Hamiltonian system form a group, it is the group of 2 n by 2 n symplectic matrices. (Refer Slide Time: 34:57) And this group has a name and this group is called Sp 2 n. And since we are working on the real because you cannot complex over the whole thing it is called as p 2 n comma R. That is the group of canonical transformation. No a symplectic matrix cannot be singular it has an inverse it guaranty to have an inverse is J s symplectic matrix itself. J itself is one you can see that right away, because J transpose that is the same as minus J, but j square equal to minus identity. So, this has J of course, it is true immediately what is the canonical transformation corresponding to choosing that Jacobean matrix to be J itself Q equal minus p, p equal to q the one we those with yesterday that is the transformation. So, that was a simplest of transformation the first canonical transformation you can think of, where the coordinates in momentum actually interchange minus sign. And that corresponded to choosing M equal to J itself, this is a fairly large group every group of matrices has a certain number of parameters independent parameters the group of

17 rotations in a plane has one parameters the rotation angle. The group of rotations in three dimensions has, in three dimensions three parameters. The group of rotations in three dimension has three parameters there are three Euler angles which you need to go from any coordinate system to any other by rotation. So, it has three real parameters we say that the group of orthogonal matrices with determinant plus 1 S O 3 in three dimension, S O 3 has three parameter. How many parameters does the group of rotation in n dimension have it is the group of orthogonal matrices in n dimensions with determinant plus 1. You would not would like look at proper rotations how many parameters does that have, how many angles are needed to specify a rotation in n dimensions. Any angles why do you say that, is a good guess why did you say n. (()) Very good point with respect to each axis you rotate and each axis there are n independent axis and therefore, is argument is you have n angles. But, what happens in 2 dimensions there is only 1 angle, how do you say that, how do you define a rotation in 2 dimension. What is a rotation in two dimensions 2 euclidean dimensions rotation, where is the z, there is no z, about the origin, rotation about the origin. So, it is rotation about a point there is no axis it is rotation about a point, rotations are little trickier than you imagine, so let us do that.

18 (Refer Slide Time: 38:25) In 2 D I rotate from this set of axis to that set of axis, parameterize by single alpha here. So, here is x y here is x prime y prime. So, the group of rotations which comprises the group of matrices which are orthogonal, real entries and has determinant plus 1, can always be written in the form this contain. So, all matrices of the form S O 2 contain all matrices of the form cos alpha, sin alpha minus sin alpha cos alpha. And what does a rotations do, it takes the x y takes the original coordinates makes each goes to new set of coordinates each of the new components is a linear combination of the old variables. And it is homogeneous the set of transformations is homogenous, because the origin remains unchanged, no constant is added not a translation rotation. So, linear homogeneous transformation, which leave a point unchanged the origin unchanged. Therefore, it is homogeneous is a rotation it must have determinant plus 1 otherwise the axis get flipped the handedness gets flipped. So, linear homogeneous transformation, which it has determinant plus 1 it is called unimodular is what a rotation is; now how do you define a rotation in three dimension, what is the basic property of a rotation. It will a give lengths unchanged distance between any 2 points is unchanged, that is the basic property. So, all you need to define a rotation is it should be a linear transformation, it

19 should be homogeneous without loss of generalities we say let us the origin be unchanged. It should leave distances unchanged therefore, it must be orthogonal the transformation must be orthogonal that is the necessary and sufficient condition. It must be specified by an orthogonal matrix that is necessary and sufficient to ensure that distances are unchanged. And it must have determinant plus 1 that is it that is all you need for defining a rotation. Now, suppose I am in four dimension and the coordinates are x 1 x 2 x 3 and x 4 that is, it I rotate in the x 1 x 2 plain. And I leave x 3 and x 4 unchanged is this a rotation or not yes it is, but which axis have I rotated by, about which axis have I rotated, I have left the whole of the x 3 x 4 space unchanged. So, rotation is not defined by saying you rotate about an axis. Rotation is a linear transformation, which is homogeneous, orthogonal and unimodular that is it, that is the general definition of a rotation. It applies to all dimensionality; that is why we define a rotation. (()) That is not part of a rotation that is a separate transformation all together right. So, when 2 translations group get enlarge to what is call the Euclidean group. Right now I am talking only about rotation about homogenous transformation. And I am trying to define a rotation appropriately in a rigorous way. (()) yes yes. (()) No no no no no that is a good question I am not saying that at all I am not saying a trans translation would leave change lengths and so, on. I am not saying that, I am saying what is the set of homogeneous transformations that leaves lengths unchanged you have to have orthogonality. And if I want to live orientations unchanged, you must have handedness unchanged you must also have unimodularity. Every orthogonal matrix has determinant either plus 1 or minus 1, you choose the plus 1 for the proper transformations, if you include reflections then it is minus 1 alright.

20 So, that is my definition of a rotation you cannot always associate an axis with a rotation, in three dimensions, what you can do is associate a plane you can always say every rotation can be regarded as rotation. In some plane does not have to be 1 of the Cartesian planes could be something tilted an angle, but there is always a plane. So, 2 variables are affected at a time; now, therefore, tell me since 2 variables are affected at a time how many rotations are there NC 2. So, it is n times n minus 1 over 2, that is the number of angles you need to specify a rotation in n dimensions and what is it for n equal to 2, 1 and then equal to 3, 3. (Refer Slide Time: 43:40)

21 It turns out that this quantity n times n minus 1 over 2, which is a number of independent planes that you can have orthogonal planes, happens to be equal to n when n is equal to 3. This is a great accident well may be not such a great accident after all, that is the reason you say s rotation about the z axis, the about the x axis about the y axis. Because, you really saying, rotation about the y z in the y z plane z x plane and x y plane, you cannot do this any other dimensionality including two. This has a solution in integers only for n e q implies and equal to 3, so one of the reasons why three dimensions is very special. So, how many generators thus S O n have, in exactly the same way you could ask how many generators does the symplective group have; how many possible parameters do you need to specify or possible canonical transformations. And the way to do this is exactly what you did for rotations. Then we ask if I make an infinite decimal transformation how many parameters are there how many independent ways can I make infinite decimal transformations. So, let us make an infinite decimal transformations of out here. So, let us say M is equal to the identity transformation plus some epsilon some small parameter like a delta alpha times G, G some other 2 n by 2 n matrix called the generator of a transformation.

22 (Refer Slide Time: 45:20) Plug that in here and what you get this implies I plus epsilon G transpose J I plus Epsilon G equal to J. And let us open it out and you have to retain to first order in epsilon and what does it tell you it say s. So, if you multiply this through its J plus epsilon G transpose J time s I plus J epsilon G equal to J; dropping the Epsilon square term and the J cancels out and the epsilon trans. So, symplective transformations are generated by matrices which satisfy this property G transpose is J minus G transpose J is minus J G. And let us take the J to the right hand side, so this becomes J inverse but, J inverse is minus J. So, the generator of a simplected transformation, the infinite decimal generator must satisfy this condition the transpose of this generator must be equal to J G J. And now you can count how many parameters it must have. So, keep this as an exercise to you to show that S p 2 n twice 2 n plus 1 generators it is much larger than the.

23 (Refer Slide Time: 47:39) Well, I have not quite defined what a generator is but, where like to look at it is if I look at what happens here for in infinite decimal rotation. When let us replace this cos alpha by delta alpha sin alpha by a delta alpha delta alpha and what to first order in delta alpha and what is cos delta alpha to first order in delta alpha. 1 minus delta alpha whole square over 2 factorial, but then I am working to first order. So, cos alpha is just to 1 right, and this is equal to 1 delta alpha minus delta alpha 1 to first order in delta alpha, which is equal to the identity matrix plus delta alpha times 0 1 minus 1 0. This is the parameter, you can change it but, that is a given matrix and that matrix is what I call the generator. Question is find out how many such independent matrices are there in general, in three dimension it is not hard see that there be three of them for rotations for the symplectic group, that be twiced 2 n plus 1 this is a large group; so if at all kinds of transformations possible as infinite decimal as canonical transformations. So, fairly large group we will, we will say a little more about it here I will come back and spend some time on rot the rotation group and it is generator and so, on. Because that is not quite the way, I would like to write the generator, I did like write the generator as a Hermitian matrix. And the reason is I would like to exponentiate this generator, when you exponentiate the Hermitian matrix, what do you get a to the power I times the Hermitian matrix is a unitary matrix.

24 And a unitary matrix has interesting properties the analog of canonical transformations in classical physics. So, we will do a little bit of this subsequently but, right now the point I want to make is that the group of canonical transformations can be related to a group of matrices, you can see the generated by this symplective group. But now I pointed out coming back to our original problem, the symmetry group of a dynamical system is that set of transformations, which leaves the set of equations of motions unchanged, therefore, the set of solutions unchanged. Now, in the Hamiltonian framework you first needed to ensure that the structure of Hamilton s equations did not change. So, you needed to ensure that they were canonical transformations but, you also need to ensure that these transformations do not change the Hamiltonian itself, that the k was equal to the h itself. And what would you expect that to be I had expect that this dynamical group the symmetry group, would be a certain sub group of a symplectic group. Some canonical transformations would not change the Hamiltonian and that would be me symmetry group. So, this is the expectation and the best way to do, this is to do this in terms of an example, so let me do that. (Refer Slide Time: 50:54) And let us look at the simple example, example 2 D oscillator, let me choose again two dimension simple harmonic oscillator for which we understood the motion. It is going to be

25 some torus and it could be periodic and it could be cos periodic but, look at what happens let me choose the mash to be equal to 1. And let me choose the frequency of 1 of the oscillator to be 1. So, I write the Hamiltonian q 1, q 2, p 1, p 2 to be equal to 1 half q 1 square plus p 1 square plus omega square q 2 square plus p 2 square. So, I choose the frequency have to be 1 and the other one to be omega and this in general let to immediately let to cos i periodic motion. In fact, it was not periodic even if omega was irrational then omega became rational it is periodic motion but, still fairly complicated is it a central force is it a central force. If I imagine q 1 and q 2 to be the x and y coordinates of a particle and it is attach by springs, two springs here at right angles then when is it a central force. When both are exactly the same spring constant, then the force is always directed towards the origin right and what would that imply here what should. Omega equal to 1 have to put omega equal to 1. So, I do this and this is called the isotropic oscillators, why do we call it isotropic. The same force in all directions independent of the direction it is always got the same spring constant, so same in all directions on the plane. So, this isotropic oscillator has an extra symmetry and what is a symmetry it is obvious here, look at it, it has circular symmetry, rotational symmetry not true for the non isotropic and isotropic oscillator. So, it is got an extra degree of symmetry this problem, what set of transformations leaves the Hamiltonian unchanged. It is obvious from this form q 1, q 2, p 1, q p 2 are four real numbers running from minus infinity to infinity what transformations would leave this unchanged. Rotations not just in physical space, rotations in face space, the four dimensional face space. So, the q s and p s could get mixed up does not matter but, the Hamiltonian would remain unchanged. What group of rotations is that, I mean what what what group is that set of rotations it is S O 4. So, H has is is invariant under S O 4, this is not a group of transformations in physical space. It is a set of rotations in the four-dimensional face space. So, the first lesson that comes out is that you could have dynamical symmetry arising as a consequence of transformations not just of the potential not just in physical coordinates but, it could also involve the momenta. So, that is the real set of transformations.

26 So, how many generator does SO 4 have 6 generators right lot s of transformations; what is the set of canonical transformations of this system it is Sp what? Well n equal to 2 in this problem. So, this is Sp 4 4 by 4 symplectic matrices for the face spaces four dimension how many generators does that have, twice 4 plus 1 equal to 10 generators. So, it is clear these are different groups. So, it is clear immediately from this that all transformations which leave the Hamiltonian unchanged need not be canonical transformations. And similarly all canonical transformations on the system need not leave the Hamiltonian unchanged for a symmetry, you need both of the both these properties. So, you need some sub groups of this symplectic group which contains the other group but, all members of the other group need not to be sitting here at all. (Refer Slide Time: 56:17) So, it is like this symbolically this is Sp 4 and this is SO 4. So, what should I choose to be the symmetry group, the intersection of these groups. Let us guaranty to the sub group of the symplectic group, I am not going to prove this but, it turns out the symmetry group of this system here the dynamical symmetry group, is the intersection of Sp 4 intersection with SO 4.

27 And that is isomorphic to or at least 1 to 1 correspondence with the following set of transformations. This is isomorphic to a group called SU 2 it is a set of 2 by 2 matrices which are unitary and have determinant plus 1. The matrices could have complex entries does not matter but, it is set of unitary matrices, what is the definition of unitary matrix? Implies complex conjugate transports call the hermitian conjugate U on U must be equal to the identity matrix. In other words the inverse is equal to the Hermitian conjugate not the transpose alone, but complex conjugate transposes that is the unitary matrix. Is this also true is this also true for finite dimensional matrices this is true, because the left inverse is equal to the right inverse for finite dimension matrices, but if these are infinite dimension matrices. Then there is no guaranty that U, U dagger equal to I implies you dagger U equal to I does not imply that at all but, we are concerned with finite matrices. So, it does not matter finite dimension matrices incidentally just for information U dagger U equal to I simply say s that you have property call partial isometric. And similarly U U dagger equal I says you have partial isometry one of them says you have a left inverse but, not a right inverse and vice versa on the other case. But, if both are true then you have complete isometry which is also called unitarity, then you have a unitary matrix but, otherwise it is partial in our finite case it does not matter this implies that. Now, we are saying that the dynamical symmetric group of this two dimensional harmonic oscillator is that set of matrices which is is that set of transformations which is an 1 to 1 correspondence with the group of 2 by 2 unitary matrices with unique determinant. And then the next question to ask this how many generator does that have and what are these generators. And next time since I have gone out of time we will write down what the generators are. Mean while I will leave it to as an exercise to verify that the following is true. Verify that in this problem the following are constants of the motions.

28 (Refer Slide Time: 59:49) J 1 is 1 quarter q 1 square plus p 1 square minus q 2 square minus p 2 square, J 2 is 1 half q 1 q 2 plus p 1 p 2 and J 3 equal to 1 half q 1 p 2 minus q 2 p 1. Verify that J i H equal to 0, these are constants of the motion, but they not involution with each other. So, verify that J i J j equal to epsilon i j k J k. So, cos of J 1 with J 2 J 3 and so on; in cyclic presentation, they are going to turn out to be the generators of our dynamical symmetric group. They are not an involution with each other we will see where that fits in here. So, even the humble two dimensional oscillator has a great deal of deep mathematics unit at some level and our aim actually is to use this as an example. And then go over to the Keplar problem the 1 over our potential which has an even better symmetry than this, so that is my next target. But this is a preliminary required for that, so let me stop here.

### Computer Graphics Prof. Sukhendu Das Dept. of Computer Science and Engineering Indian Institute of Technology, Madras Lecture 7 Transformations in 2-D

Computer Graphics Prof. Sukhendu Das Dept. of Computer Science and Engineering Indian Institute of Technology, Madras Lecture 7 Transformations in 2-D Welcome everybody. We continue the discussion on 2D

### Basic Quantum Mechanics Prof. Ajoy Ghatak Department of Physics. Indian Institute of Technology, Delhi

Basic Quantum Mechanics Prof. Ajoy Ghatak Department of Physics. Indian Institute of Technology, Delhi Module No. # 02 Simple Solutions of the 1 Dimensional Schrodinger Equation Lecture No. # 7. The Free

### Lecture L3 - Vectors, Matrices and Coordinate Transformations

S. Widnall 16.07 Dynamics Fall 2009 Lecture notes based on J. Peraire Version 2.0 Lecture L3 - Vectors, Matrices and Coordinate Transformations By using vectors and defining appropriate operations between

### Let s first see how precession works in quantitative detail. The system is illustrated below: ...

lecture 20 Topics: Precession of tops Nutation Vectors in the body frame The free symmetric top in the body frame Euler s equations The free symmetric top ala Euler s The tennis racket theorem As you know,

### Chapter 17. Orthogonal Matrices and Symmetries of Space

Chapter 17. Orthogonal Matrices and Symmetries of Space Take a random matrix, say 1 3 A = 4 5 6, 7 8 9 and compare the lengths of e 1 and Ae 1. The vector e 1 has length 1, while Ae 1 = (1, 4, 7) has length

### Unified Lecture # 4 Vectors

Fall 2005 Unified Lecture # 4 Vectors These notes were written by J. Peraire as a review of vectors for Dynamics 16.07. They have been adapted for Unified Engineering by R. Radovitzky. References [1] Feynmann,

### Theory of Computation Prof. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras

Theory of Computation Prof. Kamala Krithivasan Department of Computer Science and Engineering Indian Institute of Technology, Madras Lecture No. # 31 Recursive Sets, Recursively Innumerable Sets, Encoding

### 1 Scalars, Vectors and Tensors

DEPARTMENT OF PHYSICS INDIAN INSTITUTE OF TECHNOLOGY, MADRAS PH350 Classical Physics Handout 1 8.8.2009 1 Scalars, Vectors and Tensors In physics, we are interested in obtaining laws (in the form of mathematical

### [1] Diagonal factorization

8.03 LA.6: Diagonalization and Orthogonal Matrices [ Diagonal factorization [2 Solving systems of first order differential equations [3 Symmetric and Orthonormal Matrices [ Diagonal factorization Recall:

### Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur

Probability and Statistics Prof. Dr. Somesh Kumar Department of Mathematics Indian Institute of Technology, Kharagpur Module No. #01 Lecture No. #15 Special Distributions-VI Today, I am going to introduce

### Linear Algebra Notes for Marsden and Tromba Vector Calculus

Linear Algebra Notes for Marsden and Tromba Vector Calculus n-dimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of

### Quantum Mechanics and Representation Theory

Quantum Mechanics and Representation Theory Peter Woit Columbia University Texas Tech, November 21 2013 Peter Woit (Columbia University) Quantum Mechanics and Representation Theory November 2013 1 / 30

### 2D Geometric Transformations. COMP 770 Fall 2011

2D Geometric Transformations COMP 770 Fall 2011 1 A little quick math background Notation for sets, functions, mappings Linear transformations Matrices Matrix-vector multiplication Matrix-matrix multiplication

### Particle Physics. Michaelmas Term 2011 Prof Mark Thomson. Handout 7 : Symmetries and the Quark Model. Introduction/Aims

Particle Physics Michaelmas Term 2011 Prof Mark Thomson Handout 7 : Symmetries and the Quark Model Prof. M.A. Thomson Michaelmas 2011 206 Introduction/Aims Symmetries play a central role in particle physics;

### DIFFERENTIABILITY OF COMPLEX FUNCTIONS. Contents

DIFFERENTIABILITY OF COMPLEX FUNCTIONS Contents 1. Limit definition of a derivative 1 2. Holomorphic functions, the Cauchy-Riemann equations 3 3. Differentiability of real functions 5 4. A sufficient condition

### (Most of the material presented in this chapter is taken from Thornton and Marion, Chap. 7)

Chapter 4. Lagrangian Dynamics (Most of the material presented in this chapter is taken from Thornton and Marion, Chap. 7 4.1 Important Notes on Notation In this chapter, unless otherwise stated, the following

### Taylor Polynomials and Taylor Series Math 126

Taylor Polynomials and Taylor Series Math 26 In many problems in science and engineering we have a function f(x) which is too complicated to answer the questions we d like to ask. In this chapter, we will

### We call this set an n-dimensional parallelogram (with one vertex 0). We also refer to the vectors x 1,..., x n as the edges of P.

Volumes of parallelograms 1 Chapter 8 Volumes of parallelograms In the present short chapter we are going to discuss the elementary geometrical objects which we call parallelograms. These are going to

### Vectors, Gradient, Divergence and Curl.

Vectors, Gradient, Divergence and Curl. 1 Introduction A vector is determined by its length and direction. They are usually denoted with letters with arrows on the top a or in bold letter a. We will use

### The Schrödinger Equation

The Schrödinger Equation When we talked about the axioms of quantum mechanics, we gave a reduced list. We did not talk about how to determine the eigenfunctions for a given situation, or the time development

### State of Stress at Point

State of Stress at Point Einstein Notation The basic idea of Einstein notation is that a covector and a vector can form a scalar: This is typically written as an explicit sum: According to this convention,

### Lectures notes on orthogonal matrices (with exercises) 92.222 - Linear Algebra II - Spring 2004 by D. Klain

Lectures notes on orthogonal matrices (with exercises) 92.222 - Linear Algebra II - Spring 2004 by D. Klain 1. Orthogonal matrices and orthonormal sets An n n real-valued matrix A is said to be an orthogonal

### 2 Complex Functions and the Cauchy-Riemann Equations

2 Complex Functions and the Cauchy-Riemann Equations 2.1 Complex functions In one-variable calculus, we study functions f(x) of a real variable x. Likewise, in complex analysis, we study functions f(z)

### Mathematics Course 111: Algebra I Part IV: Vector Spaces

Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 1996-7 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are

### Digital Image Processing. Prof. P.K. Biswas. Department of Electronics & Electrical Communication Engineering

Digital Image Processing Prof. P.K. Biswas Department of Electronics & Electrical Communication Engineering Indian Institute of Technology, Kharagpur Lecture - 27 Colour Image Processing II Hello, welcome

### MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a

### 2 Session Two - Complex Numbers and Vectors

PH2011 Physics 2A Maths Revision - Session 2: Complex Numbers and Vectors 1 2 Session Two - Complex Numbers and Vectors 2.1 What is a Complex Number? The material on complex numbers should be familiar

### Some Notes on Taylor Polynomials and Taylor Series

Some Notes on Taylor Polynomials and Taylor Series Mark MacLean October 3, 27 UBC s courses MATH /8 and MATH introduce students to the ideas of Taylor polynomials and Taylor series in a fairly limited

### MATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.

MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column

### 3.8 Finding Antiderivatives; Divergence and Curl of a Vector Field

3.8 Finding Antiderivatives; Divergence and Curl of a Vector Field 77 3.8 Finding Antiderivatives; Divergence and Curl of a Vector Field Overview: The antiderivative in one variable calculus is an important

### Geometric Transformations

Geometric Transformations Definitions Def: f is a mapping (function) of a set A into a set B if for every element a of A there exists a unique element b of B that is paired with a; this pairing is denoted

### 1 Symmetries of regular polyhedra

1230, notes 5 1 Symmetries of regular polyhedra Symmetry groups Recall: Group axioms: Suppose that (G, ) is a group and a, b, c are elements of G. Then (i) a b G (ii) (a b) c = a (b c) (iii) There is an

### Electronics for Analog Signal Processing - II Prof. K. Radhakrishna Rao Department of Electrical Engineering Indian Institute of Technology Madras

Electronics for Analog Signal Processing - II Prof. K. Radhakrishna Rao Department of Electrical Engineering Indian Institute of Technology Madras Lecture - 18 Wideband (Video) Amplifiers In the last class,

### We now explore a third method of proof: proof by contradiction.

CHAPTER 6 Proof by Contradiction We now explore a third method of proof: proof by contradiction. This method is not limited to proving just conditional statements it can be used to prove any kind of statement

### Physics 235 Chapter 1. Chapter 1 Matrices, Vectors, and Vector Calculus

Chapter 1 Matrices, Vectors, and Vector Calculus In this chapter, we will focus on the mathematical tools required for the course. The main concepts that will be covered are: Coordinate transformations

### Numerically integrating equations of motion

Numerically integrating equations of motion 1 Introduction to numerical ODE integration algorithms Many models of physical processes involve differential equations: the rate at which some thing varies

### 8 Square matrices continued: Determinants

8 Square matrices continued: Determinants 8. Introduction Determinants give us important information about square matrices, and, as we ll soon see, are essential for the computation of eigenvalues. You

### Factoring Patterns in the Gaussian Plane

Factoring Patterns in the Gaussian Plane Steve Phelps Introduction This paper describes discoveries made at the Park City Mathematics Institute, 00, as well as some proofs. Before the summer I understood

### ASEN 3112 - Structures. MDOF Dynamic Systems. ASEN 3112 Lecture 1 Slide 1

19 MDOF Dynamic Systems ASEN 3112 Lecture 1 Slide 1 A Two-DOF Mass-Spring-Dashpot Dynamic System Consider the lumped-parameter, mass-spring-dashpot dynamic system shown in the Figure. It has two point

### Fractions and Decimals

Fractions and Decimals Tom Davis tomrdavis@earthlink.net http://www.geometer.org/mathcircles December 1, 2005 1 Introduction If you divide 1 by 81, you will find that 1/81 =.012345679012345679... The first

### 1 Review of complex numbers

1 Review of complex numbers 1.1 Complex numbers: algebra The set C of complex numbers is formed by adding a square root i of 1 to the set of real numbers: i = 1. Every complex number can be written uniquely

### Math 3000 Section 003 Intro to Abstract Math Homework 2

Math 3000 Section 003 Intro to Abstract Math Homework 2 Department of Mathematical and Statistical Sciences University of Colorado Denver, Spring 2012 Solutions (February 13, 2012) Please note that these

### CHAPTER 3 Numbers and Numeral Systems

CHAPTER 3 Numbers and Numeral Systems Numbers play an important role in almost all areas of mathematics, not least in calculus. Virtually all calculus books contain a thorough description of the natural,

### Notes on Orthogonal and Symmetric Matrices MENU, Winter 2013

Notes on Orthogonal and Symmetric Matrices MENU, Winter 201 These notes summarize the main properties and uses of orthogonal and symmetric matrices. We covered quite a bit of material regarding these topics,

### 3. INNER PRODUCT SPACES

. INNER PRODUCT SPACES.. Definition So far we have studied abstract vector spaces. These are a generalisation of the geometric spaces R and R. But these have more structure than just that of a vector space.

### 1 Lecture 3: Operators in Quantum Mechanics

1 Lecture 3: Operators in Quantum Mechanics 1.1 Basic notions of operator algebra. In the previous lectures we have met operators: ˆx and ˆp = i h they are called fundamental operators. Many operators

### Cryptography and Network Security Department of Computer Science and Engineering Indian Institute of Technology Kharagpur

Cryptography and Network Security Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Module No. # 01 Lecture No. # 05 Classic Cryptosystems (Refer Slide Time: 00:42)

### Linear Algebra: Vectors

A Linear Algebra: Vectors A Appendix A: LINEAR ALGEBRA: VECTORS TABLE OF CONTENTS Page A Motivation A 3 A2 Vectors A 3 A2 Notational Conventions A 4 A22 Visualization A 5 A23 Special Vectors A 5 A3 Vector

### Chapter 9 Unitary Groups and SU(N)

Chapter 9 Unitary Groups and SU(N) The irreducible representations of SO(3) are appropriate for describing the degeneracies of states of quantum mechanical systems which have rotational symmetry in three

### Continued Fractions and the Euclidean Algorithm

Continued Fractions and the Euclidean Algorithm Lecture notes prepared for MATH 326, Spring 997 Department of Mathematics and Statistics University at Albany William F Hammond Table of Contents Introduction

### No Solution Equations Let s look at the following equation: 2 +3=2 +7

5.4 Solving Equations with Infinite or No Solutions So far we have looked at equations where there is exactly one solution. It is possible to have more than solution in other types of equations that are

### a 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + + a 2n x n = b 2.

Chapter 1 LINEAR EQUATIONS 1.1 Introduction to linear equations A linear equation in n unknowns x 1, x,, x n is an equation of the form a 1 x 1 + a x + + a n x n = b, where a 1, a,..., a n, b are given

### TOPIC 3: CONTINUITY OF FUNCTIONS

TOPIC 3: CONTINUITY OF FUNCTIONS. Absolute value We work in the field of real numbers, R. For the study of the properties of functions we need the concept of absolute value of a number. Definition.. Let

### Cryptography and Network Security. Prof. D. Mukhopadhyay. Department of Computer Science and Engineering. Indian Institute of Technology, Kharagpur

Cryptography and Network Security Prof. D. Mukhopadhyay Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Module No. # 01 Lecture No. # 12 Block Cipher Standards

### Math 4310 Handout - Quotient Vector Spaces

Math 4310 Handout - Quotient Vector Spaces Dan Collins The textbook defines a subspace of a vector space in Chapter 4, but it avoids ever discussing the notion of a quotient space. This is understandable

### Pythagorean Triples. Chapter 2. a 2 + b 2 = c 2

Chapter Pythagorean Triples The Pythagorean Theorem, that beloved formula of all high school geometry students, says that the sum of the squares of the sides of a right triangle equals the square of the

### Data Communications Prof. Ajit Pal Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur Lecture # 03 Data and Signal

(Refer Slide Time: 00:01:23) Data Communications Prof. Ajit Pal Department of Computer Science & Engineering Indian Institute of Technology, Kharagpur Lecture # 03 Data and Signal Hello viewers welcome

### Index notation in 3D. 1 Why index notation?

Index notation in 3D 1 Why index notation? Vectors are objects that have properties that are independent of the coordinate system that they are written in. Vector notation is advantageous since it is elegant

### Revised Version of Chapter 23. We learned long ago how to solve linear congruences. ax c (mod m)

Chapter 23 Squares Modulo p Revised Version of Chapter 23 We learned long ago how to solve linear congruences ax c (mod m) (see Chapter 8). It s now time to take the plunge and move on to quadratic equations.

### Algebra 2 Chapter 1 Vocabulary. identity - A statement that equates two equivalent expressions.

Chapter 1 Vocabulary identity - A statement that equates two equivalent expressions. verbal model- A word equation that represents a real-life problem. algebraic expression - An expression with variables.

### Section 1.1. Introduction to R n

The Calculus of Functions of Several Variables Section. Introduction to R n Calculus is the study of functional relationships and how related quantities change with each other. In your first exposure to

### Cryptography and Network Security Prof. D. Mukhopadhyay Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur

Cryptography and Network Security Prof. D. Mukhopadhyay Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur Lecture No. # 11 Block Cipher Standards (DES) (Refer Slide

### Geometry of Vectors. 1 Cartesian Coordinates. Carlo Tomasi

Geometry of Vectors Carlo Tomasi This note explores the geometric meaning of norm, inner product, orthogonality, and projection for vectors. For vectors in three-dimensional space, we also examine the

### MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +

### WHICH LINEAR-FRACTIONAL TRANSFORMATIONS INDUCE ROTATIONS OF THE SPHERE?

WHICH LINEAR-FRACTIONAL TRANSFORMATIONS INDUCE ROTATIONS OF THE SPHERE? JOEL H. SHAPIRO Abstract. These notes supplement the discussion of linear fractional mappings presented in a beginning graduate course

### Introduction to Matrix Algebra I

Appendix A Introduction to Matrix Algebra I Today we will begin the course with a discussion of matrix algebra. Why are we studying this? We will use matrix algebra to derive the linear regression model

### 2.2 Magic with complex exponentials

2.2. MAGIC WITH COMPLEX EXPONENTIALS 97 2.2 Magic with complex exponentials We don t really know what aspects of complex variables you learned about in high school, so the goal here is to start more or

### Simple Harmonic Motion

Simple Harmonic Motion 1 Object To determine the period of motion of objects that are executing simple harmonic motion and to check the theoretical prediction of such periods. 2 Apparatus Assorted weights

### Polynomial Invariants

Polynomial Invariants Dylan Wilson October 9, 2014 (1) Today we will be interested in the following Question 1.1. What are all the possible polynomials in two variables f(x, y) such that f(x, y) = f(y,

### MATH 4330/5330, Fourier Analysis Section 11, The Discrete Fourier Transform

MATH 433/533, Fourier Analysis Section 11, The Discrete Fourier Transform Now, instead of considering functions defined on a continuous domain, like the interval [, 1) or the whole real line R, we wish

### Vieta s Formulas and the Identity Theorem

Vieta s Formulas and the Identity Theorem This worksheet will work through the material from our class on 3/21/2013 with some examples that should help you with the homework The topic of our discussion

### Current Standard: Mathematical Concepts and Applications Shape, Space, and Measurement- Primary

Shape, Space, and Measurement- Primary A student shall apply concepts of shape, space, and measurement to solve problems involving two- and three-dimensional shapes by demonstrating an understanding of:

### Lecture 1: Schur s Unitary Triangularization Theorem

Lecture 1: Schur s Unitary Triangularization Theorem This lecture introduces the notion of unitary equivalence and presents Schur s theorem and some of its consequences It roughly corresponds to Sections

### Chapter 6 Circular Motion

Chapter 6 Circular Motion 6.1 Introduction... 1 6.2 Cylindrical Coordinate System... 2 6.2.1 Unit Vectors... 3 6.2.2 Infinitesimal Line, Area, and Volume Elements in Cylindrical Coordinates... 4 Example

### Introduction to Matrices

Introduction to Matrices Tom Davis tomrdavis@earthlinknet 1 Definitions A matrix (plural: matrices) is simply a rectangular array of things For now, we ll assume the things are numbers, but as you go on

### The Quantum Harmonic Oscillator Stephen Webb

The Quantum Harmonic Oscillator Stephen Webb The Importance of the Harmonic Oscillator The quantum harmonic oscillator holds a unique importance in quantum mechanics, as it is both one of the few problems

### TOPIC 4: DERIVATIVES

TOPIC 4: DERIVATIVES 1. The derivative of a function. Differentiation rules 1.1. The slope of a curve. The slope of a curve at a point P is a measure of the steepness of the curve. If Q is a point on the

### Estimated Pre Calculus Pacing Timeline

Estimated Pre Calculus Pacing Timeline 2010-2011 School Year The timeframes listed on this calendar are estimates based on a fifty-minute class period. You may need to adjust some of them from time to

### 1. LINEAR EQUATIONS. A linear equation in n unknowns x 1, x 2,, x n is an equation of the form

1. LINEAR EQUATIONS A linear equation in n unknowns x 1, x 2,, x n is an equation of the form a 1 x 1 + a 2 x 2 + + a n x n = b, where a 1, a 2,..., a n, b are given real numbers. For example, with x and

### Soil Dynamics Prof. Deepankar Choudhury Department of Civil Engineering Indian Institute of Technology, Bombay

Soil Dynamics Prof. Deepankar Choudhury Department of Civil Engineering Indian Institute of Technology, Bombay Module - 2 Vibration Theory Lecture - 8 Forced Vibrations, Dynamic Magnification Factor Let

### Vocabulary Words and Definitions for Algebra

Name: Period: Vocabulary Words and s for Algebra Absolute Value Additive Inverse Algebraic Expression Ascending Order Associative Property Axis of Symmetry Base Binomial Coefficient Combine Like Terms

### Assessment Plan for Learning Outcomes for BA/BS in Physics

Department of Physics and Astronomy Goals and Learning Outcomes 1. Students know basic physics principles [BS, BA, MS] 1.1 Students can demonstrate an understanding of Newton s laws 1.2 Students can demonstrate

### Lecture 16. Newton s Second Law for Rotation. Moment of Inertia. Angular momentum. Cutnell+Johnson: 9.4, 9.6

Lecture 16 Newton s Second Law for Rotation Moment of Inertia Angular momentum Cutnell+Johnson: 9.4, 9.6 Newton s Second Law for Rotation Newton s second law says how a net force causes an acceleration.

### 1D 3D 1D 3D. is called eigenstate or state function. When an operator act on a state, it can be written as

Chapter 3 (Lecture 4-5) Postulates of Quantum Mechanics Now we turn to an application of the preceding material, and move into the foundations of quantum mechanics. Quantum mechanics is based on a series

### Stanford Math Circle: Sunday, May 9, 2010 Square-Triangular Numbers, Pell s Equation, and Continued Fractions

Stanford Math Circle: Sunday, May 9, 00 Square-Triangular Numbers, Pell s Equation, and Continued Fractions Recall that triangular numbers are numbers of the form T m = numbers that can be arranged in

### Coefficient of Potential and Capacitance

Coefficient of Potential and Capacitance Lecture 12: Electromagnetic Theory Professor D. K. Ghosh, Physics Department, I.I.T., Bombay We know that inside a conductor there is no electric field and that

### = 2 + 1 2 2 = 3 4, Now assume that P (k) is true for some fixed k 2. This means that

Instructions. Answer each of the questions on your own paper, and be sure to show your work so that partial credit can be adequately assessed. Credit will not be given for answers (even correct ones) without

### Supplemental Worksheet Problems To Accompany: The Pre-Algebra Tutor: Volume 1 Section 1 Real Numbers

Supplemental Worksheet Problems To Accompany: The Pre-Algebra Tutor: Volume 1 Please watch Section 1 of this DVD before working these problems. The DVD is located at: http://www.mathtutordvd.com/products/item66.cfm

### Lecture - 4 Diode Rectifier Circuits

Basic Electronics (Module 1 Semiconductor Diodes) Dr. Chitralekha Mahanta Department of Electronics and Communication Engineering Indian Institute of Technology, Guwahati Lecture - 4 Diode Rectifier Circuits

### 521493S Computer Graphics. Exercise 2 & course schedule change

521493S Computer Graphics Exercise 2 & course schedule change Course Schedule Change Lecture from Wednesday 31th of March is moved to Tuesday 30th of March at 16-18 in TS128 Question 2.1 Given two nonparallel,

### Lecture 7. Matthew T. Mason. Mechanics of Manipulation. Lecture 7. Representing Rotation. Kinematic representation: goals, overview

Matthew T. Mason Mechanics of Manipulation Today s outline Readings, etc. We are starting chapter 3 of the text Lots of stuff online on representing rotations Murray, Li, and Sastry for matrix exponential

### 15.062 Data Mining: Algorithms and Applications Matrix Math Review

.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop

### South Carolina College- and Career-Ready (SCCCR) Pre-Calculus

South Carolina College- and Career-Ready (SCCCR) Pre-Calculus Key Concepts Arithmetic with Polynomials and Rational Expressions PC.AAPR.2 PC.AAPR.3 PC.AAPR.4 PC.AAPR.5 PC.AAPR.6 PC.AAPR.7 Standards Know

### 3. Mathematical Induction

3. MATHEMATICAL INDUCTION 83 3. Mathematical Induction 3.1. First Principle of Mathematical Induction. Let P (n) be a predicate with domain of discourse (over) the natural numbers N = {0, 1,,...}. If (1)

### (Refer Slide Time: 02:55)

Maxwell-Boltzmann Distribution vs Fermi-Dirac Distribution Prof. Dr.Prathap Haridoss Department of Metallurgical and Materials Engineering Indian Institute of Technology, Chennai Lecture No. # 20 Hello,

### Linear Algebra Notes

Linear Algebra Notes Chapter 19 KERNEL AND IMAGE OF A MATRIX Take an n m matrix a 11 a 12 a 1m a 21 a 22 a 2m a n1 a n2 a nm and think of it as a function A : R m R n The kernel of A is defined as Note

### Sec 4.1 Vector Spaces and Subspaces

Sec 4. Vector Spaces and Subspaces Motivation Let S be the set of all solutions to the differential equation y + y =. Let T be the set of all 2 3 matrices with real entries. These two sets share many common

### PRINCIPLES OF PROBLEM SOLVING

PRINCIPLES OF PROBLEM SOLVING There are no hard and fast rules that will ensure success in solving problems. However, it is possible to outline some general steps in the problem-solving process and to