13:12:15	 From:What's the point of problem 4a on the mock exam? Are there not, in this case, multiple different possible permutation matrices P? Do we just pick one out of the many and use it to compute PA=LU?
13:12:16	 From:Could we do a quick review on orthogonality please? 
13:12:30	 From:Could you explain the mock exam 4b?
13:12:43	 From:Can we use a calculator for the exam?
13:12:57	 From:Could you please explain Problem 2?
13:13:00	 From:when will the solutions for the mock exam be posted?
13:13:02	 From:how do we calculate the orthnormal eigenvectors in pb 5?
13:13:48	 From:What does it mean for a matrix to change row geometrically? Why would the determinant change its sign?
13:14:04	 From:What does N(A) in the stack question 4 mean
13:14:29	 From:Nullspace
13:15:35	 From:how do we do the limit in pb 6?? can you give us some hints
13:15:59	 From:what are the standard matrix notations that we use in the test?
13:16:03	 From:Do we need to memorize the SVD equation by heart?
13:16:14	 From:how do we calculate the determinant of 4x4 or greater matrices?
13:17:04	 From:can you explain mock exam pb2?
13:17:07	 From:what exactly does “unique” mean in this context?
13:17:41	 From:what is the difference between A = Q^QT  and A = S^S-1?
13:21:29	 From:Unique means that there are many different LU decompositions depending on which permutation matrix P you decide to use. In the case of 4a, for example, you have 3!=6 different permutation matrices P.
13:24:30	 From:what do yo mean by normalizing the vectors?
13:25:08	 From:You divide the vectors by their magnitude
13:25:22	 From:thanks
13:25:35	 From:what does "higher algebraic multiplicity" mean
13:28:07	 From:Could you please tell the meaning of P in the problem 4? Is it a permutation matrix? Do we really need it here considering there's 1 in the left upper corner? As far as I understand, the permutation is used to get 1 in the left upper corner, right?
13:28:52	 From:Would repeated eigenvalues and eigenvectors appear in the test?
13:28:59	 From:and after we get the matrix A^k, how do we get the limit of that?
13:29:19	 From:how do we know that a vector is not in normalized form?
13:32:04	 From:For problem 4, if permutation is not needed then P would be identity matrix right?
13:32:22	 From:do we just email you about having finished the revision stack, or will that be taken into account automatically?
13:32:35	 From:in the previous year's exam their was a prove question, will we have one too?
13:32:53	 From:To find orthonormal eigenvectors, does it mean that we dont need to multiply the orthonormal  vectors with any unknown? just the vector itself?
13:33:06	 From:Do we have to worry about the early lectures concerning complex values on the exam?
13:34:39	 From:Would the exam cover Euclidean transformations and how to identify them?
13:35:09	 From:on pb 3, the linear combination part, we get infinite number of solutions for the system. is it still a linear  combination?
13:36:38	 From:Would you suggest reducing to row echelon form when you have non-square matrices and you want to determine linear independence?
13:37:12	 From:what is the best thought process when determining the eigenvectors once you have solved for the values?
13:39:00	 From:When do we know whether to use A=Q^QT vs A=S^S-1? Should we always test if A is symmetric first, and if so use the Q, and if not use the S?
13:39:18	 From:What is the Vandermonde matrix
13:41:12	 From:in what case would we use cholesky decomposition?
13:43:05	 From:Do you have any suggestions for exam preparation?:)
13:43:08	 From:Do we have prove that finding Q in P6 is based on the eigenvectors?
13:43:11	 From:What exactly is a pivot because I seemingly find different requirements for pivots in different contexts? Do they have to be 1 and should they be immediately on the right of the pivot of the row above it?
13:43:18	 From:when we need to use A = LDU instead of A = LU?
13:43:28	 From:will all the homework solutions be uploaded to mycourses
13:43:33	 From:Will Cramer's rule be in the exam?
13:43:52	 From:Isn't it so that LDL^T is for symmetric matrices and you can additionally reduce it to LL^T (Cholesky) if it is positive definite?
13:43:59	 From:IS A=LDLT technically the same as A=Q^QT?
13:44:11	 From:can you explain what spectral theorem is?
13:47:21	 From:will the answer to the mock exam be published to MyCourses?
13:49:07	 From:What should we know about quadratic forms and must the matrix A in them always be symmetric?
13:50:00	 From:May you please further explain when to use the Gauss equation and the diag. equation?
13:50:57	 From:when finding Q in the A = QΛQT, why do you have to normalise the matrix formed by the eigenvectors of A to get Q?
13:52:10	 From:I sent an e-mail a few days ago, can you please have a look at it later today?
13:55:19	 From:Can you please upload these notes on MyCo?
13:56:08	 From:why do we use LDL? HDL is better for you
13:56:38	 From:cholesterol
13:56:52	 From:will the geometric meanings (reflection, rotation, scaling) be in the exam somehow
13:57:46	 From:4x4 determinant relevant?
13:58:05	 From:for the exam?
13:58:35	 From:But not the calculation of it?
13:59:29	 From:Do you like maksalaatikkoa
14:00:43	 From:will we have to form matrices from the quadratic forms?
14:01:13	 From:best way for solving the simple cubics?
14:02:12	 From:pleaseee dont make the exam difficult. thank you!!!
14:02:29	 From:Thank you very much!
14:02:58	 From:this exam isn't difficult, I solved the problems myself!
14:03:02	 From:Thank you have a nice day!
14:03:05	 From:Thank you!
14:03:10	 From:Thanks