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For anyone who is already familiar with the Prof. Strang's lectures from previous years, the main new thing in this five-lecture mini-series is he tries to cond
by ivan_ah 6y ago
For anyone who is already familiar with the Prof. Strang's lectures from previous years, the main new thing in this five-lecture mini-series is he tries to condense the material even further—maximum intuition and power-ideas, instead of the full-length in-class lecture format with derivations. This makes the material difficult to understand for beginners, but makes a great second source in addition to or after a regular LA class.
One of the interesting new ways of thinking in these lectures is the A = CR decomposition for any matrix A, where C is a matrix that contains a basis for the column space of A, while R contains the non-zero rows in RREF(A) — in other words a basis for the row space, see https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-linear-algebra-spring-2020/videos/MITRES_18_010S20_LA_Slides.pdf#page=7 https://ocw.mit.edu/resources/res-18-010-a-2020-vision-of-li...
Example you can play with: https://live.sympy.org/?evaluate=C%20%3D%20Matrix(%5B%5B1%2C4%5D%2C%20%5B3%2C2%5D%2C%20%5B2%2C1%5D%5D)%0A%23--%0AC%0A%23--%0AR%20%3D%20Matrix(%5B%5B1%2C0%2C1%5D%2C%20%5B0%2C1%2C1%5D%5D)%0A%23--%0AR%0A%23--%0AC*R%0A%23--%0A(C*R).rref()%5B0%5D%0A%23--%0A https://live.sympy.org/?evaluate=C%20%3D%20Matrix(%5B%5B1%2C...
Thinking of A as CR might be a little intense as first-contact with linear algebra, but I think it contains the "essence" of what is going on, and could potentially set the stage for when these concepts are explained (normally much later in a linear algebra course). Also, I think the "A=CR picture" is a nice justification for where RREF(A) comes about... otherwise students always complain that the first few chapters on Gauss-Jordan elimination is "mind-numbing arithmetic" (which is kind of true...) but maybe if we present the algorithm as "finding the CR-decomposition which will help you understand dozens of other concepts in the remainder of the course" it would motivate more people to learn about RREFs and the G-J algo.
- ivan_ah 6y agoSince y'all are code-literate, here the SymPy function for finding the CR-decomposition of any matrix A: def crd(A): """ Computes the CR decomposition of the matrix A. """ rrefA, licols = A.rref() # compute RREF(A) C = A[:, licols] # linearly indep. cols of A r = len(licols) # = rank(A) R = rrefA[0:r, :] # non-zero rows in RREF(A) return C, R Test to check it works: https://live.sympy.org/?evaluate=A%20%3D%20Matrix(%5B%0A%20%20%5B1%2C%204%2C%205%5D%2C%0A%20%20%5B3%2C%202%2C%205%5D%2C%0A%20%20%5B2%2C%201%2C%203%5D%5D)%0A%23--%0AA%0A%23--%0Adef%20crd(A)%3A%0A%20%20%20%20%22%22%22%0A%20%20%20%20Computes%20the%20CR%20decomposition%20of%20the%20matrix%20A.%0A%20%20%20%20%22%22%22%0A%20%20%20%20rrefA%2C%20licols%20%3D%20A.rref()%20%20%23%20compute%20RREF(A)%0A%20%20%20%20C%20%3D%20A%5B%3A%2C%20licols%5D%20%20%20%20%20%20%20%20%20%20%23%20linearly%20indep.%20cols%20of%20A%0A%20%20%20%20r%20%3D%20len(licols)%20%20%20%20%20%20%20%20%20%20%20%23%20%3D%20rank(A)%0A%20%20%20%20R%20%3D%20rrefA%5B0%3Ar%2C%20%3A%5D%20%20%20%20%20%20%20%20%20%23%20non-zero%20rows%20in%20RREF(A)%0A%20%20%20%20return%20C%2C%20R%0A%23--%0AC%2C%20R%20%3D%20crd(A)%0A%23--%0AC%0A%23--%0AR%0A%23--%0AC*R%0A%23--%0A https://live.sympy.org/?evaluate=A%20%3D%20Matrix(%5B%0A%20%...