Powers of Matrices and Markov Matrices

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  • เผยแพร่เมื่อ 28 พ.ย. 2024

ความคิดเห็น • 28

  • @georgesadler7830
    @georgesadler7830 3 ปีที่แล้ว +3

    Dr. Strang really wants each student to understand linear algebra in all phases from top to bottom and from left to right. After the lectures are over he wants you to retain this information forever. These lectures really tell you the student the passion DR. Strang has for teaching.

  • @AmandaSMaria
    @AmandaSMaria 7 ปีที่แล้ว +27

    the best teacher in the world!! Thank you professor Strang!!

  • @yujing7544
    @yujing7544 7 ปีที่แล้ว +8

    “the best teacher in the world!! Thank you professor Strang!! ”, the same words from my heart.

  • @ffvgdsg5584
    @ffvgdsg5584 5 ปีที่แล้ว +4

    as a student of international university, which is not in US i've witnessed that most professors are using prof Strang's lectures as a pattern and the book particularly we use is intro to linear algebra G.Strang.
    That is another evidence of that he is currently the best professor in the world

  • @Jeshtroy
    @Jeshtroy 7 ปีที่แล้ว +22

    He teaches in a way that a 3rd grader could understand!

  • @paulchan6818
    @paulchan6818 ปีที่แล้ว

    A good lecturer that connects to his students.

  • @dengdengkenya
    @dengdengkenya 5 ปีที่แล้ว +1

    Fantastic! I'm really enlightened by this lecture, though I can't say Professor Strang always gave such a clear explanation on each linear algebra subject.

  • @arvindvishwakarma4257
    @arvindvishwakarma4257 6 ปีที่แล้ว +1

    Best teacher in the world

  • @jimnewton4534
    @jimnewton4534 3 ปีที่แล้ว +2

    To compute the nth power of a square matrix A requires at most log(n) matrix multiplications, i.e. n^3 log n. (assuming cubic time matrix multiplication) Why? because if n is even I can simply square A^{n/2}, and if n is odd I can multiply A by A^{n-1}. If I have the Eigen decomposition then raising to the nth power is n^3 (just compute the powers along the diagonal of the diagonal matrix) and multiple on left and right by V and V^{-1}.

  • @edwardhartz1029
    @edwardhartz1029 4 ปีที่แล้ว

    That was superb, I love how thorough you are. Now I understand why pii^(n)=a*(lambda1)^n+b*(lambda2)^n+...
    (For some a,b,c.. which can be determined using simultaneous equations.)

  • @jonahansen
    @jonahansen 6 ปีที่แล้ว +2

    What a great teacher!

  • @jesusglez09
    @jesusglez09 3 ปีที่แล้ว

    i love this Thank you Prof. Thank you MIT

  • @saeida.alghamdi1671
    @saeida.alghamdi1671 4 ปีที่แล้ว

    Quite Interesting implication of the presentation!

  • @devrimturker
    @devrimturker 4 ปีที่แล้ว

    Excellent explanations

  • @tgx3529
    @tgx3529 3 ปีที่แล้ว

    Time 12:24, is it realy Markov matrix from Markov proces?? 0,8+0,31 and 0,2+0,71

  • @dengdengkenya
    @dengdengkenya 5 ปีที่แล้ว +1

    What if all eigenvalues were less than one in the case of Markov Matrix example? Or is there any theorem that proves at least one lambda is greater than one here?

    • @jordanhansen6649
      @jordanhansen6649 4 ปีที่แล้ว

      There is always an Eigen value that equals 1 in the case of a Markov Matrix

  • @sauravnagar1745
    @sauravnagar1745 6 ปีที่แล้ว +1

    How does he write the values of eigenvectors so easily? I mean he doesn't even perform any mental calculations. Does anyone has any clue?

  • @checkout8352
    @checkout8352 5 ปีที่แล้ว

    Thank you vey much

  • @yohanshailu5620
    @yohanshailu5620 3 ปีที่แล้ว

    make it 1.75x

  • @wuoshiwzm001
    @wuoshiwzm001 7 ปีที่แล้ว +3

    professor Strang is getting old.... but always giant to me..

    • @videofountain
      @videofountain 7 ปีที่แล้ว

      Everyone is getting older or the other alternative. Including you.

  • @engineershmily
    @engineershmily 6 ปีที่แล้ว

    please anyone can guide me how if U(k+1)=AU(K) then U(k)=A^k U(0) at time 3:25

    • @jancirani2748
      @jancirani2748 6 ปีที่แล้ว +1

      Start with u(1).
      1. U(1)= A.U(0). --> eq.1
      2. U(2)= A.U(1) = A.A.U(0) (from eq.1)
      U(2)= A^2.U(0)
      Proceeding like this,
      It gives
      U(k) = A^k.U(0).
      Hope, it's clear

    • @shrinivasiyengar5799
      @shrinivasiyengar5799 6 ปีที่แล้ว

      @@jancirani2748 can this be said to be similar to how in the continuous time system X' = AX gives a solution X(t) = (e^At)X(0)

    • @WolfixDwell
      @WolfixDwell 4 ปีที่แล้ว

      @@shrinivasiyengar5799 Hi, prolly little bit late but: Solution to differential equation as your system dynamic is equal to x(t)= (e^At)x(0), so this solution coresponds to differential equation. And solving the e^At corresponds to e^\lambda_1 t + e^\lambda_2 t ... bcs of equality of characteristic polynomial equation

  • @justinkim7743
    @justinkim7743 4 ปีที่แล้ว

    Thank you so much