The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. PROFESSOR: Ladies and gentlemen, welcome to lecture number 11. In this lecture, I would like to continue discussing with you the solution of the dynamic equilibrium equations. We discussed the solution of dynamic equilibrium equations already in lecture number 10. In that lecture, we consider direct integration methods. In this lecture, I would like to consider with you, discuss with you the mode superposition analysis. The basic idea in the mode superposition analysis is the transformation from the U displacements into a set of new displacements, X. P is an n by n matrix, which is nonsingular. If P is nonsingular, we should recognize that the vector U of lengths n is uniquely given wh...
[SQUEAKING] [RUSTLING] [CLICKING] JUSTIN SOLOMON: So today, we're going to continue in our discussion of dynamic programming. I actually found this set of problem session problems to be easier than the previous one. There's a funny thing, which is we learned in class about pseudo polynomial time style dynamic programs. Somehow, that language is a little bit liberating, in the sense that you're using parameters that you really shouldn't, when it comes to the runtime of your algorithm. Well, I suppose we should, in the sense that it's allowed, if you call your algorithm pseudo polynomial time. But it somehow makes it a little easier to formulate your dynamic programming algorithm, because all the numbers are staring you right in the face. You don't have to be so careful about what's fair game and what's not, when you post your algorithm so long as it's efficient in the values that you care about. And so today's problem session has fiv...
Comments
Post a Comment