Further tags : implicit form, Matlab codegen for GPOPS purposes

Hi there,

I would like to introduce my self and ask you for an advice.

I have a four linked robot that i would really like to control by the meaning of the optimal control theory. The arm has 4 actuators (giving torques U) in corrispondence of 4 rotational joints. P parameters describe the system.

I got ht edynamic system with the Lagrangian method and now I have 4 2nd order...

I have a piece of code that uses lists instead of vectors and recently, it crashed, it ran out of memory. Long story short, if I use vectors instead of lists, the performance is much better and optimal (this I knew but I was lazy to translate all my code). However, there's one single line that I cannot efficiently translate and it's the one that uses the function member(). Before, I had the following (it's an oversimplification of my code in order to easily discuss it in here):

Hi everyone.

I am currently trying to write the gradient projection algorythm for an optimal control problem with Maple.

This involves calculation of the gradient of the objective function at every new iteration, which would be not a problem per se. However, in optimal control problem this last depends on state and costate variables which, in turn, are given by the system of ODEs. This system is solved numerically only. As far as I understood Maple is recalculating...

I've found no questions about these types of functions. When I try to solve this equation by using the FOC function and then try to isolate one of the variables 'effort' (or optimal effort) the outcome is about 6 lines of algebra. When I use simplify it expands even further. What other options are there to get a 'nice' expression?

This is in a similar vein to the Diet problem (integer programming), but harder.

http://en.wikipedia.org/wiki/Cutting_stock_problem

This guy has solved this problem using 3 programs, Maxima and LPsolveIDE and Excel:

https://docs.google.com/View?docid=dfkkh8qj_64rrpz86db

A text file generated by maxima is fed into LPSolveIDE ...

I have a problem understanding how the below reasoning worksfor Bruss article “Sum the Odds to One and Stop,”

i) I would like to understand the die problem and more specificalywhy the probability is k(1/6)^1*(5/6)^(k-1). I have a hard time understanding thisii) How can I apply this reasonong to a coin toss example?I have managed...

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