Dear support team

As I?fd like to compare the different opimization criteria for the

muscle force distribution I?fm trying to understand the different

recruitment solver within AnyBody, most of all the two

variables ?elinear penalty?f and ?equadratic penalty?f. If I understood

it correctly the min/max solver minimizes the objective function

f(zi)= b + Qp * â€¡"zi,

where zi is the activity of muscle I, b is the maximum activity (with

zi<=b), and Lp is the linear penalty. In this case, when Lp = 0 the

maximum activity is minimized. When Lp is high, the sum of the muscle

activity will be minimized.

My question concerns the quadratic programming algorithm: what is

here the objective function to be minimized? Is it accordingly:

f(zi)= b + Qp * â€¡"zi^2?

If so, how high must Qp be that the minimization problem corresponds

to a minimization of the square sum of the muscle activity? I?fm

interested in the latter minimization. As the model consists of a lot

of muscle, at one point the quadratic programming solver doesn?ft find

a solution anymore (?eproblem is unbounded?f). When I decrease Qp (<35)

a solution is found. Now, I?fd like to know what this variable Qp

means. In a simple model (consisting of two segments connected by a

hinge joint, 2 agonistic and 1 antagonistic muscle, 1 external force)

it is easy to show that as Qp increases the solution converge to the

force distribution where the square sum of muscle activity is minimal

(as this simple problem is analytically solvable). But is it possible

to say how close to the latter solution I am with a smaller Qp?

By the way, have you changed the quadratic programming solver in the

beta version 2.0? Strangely, in the new version the problem is always

unbounded using the OOSOLQP recruitment solver, although it is

exactly the same model, which worked quite well in the old version.

Thanks a lot for your help

Christine