Specifications

This result is much different from the one we had before. The value of 1.0e+3 completely dominates the

criterion, so here we are actually minimizing the sum of muscle activities. This has the effect of recruiting a

minimum set of muscles and only those that are best suited at each position. This is known not to be correct

because the muscles are not collaborating. So what is RecruitmentLpPenalty good for anyway? Well, the

clean min/max criterion with e1 = 0 sometimes can cause unrealistic fluctuations of submaximal muscles

between time steps. In those cases, small values of RecruitmentLpPenalty, say 1.0e-6 or 1.0e-5 can

regularize the problem numerically. In this simple case, it makes no difference. If you keep increasing

RecruitmentLpPenalty to, say, 1.0e-3, it will begin to make a difference on the submaximal muscles, and

this is a sign that you may have increased it too much.

Quadratic muscle recruitment

As long as the solution algorithm is linear, the value of e2 will make no difference. The algorithm is specified

by the variable RecruitmentSolver, and the linear options are

• RecruitmentSolver = MinMaxSimplex;

• RecruitmentSolver = MinMaxOOSolSimplex;

These algorithms are very similar in nature and are really just different implementations of the same

technology. For very large and complex problems one algorithm may outperform the other, and this is why

both are available in the system.

However, there is also a third algorithm available, and it differs radically from the two former:

• RecruitmentSolver = MinMaxOOSolQP;

As the name indicates, this algorithm is quadratic, and it permits inclusion of quadratic terms and thereby