Specifications

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e2 in the criterion. This has two potentials:
1. Small values of e2 may regularize complex problems numerically much like e1 can do.
2. Large values of e2 can change the formulation of the problem into something different just like e1
can do, but unlike e1, it makes physiological sense to use large values of e2.
Initially, try setting up the study like this:
// The study: Operations to be performed on the model
AnyBodyStudy ArmModelStudy = {
AnyFolder &Model = .ArmModel;
RecruitmentSolver = MinMaxOOSolQP;
RecruitmentLpPenalty = 0.0;
RecruitmentQpPenalty = 0.0;
Gravity = {0.0, -9.81, 0.0};
};
As you have probably guessed, the AnyScript name of e2 is RecruitmentQpPenalty. Load and rerun the
model again and have a look at the activities. They should be just like before. You have changed the
solution algorithm from linear to quadratic but not the formulation of the problem. Since both algorithms
solve the same problem, the results are identical.
However, try changing the setting of the variable like this:
RecruitmentQpPenalty = 1000.0;
This rather high value of e2 will dominate the criterion and leave you with a quadratic solution to the muscle
recruitment problem. Some scientists prefer this solution to the min/max formulation, and AnyBody gives
you the opportunity to choose min/max, quadratic, or any combination of the two by variation of
RecruitmentQpPenalty. The quadratic solution to the problem is the following: