I have encountered this optimization problem

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while trying to implement the method proposed in this very interesting paper:


the thing is that the optimization is a constrained nonlineal optimization. So far my colleagues and I have found a great solution using fmincon, a Matlab function which is part of the optimization toolbox. However we need to implement our solution in c++. While working with Matlab we got a warning saying that the software was going to use an active-set method, and here


it says that the way it handles the optimization is by using SQP (sequential quadratic programming), but for that it solves a quadratic subproblem , and I quote : "Here you simplify Equation 2-1 by assuming that bound constraints have been expressed as inequality constraints. You obtain the QP subproblem by linearizing the nonlinear constraints."

My question to this community is: how can I linearize the constraints, knowing that they are expressed in the form of a distance between two points that should be equal to a number that is well known ?(basically the constraint says that the lengths of the bones of a person must not change between different frames while being acquired with the kinect).

Any help in this matter will be greatly appreciated, I just need you to point me to the correct literature on how to solve this kind of optimization problems, since fmincon source code is not very clear.

Happy Holidays


1 Answer 1


First, regarding the active-set warning, I am pretty sure the solver (fmincon) has told you that it switches from the default sqp approach, to an active set method. It automatically does this if it deems it suitable (large sparse problems etc). As you can see on the page you linked to, fmincon has 4 different methods implemented, sqp being one of them.

Regarding the linearization done inside fmincon. It depends on how you express $||x-y||-l=0$. This form is probably bad as it is non-smooth. I would write it as

$$(x-y)^T(x-y)-l^2=0$$ The gradient is given by $2(x-y)$. Hence, the linearization at an iterate $(x_k,y_k)$ would be $$(x_k-y_k)^T(x_k-y_k) + 2(x_k-y_k)^T((x-x_k) - (y-y_k)) - l^2 = 0$$

If you still want to work with the norm, you will have to work with sub-gradients etc.


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