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A question regarding convex optimization and (maybe) duality.

I have a problem in the form \begin{align} x^* = \mathrm{arg} \min_x f(x) \quad \text{s.t.} \quad A x \leq b, C x = d, \end{align} where $f$ is either quadratic or, if it makes things easier, linear. Let's also assume that $P = \{ x \mid A x \leq b \}$ is bounded and not empty.

I would like to design an auxiliary optimization problem that:

  • if the original problem is feasible returns the solution $x^*$ of the original problem,
  • if the original problem is infeasible returns the minimum perturbation (according to some norm) $e^*$ that I should add to $d$ to make the problem feasible (remember $P \neq \emptyset$).

A naive approach could be, for example, something like \begin{align} \min_{x,e} f(x) + M \| e \| \quad \text{s.t.} \quad A x \leq b, C x = d + e, \end{align} where $M$ is "big enough". This however is very unpractical since the high value of $M$ would lead to numeric problems.

Do you have any better idea?

Do you think duality can help in some way here? If I solve the dual of the original problem, and I detect unboundedness (we know that, under these assumptions, the primal infeasible implies the dual unbounded) can I elaborate the result to get something related to $e^*$? For example, could an extreme ray of the dual feasible set help?

Thank you very much!

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  • $\begingroup$ If the primal is infeasible, the dual may also be infeasible. Why not solve 2 problems? $\endgroup$
    – LinAlg
    Nov 28 '18 at 19:03
  • $\begingroup$ You could add a binary variable to deal with infeasibility, but I doubt it is faster. $\endgroup$
    – LinAlg
    Nov 28 '18 at 19:17
  • $\begingroup$ In the quadratic case, the dual is always feasible, but in general you are right: the dual can also be infeasible. I guess solving two problems is always an option. I wanted to understand if I'm trowing away some useful information from the solution of the dual of the original problem. $\endgroup$ Nov 28 '18 at 19:20
  • $\begingroup$ This would be actually a lower level QP/LP solver of a branch and bound code for mixed integer programming (reason why I have the solution of the dual almost for free). Yes, the binary would work, but in practice it's equivalent to solve two problems... $\endgroup$ Nov 28 '18 at 19:23
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Well, if the original problem is infeasible, you can just solve for \begin{align} \min_{x,e} \|e\|: & \\ Ax&\le b, \\ e &= d-Cx. \end{align}

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  • $\begingroup$ Unfortunately I do not know a priori if the problem is infeasible. Your approach would then boil down to just solve two problems, right? $\endgroup$ Nov 28 '18 at 19:25
  • $\begingroup$ You will have to first check the feasibility of the original problem anyway. Besides, this new problem is always feasible. $\endgroup$
    – Hans
    Nov 28 '18 at 19:36
  • $\begingroup$ I do not agree that I have to first check the feasibility of the original problem anyway: the example I described with the big M in the objective function is a single problem, where I do not need that check first. No? $\endgroup$ Nov 28 '18 at 19:44
  • $\begingroup$ Your requirement is to solve for the original problem if the domain is feasible. Your objective function with the big M violates that requirement. The bigger the M is, the further away your solution deviates from the true solution, when the original problem is feasible. My formulation, on the other hand, fulfills exactly your two requirements. $\endgroup$
    – Hans
    Nov 28 '18 at 19:56
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    $\begingroup$ It's clear that solving two problems as you are suggesting would work: my question is in fact how to avoid that... $\endgroup$ Nov 28 '18 at 20:33

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