Infeasible heuristics are heuristic values that cannot be the optimal solution cost. Detecting infeasibility is a useful technique (Yang et al. 2008) to improve the quality of heuristics because it allows the heuristic value to be increased without risking it becoming inadmissible. However, extra memory is required when applying this technique. Is checking for infeasibility the best way to use ...

All practical MPC implementations should have a means to recover from infeasibility. We present a recently developed infeasibility handler which computes optimal relaxations of the relaxable constraints subject to a user-de ned prioritization, by solving only a single linear program on-line in addition to the standard quadratic programming problem on-line. A stability result for this infeasibil...

Using super-efficiency, with regard to ranking efficient units, is increasing in DEA. However, this model has some problems such as the infeasibility. Thus, this article studies infeasibility of the input-based super-efficiency model (because of the zero inputs and outputs), and presents a solution by adding two virtual DMUs with mean values (one for inputs and one for outputs). Adding virtual ...

We revisit facial reduction from the point of view projective geometry. This leads us to a homogenization strategy in conic programming that eliminates phenomenon weak infeasibility. For semidefinite programs (and others), this yields infeasibility certificates can be checked polynomial time. Furthermore, we propose refined type infeasibility, which call stably infeasible, for rational exist an...

This thesis proposes a new necessary condition for the infeasibility of non-linear optimization problems (that becomes necessary under convexity assumption) which is stated as a Pareto-criticality condition of an auxiliary multiobjective optimization problem. This condition can be evaluated, in a given problem, using multiobjective optimization algorithms, in a search that either leads to a fea...