This work regards the development of a distributed model predictive control strategy for large-scale systems, as centralized implementations often suffer from non-scalability. The decomposition overall system into minimally coupled subsystems well their coordination are based on optimality condition (OCD) and community detection. OCD approach allows to solve associated subproblems in parallel a...

Approximate optimality conditions for a class of nonconvex semi-infinite programs involving support functions are given. The objective function and the constraint functions are locally Lipschitz functions on n . By using a Karush-Kuhn-Tucker KKT condition, we deduce a necessary optimality condition for local approximate solutions. Then, generalized KKT conditions for the problems are proposed. ...

This paper studies the Generalized Neyman–Pearson (GNP) optimality of empirical likelihood-based tests for parameter hypotheses. The GNP optimality focuses on the large deviation errors of tests, i.e., the convergence rates of the type I and II error probabilities under fixed alternatives. We derive (i) the GNP optimality of the empirical likelihood criterion (ELC) test against all alternatives...

An optimal control problem is considered for the variational inequality representing the stress-based (dual) formulation of static elastoplasticity. The linear kinematic hardening model and the von Mises yield condition are used. Existence and uniqueness of the plastic multiplier is rigorously proved, which allows for the re-formulation of the forward system using a complementarity condition. I...

We study a multiobjective variational problem on time scales. For this problem, necessary and sufficient conditions for weak local Pareto optimality are given. We also prove a necessary optimality condition for the isoperimetric problem with multiple constraints on time scales.

Using ε-subdifferential calculus for difference-of-convex (d.c.) programming, Dür proposed a condition sufficient for local optimality, and showed that this condition is not necessary in general. Here it is proved that whenever the convex part is strongly convex, this condition is also necessary. Strong convexity can always be ensured by changing the given d.c. decomposition slightly. This appr...

Primal-dualinterior-point methods have proven to be very successful for both linear programming (LP) and, more recently, for semideenite programming (SDP) problems. Many of the techniques that have been so successful for LP have been extended to SDP. In fact, interior point methods are currently the only successful techniques for SDP. We present a new paradigm for deriving these methods: 1) usi...

This paper presents a new approach to the suucient conditions of nonlinear programming. Main result is a suucient condition for the global optimality of a Kuhn-Tucker point. This condition can be veriied constructively, using a novel convexity test based on interval analysis, and is guaranteed to prove global optimality of strong local minimizers for suuciently narrow bounds. Hence it is expect...