In this article, we present an efficient algorithm for solving a class of chance-constrained optimization under nonparametric uncertainty. Our is built on the possibility representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use foundation to formulate one minimizing distance between desired distribution and constraint RKHS. provide systematic way cons...

Schroedinger equation on a Hilbert space H, represents a linear Hamiltonian dynamical system on the space of quantum pure states, the projective Hilbert space PH. Separable states of a bipartite quantum system form a special submanifold of PH. We analyze the Hamiltonian dynamics that corresponds to the quantum system constrained on the manifold of separable states, using as an important example...

It is well known that the normal mixture with unequal variance has unbounded likelihood and thus the corresponding global maximum likelihood estimator (MLE) is undefined. One of the commonly used solutions is to put a constraint on the parameter space so that the likelihood is bounded and then one can run the EM algorithm on this constrained parameter space to find the constrained global MLE. H...

Despite the increasing interest in constrained multiobjective optimization recent years, problems (CMOPs) are still insufficiently understood and characterized. For this reason, selection of appropriate CMOPs for benchmarking is difficult lacks a formal background. We address issue by extending landscape analysis to optimization. By employing four exploratory techniques, we propose 29 features ...