We demonstrate that some recently proposed iterative identiication and control design schemes do not necessarily converge to a local minimum of the design objective in the case of a restricted complexity model. There is, however, a link between these approaches and a recently proposed iterative optimization based control design procedure based on experimental data. We show that if the achieved ...

In this paper we study m-discount optimality (m ≥ −1) and Blackwell optimality for a general class of controlled (Markov) diffusion processes. To this end, a key step is to express the expected discounted reward function as a Laurent series, and then search certain control policies that lexicographically maximize themth coefficient of this series form = −1, 0, 1, . . . .This approach naturally ...

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...

When dealing with many-criteria decision making and many-objectives optimization problems the concepts of Pareto optimality and Pareto-dominance are inefficient in modelling and simulating human decision making. Different fuzzy-based definitions of optimality and dominated solution are introduced and tested on analytical test cases in order to show their validity and closeness to human decision...

Sequential optimality conditions for constrained optimization are necessarily satisfied by local minimizers, independently of the fulfillment of constraint qualifications. These conditions support the employment of different stopping criteria for practical optimization algorithms. On the other hand, when an appropriate strict constraint qualification associated with some sequential optimality c...

Abstract An incomplete-block design defines both a concurrence graph and Levi graph. Properties of either can be used to compare designs with respect D-optimality A-optimality. In this paper, we show that optimality the implies strong conditions on connectivity properties graph, use classify optimal when number observational units is close minimal.

Adaptive data structures form a central topic of on-line algorithms research. The area of Competitive Analysis began with the results of Sleator and Tarjan showing that splay trees achieve static optimality for search trees, and that Move-to-Front is constant competitive for the list update problem [ST1], [ST2]. In a parallel development, powerful algorithms have been developed in Machine Learn...

Distributed constraint optimization (DCOP) is a popular formalism for modeling cooperative multi-agent systems. In large-scale networks, finding a global optimum using complete algorithms is often impractical, which leads to the study on incomplete algorithms. Traditionally incomplete algorithms can only find locally optimal solution with no quality guarantees. Recent work on ksize-optimality h...

In this paper, a new two-sampling scheme is proposed to construct appropriate confidence intervals for the lower population quantiles. The confidence intervals are determined in the parametric and nonparametric set up and the optimality problem is discussed in each case. Finally, the proposed procedure is illustrated via a real data set.