Optimality Conditions and Geometric Properties of a Linear Multilevel Programming Problem with Dominated Objective Functions

In this paper, a model of a linear multilevel programming problem with dominated objective functions (LMPPD(l)) is proposed, where multiple reactions of the lower levels do not lead to any uncertainty in the upper-level decision making. Under the assumption that the constrained set is nonempty and bounded, a necessary optimality condition is obtained. Two types of geometric properties of the solution sets are studied. It is demonstrated that the feasible set of LMPPD(l) is neither necessarily composed of faces of the constrained set nor necessarily connected. These properties are different from the existing theoretical results for linear multilevel programming problems.     

Equivalence of two linear programming relaxations for broadcast scheduling

A server needs to compute a broadcast schedule for n pages whose request times are known in advance. Outputting a page satisfies all outstanding requests for the page. The goal is to minimize the average waiting time of a client. In this paper, we show the equivalence of two apparently different relaxations that have been considered for this problem.     

Robust linear optimization under general norms

We explicitly characterize the robust counterpart of a linear programming problem with uncertainty set described by an arbitrary norm. Our approach encompasses several approaches from the literature and provides guarantees for constraint violation under probabilistic models that allow arbitrary dependencies in the distribution of the uncertain coefficients.     

Piecewise Deterministic Markov Control Processes with Feedback Controls and Unbounded Costs

The control of piecewise-deterministic processes is studied where only local boundedness of the data is assumed. Moreover the discount rate may be zero. The value function is shown to be solution to the Bellman equation in a weak sense; however the solution concept is strong enough to generate optimal policies. Continuity and compactness conditions are given for the existence of nonrelaxed optimal feedback controls.     

Stability Conditions of the MMAP [ K ]/ G [ K ]/1/ LCFS Preemptive Repeat Queue

In this paper, we study the stability conditions of the MMAP[K]/G[K]/1/LCFS preemptive repeat queue. We introduce an embedded Markov chain of matrix M/G/1 type with a tree structure and identify conditions for the Markov chain to be ergodic. First, we present three conventional methods for the stability problem of the queueing system of interest. These methods are either computationally demanding or do not provide accurate information for system stability. Then we introduce a novel approach that develops two linear programs whose solutions provide sufficient conditions for stability or instability of the queueing system. The new approach is numerically efficient. The advantages and disadvantages of the methods introduced in this paper are analyzed both theoretically and numerically.     

Thinking and Turing's test

The questions raised by A. M. Turing in his paper on thought and machines are discussed. Human thought is considered in turn as a concept of normal language usage, as a basic concept of psychology, and as the basis of intellectual activity. It is concluded that neither of these notions of thought identifies something specific that a human being can or cannot do. The imitation game proposed by Turing for deciding whether a machine can think is found to result from an arbitrary empoverishment of the channel of communication between the interrogator and the item under investigation. Turing's notions of thinking are shown to lead to logical difficulties. An alternative view of consciousness, that would place it beyond the reach of any finite test, is finally discussed.     

核磁共振跟踪反应法研究5—O—乙酰基—2.3-双脱氧-D.L-呋喃戊糖基氯的合成

以乙酸四氢呋喃甲酯为原料,通过自由基氯化反应,合成双脱氧核苷类抗艾滋病药物的重要中间体化合物5—0—乙酰基—2.3—双脱氧—D.L—吹喃戊糖基氯.对于此种不稳定的中间体化合物,用~1HNMR跟踪监测反应物的方法,研究了改变反应时间、反应温度和反应物摩尔比对反应产物的影响,获得了最佳的反应条件,使其收率从10％提高到50％左右.     

The projective method for solving linear matrix inequalities

Numerous problems in control and systems theory can be formulated in terms of linear matrix inequalities (LMI). Since solving an LMI amounts to a convex optimization problem, such formulations are known to be numerically tractable. However, the interest in LMI-based design techniques has really surged with the introduction of efficient interior-point methods for solving LMIs with a polynomial-time complexity. This paper describes one particular method called the Projective Method. Simple geometrical arguments are used to clarify the strategy and convergence mechanism of the Projective algorithm. A complexity analysis is provided, and applications to two generic LMI problems (feasibility and linear objective minimization) are discussed.