An Efficient Framework for Order Optimization

Since the introduction of cost-based query optimization, the performance-critical role of interesting orders has been recognized. Some algebraic operators change interesting orders (e.g. sort and select), while others exploit interesting orders (e.g. merge join). The two operations performed by any query optimizer during plan generation are 1) computing the resulting order given an input order and an algebraic operator and 2) determining the compatibility between a given input order and the required order a given algebraic operator can beneficially exploit. Since these two operations are called millions of times during plan generation, they are highly performance-critical. The third crucial parameter is the space requirement for annotating every plan node with its output order. Lately, a powerful framework for reasoning about orders has been developed, which is based on functional dependencies. Within this framework, the current state-of-the-art algorithms for implementing the above operations both have a lower bound time requirement of Omega(n), where n is the number of functional dependencies involved. Further, the lower bound for the space requirement for every plan node is Omega(n). We improve these bounds by new algorithms with upper time bounds O(1). That is, our algorithms for both operations work in constant time during plan generation, after a one-time preparation step. Further, the upper bound for the space requirement for plan nodes is O(1) for our approach. Besides, our algorithm reduces the search space by detecting and ignoring irrelevant orderings. Experimental results with a full fledged query optimizer show that our approach significantly reduces the total time needed for plan generation. As a corollary of our experiments, it follows that the time spent for order processing is a non-neglectable part of plan generation

An Efficient Framework For Fast Computer Aided Design of Microwave Circuits Based on the Higher-Order 3D Finite-Element Method

In this paper, an efficient computational framework for the full-wave design by optimization of complex microwave passive devices, such as antennas, filters, and multiplexers, is described. The framework consists of a computational engine, a 3D object modeler, and a graphical user interface. The computational engine, which is based on a finite element method with curvilinear higher-order tetrahedral elements, is coupled with built-in or external gradient-based optimization procedures. For speed, a model order reduction technique is used and the gradient computation is achieved by perturbation with geometry deformation, processed on the level of the individual mesh nodes. To maximize performance, the framework is targeted to multicore CPU architectures and its extended version can also use multiple GPUs. To illustrate the accuracy and high efficiency of the framework, we provide examples of simulations of a dielectric resonator antenna and full-wave design by optimization of two diplexers involving tens of unknowns, and show that the design can be completed within the duration of a few simulations using industry-standard FEM solvers. The accuracy of the design is confirmed by measurements

Warehouse Storing and Collecting of Parts

This report deals with reducing the high costs resulting from the wear and tear of the fork-lifts used to store or collect items in a warehouse. Two problems were identified and addressed separately. One concerns the way items should be stored or collected at storage locations on the shelves of one corridor. The other problem seeks for an efficient way to define which fork-lift should operate on each corridor, and the order by which the fork-lifts should visit the corridors. We give to both problems formulations that fit in the framework of combinatorial optimization

Portfolio Selection in Multidimensional General and Partial Moment Space.

This paper develops a general approach for the single period portfolio optimization problem in a multidimensional general and partial moment space. A shortage function is defined that looks for possible increases in odd moments and decreases in even moments. A main result is that this shortage function ensures suffcient conditions for global optimality. It also forms a natural basis for developing tests on the infuence of additional moments. Furthermore, a link is made with an approximation of an arbitrary order of a general indirectutility function. This nonparametric effciency measurement framework permits to dfferentiate mainly between portfolio effciency and allocative effciency. Finally, information can,in principle, be inferred about the revealed risk aversion, prudence, temperance and otherhigher-order risk characteristics of investors.shortage function, efficient frontier, K-moment portfolios