In data stream processing systems, Quality of Service (or QoS) requirements, as specified by users, are extremely important. Unlike in a database management system (DBMS), a query in a data stream management system (DSMS) cannot be optimized once and executed. It has been shown that different scheduling strategies are useful in trading tuple latency requirements with memory and throughput requi...

We introduce a novel global optimizationmethod calledContinuous GRASP (C-GRASP) which extends Feo and Resende’s greedy randomized adaptive search procedure (GRASP) from the domain of discrete optimization to that of continuous global optimization. This stochastic local search method is simple to implement, is widely applicable, and does not make use of derivative information, thus making it a w...

Global Optimization tackles the problem of finding all feasible points of a set of constraints that optimize an objective function. In the following, we restrict our attention to Continuous Global Optimization, and more specifically to Global Optimization over interval domains [6, 9]. We believe that Software Design for Continuous Global Optimization has things in common with Astronomy of Sixte...

A heterogeneous model consists of a solid model and a number of spatially distributed material attributes. Much progress has been made in developing methods for construction, design, and editing of such models. We consider the problem of optimization of a heterogeneous model, and show that its representation by a continuous function defined over a constructively represented domain naturally lea...

Many machine learning algorithms can be formulated as the minimization of a train ing criterion which involves training errors on each training example and some hyper parameters which are kept xed during this minimization When there is only a single hyper parameter one can easily explore how its value a ects a model selection criterion that is not the same as the training criterion and is used ...

Every function of several variables with the continuous second derivative can be convexified (i.e., made convex) by adding to it a quadratic “convexifier”. In this paper we give simple estimates on the bounds of convexifiers. Using the idea of convexification, many problems in applied mathematics can be reduced to convex mathematical programming models. This is illustrated here for nonlinear pr...

Robot footstep planning strategies can be divided in two main approaches: discrete searches and continuous optimizations. While discrete searches have been broadly applied, continuous optimizations approaches have been restricted for humanoid platforms. This article introduces a generalized continuous-optimization approach for multilegged footstep planning which can be adapted to different plat...

“Discrete Convex Analysis” is aimed at establishing a novel theoretical framework for solvable discrete optimization problems by means of a combination of the ideas in continuous optimization and combinatorial optimization. The theoretical framework of convex analysis is adapted to discrete settings and the mathematical results in matroid/submodular function theory are generalized. Viewed from ...

Business-driven IT management practices often involve the performance optimization of a system according to business criteria. The increased attention to dynamical aspects of the system behavior, the preference for simulative approaches rather than analytical ones, and the increased level of complexity posed by business-driven performance evaluation significantly complicate the optimization of ...