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KOBV-Volltextserver - Langfristiger Zugriff auf Kluwer-, Springer- und Elsevier-Zeitschriften
(2005)
Seit einigen Jahren lizenzieren Bibliotheken mit erheblichem finanziellen Aufwand elektronische Zeitschriften. Anders als bei einer Papierausgabe ist der dauerhafte Zugriff auf die bezahlten Dokumente allerdings nicht garantiert: Die e-Zeitschriften liegen auf dem Verlagsserver, und der Verlag schaltet den Zugriff (meist IP-Range des Campus) auf seinem Server frei. Wird der Zugriff von Verlagsseite abgeschaltet, erlöschen sämtliche Zugriffsrechte, auch auf die in der Vergangenheit lizenzierten und bezahlten Zeitschriften. Auf die neuen Abonnementbedingungen hat das Friedrich-Althoff-Konsortium (FAK) reagiert und in seinen Vertr"agen den Erwerb der Archivdaten beim Auslaufen eines Vertrages festgeschrieben. Im Rahmen eines Projektes baut die KOBV-Zentrale einen Zeitschriftenserver auf, um den Zugriff auf die lizenzierten elektronischen Zeitschriften auch nach Ablauf der Lizenzverträge zu gewährleisten. Den Grundstock bilden die vom FAK in den Jahren 1997-2003 lizenzierten elektronischen Zeitschriften der Verlage Kluwer Academic Press, Springer und Elsevier - ein Volumen von rund 1.600 Zeitschriftentiteln mit knapp 1.400.000 Artikeln. Beim Aufbau des Zeitschriftenservers kommt der vertraglich-organisatorischen Komponente eine ebenso wichtige Rolle zu wie der technischen Realisierung. Hier hat die KOBV-Zentrale ein transparentes Verfahren konzipiert und umgesetzt, um für die Verlage die notwendige Vertrauensbasis zu schaffen und gleichzeitig den Einrichtungen ihren berechtigten Zugriff auf die Zeitschriften-Volltexte zu sichern. Die Zeitschriftenartikel werden sowohl im Rahmen des KOBV-Volltextservers, einem neuen Internet-Dienst der KOBV-Zentrale, zugänglich gemacht - volltext-indiziert mit der Suchmaschine Swish-e - als auch integriert in die Metadatensuche und den Open-Linking-Dienst des KOBV-Portals. Während die Metadatenrecherche und die Sicht auf die Abstracts für alle offen sind, ist der Zugriff auf die Artikel-Volltexte auf die an den Verträgen beteiligten Einrichtungen beschränkt. Dazu wurde ein Authentifizierungsverfahren auf der Basis von IP-Ranges eingerichtet. Der vorliegende Text ist die schriftliche Fassung eines gleichnamigen Vortrages auf der ASpB-Tagung 2005 \glqq Spezialbibliotheken zwischen Auftrag und Ressourcen{\grqq} der Arbeitsgemeinschaft der Spezialbibliotheken, die vom 06.-09. September 2005 in der Technischen Universität München stattfand.
With annual consumption of approx. 95 billion cubic me-ters and similar amounts of gas just transshipped through Germany toother EU states, Germany’s gas transport system plays a vital role inEuropean energy supply. The complex, more than 40,000 km long high-pressure transmission network is controlled by several transmission sys-tem operators (TSOs) whose main task is to provide security of supplyin a cost-efficient way. Given the slow speed of gas flows through the gastransmission network pipelines, it has been an essential task for the gasnetwork operators to enhance the forecast tools to build an accurate andeffective gas flow prediction model for the whole network. By incorpo-rating the recent progress in mathematical programming and time seriesmodeling, we aim to model natural gas network and predict gas in- andout-flows at multiple supply and demand nodes for different forecastinghorizons. Our model is able to describe the dynamics in the network bydetecting the key nodes, which may help to build an optimal manage-ment strategy for transmission system operators.
The benefits of cutting planes based on the perspective function are well known for many specific classes of mixed-integer nonlinear programs with on/off structures. However, we are not aware of any empirical studies that evaluate their applicability and computational impact over large, heterogeneous test sets in general-purpose solvers. This paper provides a detailed computational study of perspective cuts within a linear programming based branch-and-cut solver for general mixed-integer nonlinear programs. Within this study, we extend the applicability of perspective cuts from convex to nonconvex nonlinearities. This generalization is achieved by applying a perspective strengthening to valid linear inequalities which separate solutions of linear relaxations. The resulting method can be applied to any constraint where all variables appearing in nonlinear terms are semi-continuous and depend on at least one common indicator variable. Our computational experiments show that adding perspective cuts for convex constraints yields a consistent improvement of performance, and adding perspective cuts for nonconvex constraints reduces branch-and-bound tree sizes and strengthens the root node relaxation, but has no significant impact on the overall mean time.
We consider a system dynamics model that describes the effect of human activity on natural resources. The central stocks are the accumulated profit, the industry structures, and the water resources. The model can be controlled through two time-dependent parameters. The goal in this paper is to find a parameter setting that leads to a maximization of a performance index, which reflects both environmental and economic aspects. Thus, the goal is to identify the most sustainable stock of industry structures within the model's constraints and assumptions. In order to find a proven global optimal parameter set, we formulate the System Dynamics Optimization model as a mixed-integer nonlinear problem that is accessible for numerical solvers. Due to the dynamic structure of the model, certain steps of the solution process must be handled with greater care, compared to standard non-dynamic problems. We describe our approach of solving the industry structure model and present computational results. In addition, we discuss the limitations of the approach and next steps.
Conflict Analysis for MINLP
(2020)
The generalization of MIP techniques to deal with nonlinear, potentially non-convex, constraints have been a fruitful direction of research for computational MINLP in the last decade. In this paper, we follow that path in order to extend another essential subroutine of modern MIP solvers towards the case of nonlinear optimization: the analysis of infeasible subproblems for learning additional valid constraints. To this end, we derive two different strategies, geared towards two different solution approaches. These are using local dual proofs of infeasibility for LP-based branch-and-bound and the creation of nonlinear dual proofs for NLP-based branch-and-bound, respectively. We discuss implementation details of both approaches and present an extensive computational study, showing that both techniques can significantly enhance performance when solving MINLPs to global optimality.
This paper is concerned with optimal operation of pressurized water supply networks at a fixed point in time. We use a mixed-integer nonlinear programming (MINLP) model incorporating both the nonlinear physical laws and the discrete decisions such as switching pumps on and off. We demonstrate that for instances from our industry partner, these stationary models can be solved to ε-global optimality within small running times using problem-specific presolving and state-of-the-art MINLP algorithms.
In our modeling, we emphasize the importance of distinguishing between what we call real and imaginary flow, i.e., taking into account that the law of Darcy-Weisbach correlates pressure difference and flow along a pipe if and only if water is available at the high pressure end of a pipe. Our modeling solution extends to the dynamic operative planning problem.
The most important ingredient for solving mixed-integer nonlinear programs (MINLPs) to global epsilon-optimality with spatial branch and bound is a tight, computationally tractable relaxation. Due to both theoretical and practical considerations, relaxations of MINLPs are usually required to be convex. Nonetheless, current optimization solver can often successfully handle a moderate presence of nonconvexities, which opens the door for the use of potentially tighter nonconvex relaxations. In this work, we exploit this fact and make use of a nonconvex relaxation obtained via aggregation of constraints: a surrogate relaxation. These relaxations were actively studied for linear integer programs in the 70s and 80s, but they have been scarcely considered since. We revisit these relaxations in an MINLP setting and show the computational benefits and challenges they can have. Additionally, we study a generalization of such relaxation that allows for multiple aggregations simultaneously and present the first algorithm that is capable of computing the best set of aggregations. We propose a multitude of computational enhancements for improving its practical performance and evaluate the algorithm’s ability to generate strong dual bounds through extensive computational experiments.
To attain the highest performance of energy supply systems, it is necessary to
rationally determine types, capacities, and numbers of equipment in consideration of
their operational strategies corresponding to seasonal and hourly variations in energy
demands. In the combinatorial optimization method based on the mixed-integer linear
programming (MILP), integer variables are used to express the selection, numbers, and
on/off status of operation of equipment, and the number of these variables increases
with those of equipment and periods for variations in energy demands, and affects the computation efficiency significantly. In this paper, a MILP method utilizing the
hierarchical relationship between design and operation variables is proposed to solve the optimal design problem of energy supply systems efficiently: At the upper level, the
optimal values of design variables are searched by the branch and bound method; At
the lower level, the values of operation variables are optimized independently at each period by the branch and bound method under the values of design variables given
tentatively during the search at the upper level; Lower bounds for the optimal value of the objective function are evaluated, and are utilized for the bounding operations at both the levels. This method is implemented into open and commercial MILP solvers. Illustrative and practical case studies on the optimal design of cogeneration systems are conducted, and the validity and effectiveness of the proposed method are clarified.
In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs) into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required. Depending on the purpose of the model this step can be based on experimental data or ODE simulations and characteristics. Analysis of the resulting Boolean model, both on its own and in comparison with the ODE model, then allows to investigate system properties not accessible in a purely continuous setting. The method is exemplarily applied to a previously published model of the bovine estrous cycle, which leads to new insights regarding the regulation among the components, and also indicates strongly that the system is tailored to generate stable oscillations.
We present Undercover, a primal heuristic for nonconvex mixed-integer nonlinear programming (MINLP) that explores a mixed-integer linear subproblem (sub-MIP) of a given MINLP. We solve a vertex covering problem to identify a minimal set of variables that need to be fixed in order to linearize each constraint, a so-called cover. Subsequently, these variables are fixed to values obtained from a reference point, e.g., an optimal solution of a linear relaxation. We apply domain propagation and conflict analysis to try to avoid infeasibilities and learn from them, respectively. Each feasible solution of the sub-MIP corresponds to a feasible solution of the original problem.
We present computational results on a test set of mixed-integer quadratically constrained programs (MIQCPs) and general MINLPs from MINLPLib. It turns out that the majority of these instances allow for small covers. Although general in nature, the heuristic appears most promising for MIQCPs, and complements nicely with existing root node heuristics in different state-of-the-art solvers.
