Relevant bibliographies by topics / Schwache und starke Dualität

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters

Academic literature on the topic 'Schwache und starke Dualität'

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Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Schwache und starke Dualität"

1

Riecke, Jörg. "Bittner, Andreas: Starke ›schwache‹ Verben – ›schwache‹ starke Verben. Deutsche Verbflexion und Natürlichkeit." Informationen Deutsch als Fremdsprache 24, no. 2-3 (June 1, 1997): 205–8. http://dx.doi.org/10.1515/infodaf-1997-2-312.

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2

Schmidt, Hartmut. "Starke Zahlen, schwache Kurse." kma - Klinik Management aktuell 10, no. 09 (September 2005): 39. http://dx.doi.org/10.1055/s-0036-1573433.

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Mediclin hat gute Halbjahres-Zahlen vorgelegt und ist auf dem besten Weg, in diesem Jahr den Break Even zu schaffen. In den Kursen hat sich die positive Entwicklung noch nicht niedergeschlagen: Das Unternehmen bietet den Anlegern zu wenig Wachstumsfantasie.
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3

Beedham, Christopher. "Review of Bittner (1996): Starke ‘schwache’ Verben – schwache ‘starke’ Verben: deutsche Verbflexion und Natürlichkeit." Studies in Language 22, no. 1 (January 1, 1998): 212–18. http://dx.doi.org/10.1075/sl.22.1.09bee.

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4

Meyeringh, Uwe. "Bibliotheksstärkungsgesetz: Schwache Stärke - starke Schwäche!" Bibliotheksdienst 53, no. 7-8 (July 26, 2019): 456–59. http://dx.doi.org/10.1515/bd-2019-0065.

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Zusammenfassung Wer ein Haus baut, fängt mit dem Keller an. Das Bibliotheksstärkungsgesetz schraubt am Dach, dabei werden entscheidende Probleme der Statik ausgeblendet. Dennoch hat es eine Stärke: Es erfasst das sich stetig verbreiternde Spektrum der bibliothekarischen Tätigkeiten und stellt Landesmittel hierfür in Aussicht. Die wesentliche Schwäche des Bibliotheksstärkungsgesetzes besteht darin, die Sonntagsruhe der Beschäftigten zur Disposition zu stellen.
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5

Kluess, H. G., T. Noppeney, F. X. Breu, U. Ehresmann, H. E. Gerlach, H. J. Hermanns, H. Nüllen, et al. "Leitlinie zur Diagnostik und Therapie der Krampfadererkrankung." Phlebologie 39, no. 05 (September 2010): 271–89. http://dx.doi.org/10.1055/s-0038-1639022.

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Leitlinien sind systematisch erarbeitete Empfehlungen, um den Arzt in Klinik und Praxis bei Entscheidungen über eine angemessene Versorgung des Patienten im Rahmen spezi-fischer klinischer Umstände zu unterstützen. Leitlinien gelten für Standardsituationen und berücksichtigen die aktuellen, zu den entsprechenden Fragestellungen zur Verfügung stehenden wissenschaftlichen Erkenntnisse. Leitlinien bedürfen der ständigen Überprüfung und eventuell der Änderung auf dem Boden des wissenschaftlichen Erkenntnisstandes und der Praktikabilität in der täglichen Praxis. Durch die Leitlinien soll die Methodenfreiheit des Arztes nicht eingeschränkt werden. Ihre Beachtung garantiert nicht in jedem Fall den diagnostischen und therapeutischen Erfolg. Leitlinien erheben keinen Anspruch auf Vollständigkeit. Die Entscheidung über die Angemessenheit der zu ergreifenden Maßnahmen trifft der Arzt unter Berücksichtigung der individuellen Problematik.Die Empfehlungsgrade entsprechen denen des American College of Chest Physicians (ACCP):GRADE 1A: starke Empfehlung, hohe Qualität der EvidenzGRADE 1B: starke Empfehlung, mittlere Qualität der EvidenzGRADE 1C: starke Empfehlung, niedrige Qualität der EvidenzGRADE 2A: schwache Empfehlung, hohe Qualität der EvidenzGRADE 2B: schwache Empfehlung, mittlere Qualität der EvidenzGRADE 2C: schwache Empfehlung, niedrige Qualität der EvidenzAngaben der evidenzbasierten Level erfolgen entsprechend den Empfehlungen der DEGAM (Deutsche Gesellschaft für Allgemeinmedizin und Familienmedizin):A sehr gut fundiertB mittelmäßig fundiertC mäßige wissenschaftliche Grundlage
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6

Erb, Hans-Peter, Miriam Büscher, Gerd Bohner, and Susanne Rank. "Starke und schwache Argumente als Teile derselben Botschaft." Zeitschrift für Sozialpsychologie 36, no. 2 (January 2005): 61–75. http://dx.doi.org/10.1024/0044-3514.36.2.61.

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Zusammenfassung: In der Persuasionsforschung beinhaltet die Standard-Methode zur Untersuchung kognitiven Aufwands bei der Verarbeitung persuasiver Botschaften die Variation der Überzeugungskraft der Botschaftsargumente zwischen Versuchspersonen. Wenn Rezipienten diese Botschaften ausführlich verarbeiten, führen Botschaften mit starken Argumenten zu positiveren Einstellungen als Botschaften mit schwachen Argumenten. Die Standard-Methode birgt allerdings einige Nachteile: Die Qualität der Botschaft kann unerwünschterweise die Evaluation der Persuasionsquelle beeinflussen, Botschaften mit eindeutig schwachen oder starken Argumenten unterdrücken den Einfluss persuasiver Information, die nicht Teil der Botschaft ist, und sie eignen sich nicht zum Nachweis verzerrter Informationsverarbeitung. Die “Mixed-Message-Methode” überwindet diese Schwächen. Sie beinhaltet die Variation der Überzeugungskraft der Argumente zu einzelnen Aspekten innerhalb einer Botschaft. Die Differenzierung zwischen spezifisch auf unterschiedliche Aspekte bezogenen Einstellungsskalen kann als Maß für den kognitiven Aufwand bei der Verarbeitung persuasiver Botschaften dienen.
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7

Riwar, Leslie-Joana, Nils Trapp, Katharina Root, Renato Zenobi, and François Diederich. "Supramolekulare Kapseln: starke und schwache Chalkogenbrücken im Vergleich." Angewandte Chemie 130, no. 52 (November 27, 2018): 17506–12. http://dx.doi.org/10.1002/ange.201812095.

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8

Gesang, Bernward, Marcel Mertz, Barbara Meyer-Zehnder, and Stella Reiter-Theil. "Starke und schwache Autonomie – eine hilfreiche Unterscheidung für die Vorbeugung von Unter- und Überbehandlung." Ethik in der Medizin 25, no. 4 (October 18, 2012): 329–41. http://dx.doi.org/10.1007/s00481-012-0226-4.

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9

Jage. "Opioids for pain therapy." Therapeutische Umschau 56, no. 8 (August 1, 1999): 435–40. http://dx.doi.org/10.1024/0040-5930.56.8.435.

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In der klinischen Praxis sowie in der wissenschaftlichen Literatur werden die Begriffe «Opioide» und «Opiat» synonym benutzt. Die Bezeichnung «Narkotika» sollte ebenso wie «Gift» der Vergangenheit angehören. Leider ist seitens des Gesetzgebers noch immer die Bezeichnung «Betäubungsmittel» sanktioniert. Dies entspricht nicht der klinisch breit genutzten Wirkung und ist aus sachlicher Sicht veraltet. Man unterscheidet Opioide gegen mäßige bis mittelstarke Schmerzen (Tramadol, Tilidin, Dihydrocodein) und gegen starke bis unerträgliche Schmerzen (Morphin, Fentanyl, Buprenorphin, Hydromorphon, Levo-Methadon) (Tab. 1). Morphin ist das Referenzopioid gegen starke Schmerzen, die anderen Opioide dieser Gruppe sind Alternativen, wenn die Morphintherapie unzureichend oder nicht möglich ist. Auf der Grundlage der Wirkungen an den Opioidrezeptoren unterscheidet man nicht nur schwache von stark wirksamen Opioiden, sondern innerhalb der stark wirksamen Opioide die reinen Opioidrezeptor-Agonisten von den partiellen Agonisten bzw. von den Agonisten mit antagonistischem Anteil (Tab. 1).
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10

Bloomer, Robert K. "Starke “schwache” Verben — schwache “starke” Verben: Deutsche Verbflexion und Natürlichkeit. By Andreas Bittner. (Studien zur deutschen Grammatik, 51.) Tübingen: Stauffenburg Verlag, 1996. Pp. xiii, 224. Paper. DM 68,00." American Journal of Germanic Linguistics and Literatures 11, no. 1 (1999): 103–7. http://dx.doi.org/10.1017/s1040820700002481.

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Dissertations / Theses on the topic "Schwache und starke Dualität"

1

Heinrich, André. "Fenchel duality-based algorithms for convex optimization problems with applications in machine learning and image restoration." Doctoral thesis, Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-108923.

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The main contribution of this thesis is the concept of Fenchel duality with a focus on its application in the field of machine learning problems and image restoration tasks. We formulate a general optimization problem for modeling support vector machine tasks and assign a Fenchel dual problem to it, prove weak and strong duality statements as well as necessary and sufficient optimality conditions for that primal-dual pair. In addition, several special instances of the general optimization problem are derived for different choices of loss functions for both the regression and the classifification task. The convenience of these approaches is demonstrated by numerically solving several problems. We formulate a general nonsmooth optimization problem and assign a Fenchel dual problem to it. It is shown that the optimal objective values of the primal and the dual one coincide and that the primal problem has an optimal solution under certain assumptions. The dual problem turns out to be nonsmooth in general and therefore a regularization is performed twice to obtain an approximate dual problem that can be solved efficiently via a fast gradient algorithm. We show how an approximate optimal and feasible primal solution can be constructed by means of some sequences of proximal points closely related to the dual iterates. Furthermore, we show that the solution will indeed converge to the optimal solution of the primal for arbitrarily small accuracy. Finally, the support vector regression task is obtained to arise as a particular case of the general optimization problem and the theory is specialized to this problem. We calculate several proximal points occurring when using difffferent loss functions as well as for some regularization problems applied in image restoration tasks. Numerical experiments illustrate the applicability of our approach for these types of problems.
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2

Etemire, Eloho [Verfasser], Matthias [Akademischer Betreuer] Gunzer, Wiebke [Akademischer Betreuer] Hansen, and Shirley [Akademischer Betreuer] Knauer. "Molekulare Mechanismen der differentiellen Aktivierung naiver T-Zellen durch schwache und starke antigen-präsentierende Zellen / Eloho Etemire. Gutachter: Wiebke Hansen ; Shirley Knauer. Betreuer: Matthias Gunzer." Duisburg, 2013. http://d-nb.info/1043906207/34.

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3

Lorenz, Nicole. "Application of the Duality Theory." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-94108.

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The aim of this thesis is to present new results concerning duality in scalar optimization. We show how the theory can be applied to optimization problems arising in the theory of risk measures, portfolio optimization and machine learning. First we give some notations and preliminaries we need within the thesis. After that we recall how the well-known Lagrange dual problem can be derived by using the general perturbation theory and give some generalized interior point regularity conditions used in the literature. Using these facts we consider some special scalar optimization problems having a composed objective function and geometric (and cone) constraints. We derive their duals, give strong duality results and optimality condition using some regularity conditions. Thus we complete and/or extend some results in the literature especially by using the mentioned regularity conditions, which are weaker than the classical ones. We further consider a scalar optimization problem having single chance constraints and a convex objective function. We also derive its dual, give a strong duality result and further consider a special case of this problem. Thus we show how the conjugate duality theory can be used for stochastic programming problems and extend some results given in the literature. In the third chapter of this thesis we consider convex risk and deviation measures. We present some more general measures than the ones given in the literature and derive formulas for their conjugate functions. Using these we calculate some dual representation formulas for the risk and deviation measures and correct some formulas in the literature. Finally we proof some subdifferential formulas for measures and risk functions by using the facts above. The generalized deviation measures we introduced in the previous chapter can be used to formulate some portfolio optimization problems we consider in the fourth chapter. Their duals, strong duality results and optimality conditions are derived by using the general theory and the conjugate functions, respectively, given in the second and third chapter. Analogous calculations are done for a portfolio optimization problem having single chance constraints using the general theory given in the second chapter. Thus we give an application of the duality theory in the well-developed field of portfolio optimization. We close this thesis by considering a general Support Vector Machines problem and derive its dual using the conjugate duality theory. We give a strong duality result and necessary as well as sufficient optimality conditions. By considering different cost functions we get problems for Support Vector Regression and Support Vector Classification. We extend the results given in the literature by dropping the assumption of invertibility of the kernel matrix. We use a cost function that generalizes the well-known Vapnik's ε-insensitive loss and consider the optimization problems that arise by using this. We show how the general theory can be applied for a real data set, especially we predict the concrete compressive strength by using a special Support Vector Regression problem.
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4

Testov, Vladimir A. "On Evaluation Problem of the Quality of Educational Models." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-81008.

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The current approach to assessing the educational quality applicable to assessing objects and processes formed and realized in producing spheres is widely spread. However, as education is a much more complicated anthropological, social and cultural object in comparison to that of production, the above mentioned approach is least effective. In education both \"strong\" and \"weak\" models are used. There do not exist measurement instruments for accurate assessing mild results. Self control, expert assessing method and portfolio are being put forward.
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5

Heinrich, André. "Fenchel duality-based algorithms for convex optimization problems with applications in machine learning and image restoration." Doctoral thesis, 2012. https://monarch.qucosa.de/id/qucosa%3A19869.

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The main contribution of this thesis is the concept of Fenchel duality with a focus on its application in the field of machine learning problems and image restoration tasks. We formulate a general optimization problem for modeling support vector machine tasks and assign a Fenchel dual problem to it, prove weak and strong duality statements as well as necessary and sufficient optimality conditions for that primal-dual pair. In addition, several special instances of the general optimization problem are derived for different choices of loss functions for both the regression and the classifification task. The convenience of these approaches is demonstrated by numerically solving several problems. We formulate a general nonsmooth optimization problem and assign a Fenchel dual problem to it. It is shown that the optimal objective values of the primal and the dual one coincide and that the primal problem has an optimal solution under certain assumptions. The dual problem turns out to be nonsmooth in general and therefore a regularization is performed twice to obtain an approximate dual problem that can be solved efficiently via a fast gradient algorithm. We show how an approximate optimal and feasible primal solution can be constructed by means of some sequences of proximal points closely related to the dual iterates. Furthermore, we show that the solution will indeed converge to the optimal solution of the primal for arbitrarily small accuracy. Finally, the support vector regression task is obtained to arise as a particular case of the general optimization problem and the theory is specialized to this problem. We calculate several proximal points occurring when using difffferent loss functions as well as for some regularization problems applied in image restoration tasks. Numerical experiments illustrate the applicability of our approach for these types of problems.
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6

Lorenz, Nicole. "Application of the Duality Theory: New Possibilities within the Theory of Risk Measures, Portfolio Optimization and Machine Learning." Doctoral thesis, 2011. https://monarch.qucosa.de/id/qucosa%3A19760.

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The aim of this thesis is to present new results concerning duality in scalar optimization. We show how the theory can be applied to optimization problems arising in the theory of risk measures, portfolio optimization and machine learning. First we give some notations and preliminaries we need within the thesis. After that we recall how the well-known Lagrange dual problem can be derived by using the general perturbation theory and give some generalized interior point regularity conditions used in the literature. Using these facts we consider some special scalar optimization problems having a composed objective function and geometric (and cone) constraints. We derive their duals, give strong duality results and optimality condition using some regularity conditions. Thus we complete and/or extend some results in the literature especially by using the mentioned regularity conditions, which are weaker than the classical ones. We further consider a scalar optimization problem having single chance constraints and a convex objective function. We also derive its dual, give a strong duality result and further consider a special case of this problem. Thus we show how the conjugate duality theory can be used for stochastic programming problems and extend some results given in the literature. In the third chapter of this thesis we consider convex risk and deviation measures. We present some more general measures than the ones given in the literature and derive formulas for their conjugate functions. Using these we calculate some dual representation formulas for the risk and deviation measures and correct some formulas in the literature. Finally we proof some subdifferential formulas for measures and risk functions by using the facts above. The generalized deviation measures we introduced in the previous chapter can be used to formulate some portfolio optimization problems we consider in the fourth chapter. Their duals, strong duality results and optimality conditions are derived by using the general theory and the conjugate functions, respectively, given in the second and third chapter. Analogous calculations are done for a portfolio optimization problem having single chance constraints using the general theory given in the second chapter. Thus we give an application of the duality theory in the well-developed field of portfolio optimization. We close this thesis by considering a general Support Vector Machines problem and derive its dual using the conjugate duality theory. We give a strong duality result and necessary as well as sufficient optimality conditions. By considering different cost functions we get problems for Support Vector Regression and Support Vector Classification. We extend the results given in the literature by dropping the assumption of invertibility of the kernel matrix. We use a cost function that generalizes the well-known Vapnik's ε-insensitive loss and consider the optimization problems that arise by using this. We show how the general theory can be applied for a real data set, especially we predict the concrete compressive strength by using a special Support Vector Regression problem.
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Books on the topic "Schwache und starke Dualität"

1

Andreas, Bittner. Starke "schwache" Verben, schwache "starke" Verben: Deutsche Verbflexion und Natürlichkeit. Tübingen: Stauffenburg, 1996.

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2

Das verspielte Papier: Über starke, schwache und vollkommen misslungene Gedichte. München: Luchterhand, 2014.

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3

Kusová, Jana. Morphologische Varianz der peripheren Substantivparadigmen im geschriebenen Gegenwartsdeutsch: Schwache Maskulina, starke Feminina und gemischte Substantive. Wien: Praesens Verlag, 2014.

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4

Kühnel, Klaus. August der Starke und das schwache Geschlecht: Die Liebschaften des Kurfürsten Friedrich August I. von Sachsen. Wittenberg: Drei Kastanien Verlag, 2005.

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Frauenknecht, Sabine, and Gabriele Harlieb. Für starke Frauen und schwache Stunden. Herder, Freiburg, 2001.

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Book chapters on the topic "Schwache und starke Dualität"

1

Schuster, Thomas. "Starke Medien, schwache Wirkung?" In Staat und Medien, 73–81. Wiesbaden: VS Verlag für Sozialwissenschaften, 2004. http://dx.doi.org/10.1007/978-3-322-80542-3_6.

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2

Warnecke, Gerald. "Schwache und starke Konvergenz." In Analytische Methoden in der Theorie der Erhaltungsgleichungen, 117–45. Wiesbaden: Vieweg+Teubner Verlag, 1999. http://dx.doi.org/10.1007/978-3-663-09264-3_5.

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3

Niedrig, Heinz. "Starke und Schwache Wechselwirkung." In Physik, 364–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-87838-1_18.

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Avenarius, Christine B. "Starke und Schwache Beziehungen." In Handbuch Netzwerkforschung, 99–111. Wiesbaden: VS Verlag für Sozialwissenschaften, 2010. http://dx.doi.org/10.1007/978-3-531-92575-2_9.

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Ptashne, Mark. "Starke und Schwache Wechselwirkungen." In Genregulation, 125. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74022-0_7.

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Schilcher, Bernd. "Starke und schwache Rechte." In Im Dienste der Gerechtigkeit, 353–91. Vienna: Springer Vienna, 2002. http://dx.doi.org/10.1007/978-3-7091-6162-3_15.

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Biehl, Ingrid. "Starke und schwache average-case Modelle." In Eine Grundlegung der Average-Case Komplexitätstheorie, 17–57. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-93465-9_2.

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Spiering, Christian. "Die starke und die schwache Kraft." In Auf der Suche nach der Urkraft, 19–22. Wiesbaden: Vieweg+Teubner Verlag, 1986. http://dx.doi.org/10.1007/978-3-322-86648-6_5.

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Niedrig, Heinz, and Martin Sternberg. "Starke und schwache Wechselwirkung: Atomkerne und Elementarteilchen." In HÜTTE – Das Ingenieurwissen, 1–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-57492-8_18-1.

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Bildhauer, Felix, Eric Fuß, Sandra Hansen-Morath, and Franziska Münzberg. "Starke und schwache Adjektivflexion in neuem korpuslinguistischen Licht." In Neues vom heutigen Deutsch, edited by Ludwig Eichinger and Albrecht Plewnia, 293–312. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110622591-014.

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