Thematische Bibliographien / Programming functions

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Programming functions“

Autor: Grafiati
Veröffentlicht am 28. Juni 2021
Zuletzt aktualisiert am 7. Februar 2022

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Zeitschriftenartikel zum Thema "Programming functions"

1

Neralić, Luka, and Sanjo Zlobec. "LFS functions in multi-objective programming." Applications of Mathematics 41, no. 5 (1996): 347–66. http://dx.doi.org/10.21136/am.1996.134331.

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Odersky, Martin. "Programming with variable functions." ACM SIGPLAN Notices 34, no. 1 (1999): 105–16. http://dx.doi.org/10.1145/291251.289433.

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Rhodes, Frank, and H. Paul Williams. "Discrete subadditive functions as Gomory functions." Mathematical Proceedings of the Cambridge Philosophical Society 117, no. 3 (1995): 559–74. http://dx.doi.org/10.1017/s0305004100073370.

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Our aim, in this paper, is to study a class of functions which occurs in pure integer programming, and to investigate conditions under which discrete subadditive functions belong to that class. The inspiration for the paper was the problem of classifying discrete metrics used in pattern recognition, while the methods of proof of the main theorem are those of pure integer programming.
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Baykasoğlu, Adil, and Sultan Maral. "Fuzzy functions via genetic programming." Journal of Intelligent & Fuzzy Systems 27, no. 5 (2014): 2355–64. http://dx.doi.org/10.3233/ifs-141205.

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Ahluwalia, Manu, and Larry Bull. "Coevolving functions in genetic programming." Journal of Systems Architecture 47, no. 7 (2001): 573–85. http://dx.doi.org/10.1016/s1383-7621(01)00016-9.

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Savage, Neil. "Using functions for easier programming." Communications of the ACM 61, no. 5 (2018): 29–30. http://dx.doi.org/10.1145/3193776.

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Alvarez, Fernando, and Nancy L. Stokey. "Dynamic Programming with Homogeneous Functions." Journal of Economic Theory 82, no. 1 (1998): 167–89. http://dx.doi.org/10.1006/jeth.1998.2431.

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Chou, J. H., Wei-Shen Hsia, and Tan-Yu Lee. "Convex programming with set functions." Rocky Mountain Journal of Mathematics 17, no. 3 (1987): 535–44. http://dx.doi.org/10.1216/rmj-1987-17-3-535.

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Wang, Chung-lie, and An-qing Xing. "Dynamic programming and penalty functions." Journal of Mathematical Analysis and Applications 150, no. 2 (1990): 562–73. http://dx.doi.org/10.1016/0022-247x(90)90123-w.

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Weir, T. "Programming with semilocally convex functions." Journal of Mathematical Analysis and Applications 168, no. 1 (1992): 1–12. http://dx.doi.org/10.1016/0022-247x(92)90185-g.

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Dissertationen zum Thema "Programming functions"

1

Christiansen, Jan [Verfasser]. "Investigating Minimally Strict Functions in Functional Programming / Jan Christiansen." Kiel : Universitätsbibliothek Kiel, 2012. http://d-nb.info/1024079805/34.

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2

Sharifi, Mokhtarian Faranak. "Mathematical programming with LFS functions." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=56762.

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Differentiable functions with a locally flat surface (LFS) have been recently introduced and studied in convex optimization. Here we extend this motion in two directions: to non-smooth convex and smooth generalized convex functions. An important feature of these functions is that the Karush-Kuhn-Tucker condition is both necessary and sufficient for optimality. Then we use the properties of linear LFS functions and basic point-to-set topology to study the "inverse" programming problem. In this problem, a feasible, but nonoptimal, point is made optimal by stable perturbations of the parameters. The results are applied to a case study in optimal production planning.
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Trujillo-Cortez, Refugio. "LFS functions in stable bilevel programming." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ37171.pdf.

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Ahluwalia, Manu. "Co-evolving functions in genetic programming." Thesis, University of the West of England, Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322427.

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Stark, Ian David Bede. "Names and higher-order functions." Thesis, University of Cambridge, 1994. https://www.repository.cam.ac.uk/handle/1810/251879.

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Many functional programming languages rely on the elimination of 'impure' features: assignment to variables, exceptions and even input/output. But some of these are genuinely useful, and it is of real interest to establish how they can be reintroducted in a controlled way. This dissertation looks in detail at one example of this: the addition to a functional language of dynamically generated names. Names are created fresh, they can be compared with each other and passed around, but that is all. As a very basic example of state, they capture the graduation between private and public, local and global, by their interaction with higher-order functions. The vehicle for this study is the nu-calculus, an extension of the simply-typed lambdacalculus. The nu-calculus is equivalent to a certain fragment of Standard ML, omitting side-effects, exceptions, datatypes and recursion. Even without all these features, the interaction of name creation with higher-order functions can be complex and subtle. Various operational and denotational methods for reasoning about the nu-calculus are developed. These include a computational metalanguage in the style of Moggi, which distinguishes in the type system between values and computations. This leads to categorical models that use a strong monad, and examples are devised based on functor categories. The idea of logical relations is used to derive powerful reasoning methods that capture some of the distinction between private and public names. These techniques are shown to be complete for establishing contextual equivalence between first-order expressions; they are also used to construct a correspondingly abstract categorical model. All the work with the nu-calculus extends cleanly to Reduced ML, a larger language that introduces integer references: mutable storage cells that are dynamically allocated. It turns out that the step up is quite simple, and both the computational metalanguage and the sample categorical models can be reused.
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Shapiro, David. "Compiling Evaluable Functions in the Godel Programming Language." PDXScholar, 1996. https://pdxscholar.library.pdx.edu/open_access_etds/5101.

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We present an extension of the Godel logic programming language code generator which compiles user-defined functions. These functions may be used as arguments in predicate or goal clauses. They are defined in extended Godel as rewrite rules. A translation scheme is introduced to convert function definitions into predicate clauses for compilation. This translation scheme and the compilation of functional arguments both employ leftmost-innermost narrowing. As function declarations are indistinguishable from constructor declarations, a function detection method is implemented. The ultimate goal of this research is the implementation of extended Godel using needed narrowing. The work presented here is an intermediate step in creating a functional-logic language which expands the expressiveness of logic programming and streamlines its execution.
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Edwards, Teresa Dawn. "The box method for minimizing strictly convex functions over convex sets." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/30690.

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Chen, Jein-Shan. "Merit functions and nonsmooth functions for the second-order cone complementarity problem /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5782.

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Ferris, Michael Charles. "Weak sharp minima and penalty functions in mathematical programming." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292969.

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Schanzer, Emmanuel Tanenbaum. "Algebraic Functions, Computer Programming, and the Challenge of Transfer." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:16461037.

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Students' struggles with algebra are well documented. Prior to the introduction of functions, mathematics is typically focused on applying a set of arithmetic operations to compute an answer. The introduction of functions, however, marks the point at which mathematics begins to focus on building up abstractions as a way to solve complex problems. A common refrain about word problems is that “the equations are easy to solve - the hard part is setting them up!” A student of algebra is asked to identify functional relationships in the world around them - to set up the equations that describe a system- and to reason about these relationships. Functions, in essence, mark the shift from computing answers to solving problems. Researchers have called for this shift to accompany a change in pedagogy, and have looked to computer programming and game design as a means to combine mathematical rigor with creative inquiry. Many studies have explored the impact of teaching students to program, with the goal of having them transfer what they have learned back into traditional mathematics. While some of these studies have shown positive outcomes for concepts like geometry and fractions, transfer between programming and algebra has remained elusive. The literature identifies a number of conditions that must be met to facilitate transfer, including careful attention to content, software, and pedagogy. This dissertation is a feasibility study of Bootstrap, a curricular intervention based on best practices from the transfer and math-education literature. Bootstrap teaches students to build a video game by applying algebraic concepts and a problem solving technique in the programming domain, with the goal of transferring what they learn back into traditional algebra tasks. The study employed a mixed-methods analysis of six Bootstrap classes taught by math and computer science teachers, pairing pre- and post-tests with classroom observations and teacher interviews. Despite the use of a CS-derived problem solving technique, a programming language and a series of programming challenges, students were able to transfer what they learned into traditional algebra tasks and math teachers were found to be more successful at facilitating this transfer than their CS counterparts.<br>Education Policy, Leadership, and Instructional Practice
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Bücher zum Thema "Programming functions"

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Burstall, R. M. Inductively defined functions in functional programming languages. University of Edinburgh, Laboratory for Foundations of Computer Science, 1987.

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Li, Lan. Studying functions and limits through programming. typescript, 1992.

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Henglein, Fritz. Programming with structures, functions, and objects. Courant Institute of Mathematical Sciences, New York University, 1991.

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Davis, Alan M. Software requirements: Objects, functions and states. Prentice-Hall International, 1993.

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Common C functions. Que Corp., 1985.

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Reddy, Uday Sankara. Logic languages based on functions: Semantics and implementation. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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Kibzun, A. I. Stochastic programming problems with probability and quantile functions. Wiley, 1996.

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Johnson, Marcus. PC programmer's guide to low-level functions and interrupts. Sams Pub., 1994.

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Fitting, Melvin. Computability theory, semantics, and logic programming. Oxford University Press, 1987.

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Semenovich, Nemirovskiĭ Arkadiĭ, ed. Self-concordant functions and polynomial-time methods in convex programming. USSR Academy of Sciences, Central Economic & Mathematic Institute, 1989.

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Buchteile zum Thema "Programming functions"

1

Jungck, Peder, Ralph Duncan, and Dwight Mulcahy. "Functions." In packetC Programming. Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-4159-1_7.

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Sharma, Vijay Kumar, Vimal Kumar, Swati Sharma, and Shashwat Pathak. "Functions." In Python Programming. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003185505-7.

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Rolland, F. D. "Functions." In Programming with VDM. Macmillan Education UK, 1992. http://dx.doi.org/10.1007/978-1-349-12692-7_3.

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Rothwell, William “Bo.” "Functions." In Beginning Perl Programming. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5055-6_12.

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Upadhyaya, Bhim P. "Functions." In Programming with Scala. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69368-2_9.

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Ettinger, Jean. "Functions." In Programming in C++. Macmillan Education UK, 1994. http://dx.doi.org/10.1007/978-1-349-23304-5_5.

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Nagar, Sandeep. "Functions." In Beginning Julia Programming. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3171-5_10.

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Van Hoey, Jo. "Functions." In Beginning x64 Assembly Programming. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5076-1_12.

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Gowrishankar, S., and A. Veena. "Functions." In Introduction to Python Programming. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781351013239-4.

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Klein Haneveld, Willem K., Maarten H. van der Vlerk, and Ward Romeijnders. "Random Objective Functions." In Stochastic Programming. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29219-5_2.

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Konferenzberichte zum Thema "Programming functions"

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Antoy, Sergio, and Michael Hanus. "Set functions for functional logic programming." In the 11th ACM SIGPLAN conference. ACM Press, 2009. http://dx.doi.org/10.1145/1599410.1599420.

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Odersky, Martin. "Programming with variable functions." In the third ACM SIGPLAN international conference. ACM Press, 1998. http://dx.doi.org/10.1145/289423.289433.

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Anwer, Bilal, Theophilus Benson, Nick Feamster, and Dave Levin. "Programming slick network functions." In SOSR 2015: ACM SIGCOMM Symposium on SDN Research. ACM, 2015. http://dx.doi.org/10.1145/2774993.2774998.

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Stuart, David A. "Scripted signal functions." In ICFP '20: ACM SIGPLAN International Conference on Functional Programming. ACM, 2020. http://dx.doi.org/10.1145/3406088.3409016.

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Eichholz, Matthias, Guido Salvaneschi, and Mira Mezini. "Towards safe modular composition of network functions." In 2018: 2nd International Conference on the Art, Science, and Engineering of Programming 2018. ACM, 2018. http://dx.doi.org/10.1145/3191697.3213804.

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Schmidt, Michael Douglas, and Hod Lipson. "Solving iterated functions using genetic programming." In the 11th annual conference companion. ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570292.

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Abadi, Martín. "TensorFlow: learning functions at scale." In ICFP'16: ACM SIGPLAN International Conference on Functional Programming. ACM, 2016. http://dx.doi.org/10.1145/2951913.2976746.

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Kneuss, Etienne, Ivan Kuraj, Viktor Kuncak, and Philippe Suter. "Synthesis modulo recursive functions." In SPLASH '13: Conference on Systems, Programming, and Applications: Software for Humanity. ACM, 2013. http://dx.doi.org/10.1145/2509136.2509555.

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Glynn, Peter. "Linear Programming, Lyapunov Functions, and Performance Analysis." In 2008 Fifth International Conference on Quantitative Evaluation of Systems. IEEE, 2008. http://dx.doi.org/10.1109/qest.2008.50.

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Pelov, Nikolay, and Maurice Bruynooghe. "Extending constraint logic programming with open functions." In the 2nd ACM SIGPLAN international conference. ACM Press, 2000. http://dx.doi.org/10.1145/351268.351295.

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Berichte der Organisationen zum Thema "Programming functions"

1

Royset, J. O. Optimality Functions in Stochastic Programming. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada513135.

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Shapiro, David. Compiling Evaluable Functions in the Godel Programming Language. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6977.

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McCormick, Garth P., and Christoph Witzgall. On weakly analytic and faithfully convex functions in convex programming. National Institute of Standards and Technology, 2000. http://dx.doi.org/10.6028/nist.ir.6426.

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Vorvick, Janet. Evaluable Functions in the Godel Programming Language: Parsing and Representing Rewrite Rules. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7071.

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Anderson, Loren James, and Marion Kei Davis. Functional Programming in Computer Science. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1237221.

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Talcott, Carolyn. Programming and Proving with Function and Control Abstractions,. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada324006.

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Voellmy, Andreas, Ashish Agarwal, and Paul Hudak. Nettle: Functional Reactive Programming for OpenFlow Networks. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada555162.

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Boggs, P. T., J. W. Tolle, and A. J. Kearsley. A merit function for inequality constrained nonlinear programming problems. National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.4702.

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Greer, Earl D. Joint Staff Organization: Is there a Planning and Programming Function Split. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada208040.

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Jin, Dafeng, Yishi Ouyang, Yugong Luo, Keqiang Li, and Chuanyou Wu. Investigations on Both the Optimal Control of a PHEV Power Assignment and Its Cost Function of the Dynamic Programming. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0406.

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