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1Mathematical Programming For Business And Industry

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2Mathematical Programming For Management And Business

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3Mathematical Programming 1994: Vol 66 Index

Mathematical Programming 1994: Volume 66 , Issue Index. Digitized from IA1652525-04 . Previous issue: sim_mathematical-programming_1994-07-20_65_3 . Next issue: sim_mathematical-programming_1994-08-02_66_1 .

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4DTIC ADA1004570: NETGEN-II: A System For Generating Structured Network-Based Mathematical Programming Test Problems,

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The increased importance of designing and implementing algorithms to solve particular management problems has created the need for more robust test problem generators that can match the overall structure and parameter values of these problems. Of particular interest are management problems that can be modeled using a network structure. This paper discussed the design of a system for generating network-based mathematical programming test problems that conform to user-supplied structural & parameter characteristics. (Author)

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5DTIC ADA090809: Personnel Allocation Using A Mathematical Programming Model,

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The assignment selection criteria used to reassign AF personnel is examined. A mathematical programming model is presented to allocate the personnel resource. The model uses a linear programming algorithm to perform the allocation process. The changeability and adaptability of the procedure is discussed. (Author)

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6DTIC ADA141169: An Optimization Methodology For Multi-Criteria Comparison Using Response Surface Methods And Mathematical Programming.

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The maxima/minima of one response surface (measure of effectiveness, MOE) is calculated when the domain is constrained by consecutive levels (aspirations) of a second response surface. Thus, the optimal trade-off between the two responses (MOE's) is displayed over an entire operating domain, providing new and unique capabilities to evaluate options. The study applies this development to a strategic force structure modeled in a linear programming model. The methodology determines the optimal force structure to maximize one MOE given the aspiration levels of the other MOE. Therefore, the decision maker has the optimal force structures to achieve a range of demands/aspirations and also maximize the second goal. The methodology has applications to multicriteria optimization when relatively large numbers of domain control variable combinations are involved. This technique may be a tool to design the optimal weapon system characteristics or choose the optimal mix of weapon types to fulfill dual mission requirements. This work is an extension of previous work which used response surfaces, experimental design, and mathematical programming to conduct multi-dimensional, sensitivity analysis and weapon system comparisons. (Author)

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7BMP 2014 - Mathematical Programming

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TRI 1 2016/2017

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8Mathematical Programming 1998: Vol 83 Index

Mathematical Programming 1998: Volume 83 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1998-08-01_82_3_0 . Next issue: sim_mathematical-programming_1998-09-01_83_1_0 .

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9DTIC ADA382583: Machine Learning Via Mathematical Programming

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Mathematical programming approaches were applied to a variety of problems in machine learning in order to gain deeper understanding of the problems and to come up with new and more efficient computational algorithms. Theoretical and/or computational contributions were made to Data Envelopment Analysis wherein one seeks efficient decision making units, Neural Networks with as few hidden units as possible, optimization problems subject to constraints that in turn require the solution of further optimization problems, classification algorithms that suppress unnecessary or redundant features, algorithms that chunk massive data sets in order to classify them, clustering data based on the novel concept of nearness to cluster planes rather than cluster centroids, a new implementable general theory for Support Vector Machines that does away with the restrictive Mercer positive definite kernel condition that had hitherto been universally assumed, a very effective Successive Over Relaxation (SOR) algorithm for solving very large linear and nonlinear kernel classification problems, applying support vector machines to breast cancer diagnosis and prognosis, smoothing algorithms for solving large and complex classification problems, nonlinear data fitting using support vector machines and a robust loss function, and classifying data that is partly labeled and partly unlabeled.

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10Mathematical Programming 1994: Vol 66 Index

Mathematical Programming 1994: Volume 66 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1994-07-20_65_3_0 . Next issue: sim_mathematical-programming_1994-08-02_66_1_0 .

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11DTIC ADA318846: Twelfth Conference On The Mathematical Foundations Of Programming Semantics,

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Funds under this grant were used to support three principal activities. The first was the Twelfth Workshop on the Mathematical Foundations of Programming Semantics, which took place on the campus of the University of Colorado, Boulder from June 3 to June 5, 1996. The second was a series of visits by researchers to Tulane University during the two-mouth period June - July, 1996, to collaborate with the Principal Investigator on topics of common research interest. These visits formed the bulk of the Workshop on Concurrency listed in the title of the grant. Lastly, funds were used to help support participants in a small workshop in New Orleans from September 15 to September 17,1996 which focused on applications of semantic techniques to problems in security of distributed computing systems.

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12DTIC ADA089733: Mathematical Semantics For Higher Order Programming Languages.

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Complete operational and mathematical semantics are presented for a higher order applicative algorithmic language (BAL). Both semantics involve partially ordered domains closed under limits of convergent sequences. Procedure calls are formalized via lambda calculus reductions, or copy rule. Evaluations involve a more general form of computability described as nondeterministic computability. The mathematical semantics is obtained via embeddings in reflexive domains. Both semantics are proved to be equivalent, and several applications are given. The adequacy of the copy rule is proved in standard situations where computability is deterministic. Several examples are presented. (Author)

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13DTIC ADA076150: Mathematical Analysis And Programming As Applied To Electromagnetic Propagation And Antenna Design.

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In this report, the analysis and programming for various problems in the field of Electromagnetic propagation and antenna design are considered. The scope of our work extended into a variety of technical areas. Among the technical areas are: Magneto-acoustic surface waves, Signal analysis, Polar VLF propagation, Ionospheric pulse reflections, Detection and tracking of targets, and Antenna Design. (Author)

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14Computer Science Number System Conversion And Mathematical Operation Behind Programming

Computer Science - Number System Conversion and mathematical operation behind Programming

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15DTIC ADA121740: Lipschitz Properties Of Solution In Mathematical Programming.

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In this paper, we are concerned with the behaviour of the solutions of problems and also with the Lipschitzian dependence of the set of solutions with respect to the parameter alpha. The paper is organized as follows. The main theorems of the paper are stated. Sufficient conditions are give to have (1) local uniqueness of solutions and (2) Lipschitzian dependence of the solution with respect to the parameter alpha. The proofs of the theorems are given. The main tool, in this analysis, is an implicit function theorem for Lipschitzian mappings due to Clarke (see also Hiriart-Urruty). The main idea is to write the generalized equation as a system of Lipschitzian mapping, to which Clarke's implicit function can be applied. The notion of generalized derivative of the projection mapping on a convex are of particular use.

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16Mathematical Programming : Proceedings Of The International Congress On Mathematical Programming, Rio De Janeiro, Brazil, 6-8 April, 1981

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In this paper, we are concerned with the behaviour of the solutions of problems and also with the Lipschitzian dependence of the set of solutions with respect to the parameter alpha. The paper is organized as follows. The main theorems of the paper are stated. Sufficient conditions are give to have (1) local uniqueness of solutions and (2) Lipschitzian dependence of the solution with respect to the parameter alpha. The proofs of the theorems are given. The main tool, in this analysis, is an implicit function theorem for Lipschitzian mappings due to Clarke (see also Hiriart-Urruty). The main idea is to write the generalized equation as a system of Lipschitzian mapping, to which Clarke's implicit function can be applied. The notion of generalized derivative of the projection mapping on a convex are of particular use.

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17A Weighted Least Squares Approch To Direction Finding In Mathematical Programming

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18Domain-Specific Languages Of Mathematics: Presenting Mathematical Analysis Using Functional Programming

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We present the approach underlying a course on "Domain-Specific Languages of Mathematics", currently being developed at Chalmers in response to difficulties faced by third-year students in learning and applying classical mathematics (mainly real and complex analysis). The main idea is to encourage the students to approach mathematical domains from a functional programming perspective: to identify the main functions and types involved and, when necessary, to introduce new abstractions; to give calculational proofs; to pay attention to the syntax of the mathematical expressions; and, finally, to organise the resulting functions and types in domain-specific languages.

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19Methods For Computing The Greatest Common Divisor And Applications In Mathematical Programming.

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We present the approach underlying a course on "Domain-Specific Languages of Mathematics", currently being developed at Chalmers in response to difficulties faced by third-year students in learning and applying classical mathematics (mainly real and complex analysis). The main idea is to encourage the students to approach mathematical domains from a functional programming perspective: to identify the main functions and types involved and, when necessary, to introduce new abstractions; to give calculational proofs; to pay attention to the syntax of the mathematical expressions; and, finally, to organise the resulting functions and types in domain-specific languages.

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20Mathematical Programming; An Introduction To The Design And Application Of Optimal Decision Machines. --

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xi, 496 p. : 23 cm. --

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21Mathematical Programming And The Analysis Of Capital Budgeting Problems

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xi, 496 p. : 23 cm. --

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22BSTJ 59: 4. April 1980: The Application Of Mathematical Programming To Loop Feeder Allocation. (Elken, T.R.)

Bell System Technical Journal, 59: 4. April 1980 pp 479-500. The Application of Mathematical Programming to Loop Feeder Allocation. (Elken, T.R.)

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23Mathematical Programming 1998: Vol 82 Index

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24Theory And Application Of Mathematical Programming

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Mathematical Programming 1998: Volume 82 , Issue Index. Digitized from IA1652525-04 . Previous issue: sim_mathematical-programming_1998-05-01_81_3 . Next issue: sim_mathematical-programming_1998-06-01_82_1-2 .

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25Mathematical Programming 1994: Vol 67 Index

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26Mathematical Programming 1992: Vol 56 Index

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27Mathematical Programming 1992: Vol 57 Index

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28Mathematical Programming : State Of The Art 1994

Mathematical Programming 1992: Volume 57 , Issue Index. Digitized from IA1653230-05 . Previous issue: sim_mathematical-programming_1992-10-05_56_3_0 . Next issue: sim_mathematical-programming_1992-05-04_57_1_0 .

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29BMP 2014 - Mathematical Programming

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Tri 1 2019/2020

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30Introduction To Mathematical Programming

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31Introduction To Mathematical Programming

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32Introduction To Mathematical Programming : Applications And Algorithms

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33Department Of University Extension, Extension Course Sheets, Mathematics: An Introduction To Matrices And Mathematical Programming, Beginning October 8

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34Stochastic Versus Fuzzy Approaches To Multiobjective Mathematical Programming Under Uncertainty

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35Mathematical Programming Methods For Geographers And Planners

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36Mathematical Programming And The Aircraft Squadron.

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37Mathematical Foundations Of Programming Language Semantics : 3rd Workshop, Tulane University, New Orleans, Louisiana, USA, April 8-10, 1987 : Proceedings

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38Two Approaches To Modeling The Interaction Of Small And Medium Price-taking Traders With A Stock Exchange By Mathematical Programming Techniques

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The paper presents two new approaches to modeling the interaction of small and medium pricetaking traders with a stock exchange. In the framework of these approaches, the traders can form and manage their portfolios of financial instruments traded on a stock exchange with the use of linear, integer, and mixed programming techniques. Unlike previous authors publications on the subject, besides standard securities, the present publication considers derivative financial instruments such as futures and options contracts.

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39DTIC ADA214706: Mathematical Programming With And Without Differentiability

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Contents: Quasi-Newton Algorithms for Constrained Nonlinear Programming; Generic Optimality Conditions and Nondifferentiable Optimization. Keywords: Quadratic approximation methods, Quadratic programming, Lagrangian multipliers.

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40DTIC ADA186153: The Application Of Mathematical Programming To The Productivity Investment Fund Program: A Capital Rationing Problem.

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This study explores the potential applications of mathematical programming (MP) to a capital rationing problem of the Department of Defense. It demonstrates that the current method of selecting projects for funding results in a suboptimal economic mix of capital investment projects. Based on data from the Fiscal Year 1985 Productivity Investment Fund (PIF) program, substantial dollar savings are likely if PIF projects are selected using MP instead of ranking. Using a single-criterion MP model, the opportunity cost of ranking (defined as the difference between the net present value (NPV) the mix found by MP and the NPV of the mix actually funded by the DoD) was $205.6 million. The economic superiority of the MP-selected mix was demonstrated over broad ranges of budget ceilings and discount rates. The average opportunity cost of ranking ranged from $23 million to $242 million, depending on the ranking criterion or method used. Two multiple-criteria MP models were also developed and tested using the FY85 PIF data. The mixes found by these models were economically superior to those found by ranking, when pre-specified objectives (involving minimum levels of return on investment and labor savings) were set for the solution mix.

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41Model Building In Mathematical Programming

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xv, 349 p

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42Introduction To Mathematical Programming. Operations Research. Volume 1

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xv, 349 p

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43Progress In Mathematical Programming : Interior-point And Related Methods

xv, 349 p

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44Advances In Mathematical Programming And Financial Planning. Vol. 6

260p

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45Applications Of Mathematical Programming In The Development Of Some Models

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Book Source: Digital Library of India Item 2015.270391 dc.contributor.author: Poonam dc.date.accessioned: 2015-07-27T20:42:51Z dc.date.available: 2015-07-27T20:42:51Z dc.identifier.barcode: 05990010890378 dc.identifier.origpath: /data58/upload/0092/095 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/270391 dc.description.scannerno: 20003547 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 147 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: Bundelkhand Uni Jhansi dc.source.library: Bundelkhand Uni Jhansi dc.subject.classification: Philosophy dc.title: Applications Of Mathematical Programming In The Development Of Some Models dc.type: ptiff dc.type: pdf

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46Mathematical Programming 1990 - 1991: Vol 49 Index

Mathematical Programming 1990 - 1991: Volume 49 , Issue Index. Digitized from IA1652525-04 . Previous issue: sim_mathematical-programming_1986-1989_34-45_cumulative-index . Next issue: sim_mathematical-programming_1990-11_49_1 .

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47Mathematical Programming 1988: Vol 42 Index

Mathematical Programming 1988: Volume 42 , Issue Index. Digitized from IA1652525-04 . Previous issue: sim_mathematical-programming_1988-04_40_3 . Next issue: sim_mathematical-programming_1988-04_42_1 .

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48Mathematical Programming 1995: Vol 69 Index

Mathematical Programming 1995: Volume 69 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1995-03-02_68_3_0 . Next issue: sim_mathematical-programming_1995-07-25_69_1_0 .

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49Mathematical Programming 1986 - 1989: Vol 34-45 Index

Mathematical Programming 1986 - 1989: Volume 34-45 , Issue Cumulative Index. Digitized from IA1653230-05 . Previous issue: sim_mathematical-programming_1990-10_48_3_0 . Next issue: sim_mathematical-programming_1990-1991_49_index_0 .

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50Mathematical Programming: An Aid To Executive Decisions.

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