Downloads & Free Reading Options - Results

Mathematical Programming 1989: Volume 44 , Issue Index. Digitized from IA1653230-05 . Previous issue: sim_mathematical-programming_1989-04_43_3_0 . Next issue: sim_mathematical-programming_1989-05_44_1_0 .

http://uf.catalog.fcla.edu/uf.jsp?st=UF024909362&ix=pm&I=0&V=D&pm=1

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)

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)

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)

Mathematical Programming 1992: Volume 61 , Issue Index. Digitized from IA1653230-05 . Previous issue: sim_mathematical-programming_1992-12-07_57_3_0 . Next issue: sim_mathematical-programming_1993-08-11_61_1_0 .

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.

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.

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.

Mathematical Programming 1994: Volume 65 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1994-05-11_64_3_0 . Next issue: sim_mathematical-programming_1994-05-31_65_1_0 .

Mathematical Programming 1997: Volume 77 , Issue Index. Digitized from IA1652525-04 . Previous issue: sim_mathematical-programming_1997-03-01_76_3 . Next issue: sim_mathematical-programming_1997-04-01_77_1 .

Computer Science - Number System Conversion and mathematical operation behind Programming

An attempt to develop general methods for measuring cost and effectiveness implications of adding automatic data processing to command control systems for ground combat required the development of techniques for marginal-effectiveness analysis. One necessary step for such analysis was the formulation of ground combat missions to permit measurement of marginal mission performance. Examination of typical combat missions identifies three dimensions: resources, time, and area controlled by a military force. Typical missions are related to a closed continuum of tactical postures ordered on the basis of relative potential energy and movement rate. Three classes of increasingly severe constraints are identified and associated with decreasing potential energy. Two objective functions are identified: maximization of rate of advance for high-energy postures and minimization of rate of resource expenditure for low-energy postures. The quantifiability of the three dimensions of mission space is examined, and difficulties in aggregating different classes of resources and terrain of varying tactical value are recognized. A measure for assessing the degree of control over an area by a military force is postulated and tested in a simple mathematical model. Relating the performance of a mix of military missions to combat effectiveness is discussed.

A general mapping w of points z in RN into points w(z) in the same space is considered. It is shown that classical mathematical programming problems can all be restate as finding among those vectors z greater than or equal to 0 which map into w greater than or equal to 0, one which minimizes zTw. For a differentiable mapping w with a positive semi-definite Jacobian matrix the obviously sufficient (''complementary slackness'') condition zTw 0 for a minimum is shown to be necessary. Next considered is the case which in cludes quadratic and linear programming: w Mz q where q is a fixed vector and M is positive (semi-) definite with constant coefficients. It is noted that the definiteness property is pre served for any equivalent system generated by an interchange of a subset of corresponding comple ments of z and w or what is the same thing by a ''block'' pivot on a principal minor of M. Since this result is related to recent ones obtained by A. Tucker and P. Wolfe, it is planned to in corporate its proof in a separate joint paper. Finally, a simple constructive solution of quad ratic and linear programs is presented. In a sense the simplex method for linear programs and the results of Barankin, Dorfman, Wolfe, Beale, and Markowitz on quadratic programs reappear here but are curiously free of primal-dual structure. Complementary pairs of variables play a key role.

A general mapping w of points z in RN into points w(z) in the same space is considered. It is shown that classical mathematical programming problems can all be restate as finding among those vectors z greater than or equal to 0 which map into w greater than or equal to 0, one which minimizes zTw. For a differentiable mapping w with a positive semi-definite Jacobian matrix the obviously sufficient (''complementary slackness'') condition zTw 0 for a minimum is shown to be necessary. Next considered is the case which in cludes quadratic and linear programming: w Mz q where q is a fixed vector and M is positive (semi-) definite with constant coefficients. It is noted that the definiteness property is pre served for any equivalent system generated by an interchange of a subset of corresponding comple ments of z and w or what is the same thing by a ''block'' pivot on a principal minor of M. Since this result is related to recent ones obtained by A. Tucker and P. Wolfe, it is planned to in corporate its proof in a separate joint paper. Finally, a simple constructive solution of quad ratic and linear programs is presented. In a sense the simplex method for linear programs and the results of Barankin, Dorfman, Wolfe, Beale, and Markowitz on quadratic programs reappear here but are curiously free of primal-dual structure. Complementary pairs of variables play a key role.

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

Mathematical Programming 1994: Volume 64 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1994-02-25_63_3_0 . Next issue: sim_mathematical-programming_1994-03-23_64_1_0 .

Mathematical Programming 1994: Volume 64 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1994-02-25_63_3_0 . Next issue: sim_mathematical-programming_1994-03-23_64_1_0 .

Click here to view the University of Florida catalog record

Binary optimization is one of the central problems in mathematical optimization and it captures an abundance of applications. To solve this problem, we propose a new class of continuous optimization techniques which is based on Mathematical Programming with Equilibrium Constraints (MPECs). We first reformulate the binary program as an equivalent augmented biconvex optimization problem with a bilinear equality constraint, then we propose two penalization/regularization methods (exact penalty and alternating direction) to solve it. The resulting algorithms seek desirable solutions to the original problem via solving a sequence of linear programming convex relaxation subproblems. In addition, we prove that both the penalty function and augmented Lagrangian function, induced by adding the complementarity constraint to the objectives, are exact, i.e., they have the same local and global minima with those of the original binary program when the penalty parameter is over some threshold. The convergence of both algorithms can be guaranteed since they essentially reduce to block coordinate descent in the literature. Finally, we demonstrate the effectiveness and versatility of our methods on several important problems, including graph bisection, constrained image segmentation, dense subgraph discovery, modularity clustering and Markov random fields. Extensive experiments show that our methods outperform existing popular techniques, such as iterative hard thresholding, linear programming relaxation and semidefinite programming relaxation.

Document number CIA-RDP80T00246A011500230001-8 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP80T00246A011500230001-8 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Trimester 2 2015/2016

xi, 496 p. : 23 cm. --

xi, 496 p. : 23 cm. --

xi, 496 p. : 23 cm. --

Notes made by Ray Thelwell on Mathematical Programming.

Notes made by Ray Thelwell on Mathematical Programming.

Notes made by Ray Thelwell on Mathematical Programming.

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 .

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 .

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 .

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 .

Mathematical Programming 1990 - 1991: Volume 49 , Issue Index. Digitized from IA1653230-05 . Previous issue: sim_mathematical-programming_1986-1989_34-45_cumulative-index_0 . Next issue: sim_mathematical-programming_1990-11_49_1_0 .

Mathematical Programming 1996: Volume 73 , Issue Index. Digitized from IA1652525-04 . Previous issue: sim_mathematical-programming_1996-03-31_72_3 . Next issue: sim_mathematical-programming_1996-04-30_73_1 .

Mathematical Programming 1997: Volume 79 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1997-09-01_78_3_0 . Next issue: sim_mathematical-programming_1997_79_contents_0 .

Mathematical Programming 1997: Volume 79 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1997-09-01_78_3_0 . Next issue: sim_mathematical-programming_1997_79_contents_0 .

Mathematical Programming is an active publication covering mathematical programming. It was established in 1971 and is published 9 times a year by Springer. The publication aims to advance knowledge in this area by publishing original articles that deal with every aspect of mathematical programming. This includes topics related to the problem of optimizing a function with multiple variables, whether through direct or indirect approaches. It welcomes submissions from academics and researchers working in various disciplines, such as mathematics, computer science, economics, and engineering. Articles cover a wide range of subjects, including linear programming, nonlinear programming, mixed-integer programming, stochastic programming, and combinatorial optimization. Mathematical Programming can be found online at: https://www.springer.com/journal/10107

Mathematical Programming 1992: Volume 56 , Issue Index. Digitized from IA1653230-05 . Previous issue: sim_mathematical-programming_1992-07-06_55_3_0 . Next issue: sim_mathematical-programming_1992-08-03_56_1_0 .

The economic, technical and strategic factors are the three most important factors in examining the cropping patterns in Iran. Iran is geographically located in a part of the planet with specific climate constraints. Drought is one of the constraints that has been a major challenge to agricultural development for many years and has always been the subject of discussions and investigations. On the other hand, constraints such as agricultural soils, economic factors, climate change, agricultural workforce, etc., multiply the production challenges in the country. Despite such constraints, planning a coherent and targeted program for the cultivation of crops and overcome the existing problems is inevitable. The present study introduced a model for optimization of regional cropping pattern decisions, which is one of the subsets of the Multi-Objective Structural Planning (MOSP) approach, and addressed different objectives, such as economic, social and environmental objectives, separately and jointly. However, it is important to address the exchange of crops in different areas in order to achieve the fundamental objectives of determining the optimal cropping pattern. Therefore, in the proposed model of optimal regional cropping pattern, issues such as the transportation of crops and, consequently, virtual water and energy exchanges were also considered. In order to evaluate the proposed model, agricultural arable lands located in the political-geographic divisions of 23 counties of Isfahan province (Iran) were selected for examination. The results showed that in the main groups of grains and forage, a significant reduction was observed in the optimal crop area of the multi-objective model by 26% and 5%, respectively. Increasing the crop area of horticultural products by 10% in the optimal pattern of multi-objective model was another important factor in the analysis of the results. In general, in order to achieve the economic, social and environmental objectives mentioned in this study within the framework of a multi-objective planning, a 16% reduction in the level of the crop area in Isfahan province is inevitable. The results of this measure are reduction in the irrigation water consumption by 17%, increase in the profit by 58% and increase in the production by 11%. Regarding the fact that in the structural planning of cropping pattern, different and sometimes conflicting objectives are considered and the compromise between the objectives is possible in the multi-objective structural planning model, the decision makers are recommended to use this model.

Mathematical Programming 1998: Volume 80 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1997-10-01_79_1-3_0 . Next issue: sim_mathematical-programming_1998-01-01_80_1_0 .

Mathematical Programming 1998: Volume 80 , Issue Index. Digitized from IA1653230-03 . Previous issue: sim_mathematical-programming_1997-10-01_79_1-3_0 . Next issue: sim_mathematical-programming_1998-01-01_80_1_0 .

Mathematical Programming Study was a peer-reviewed academic covering mathematical programming and optimization theory. It was established in 1971 and was published 3 times a year by Elsevier until 1987. The journal serves as a platform for mathematicians, researchers, and practitioners to publish original research articles, survey papers, and technical notes in the field of mathematical programming and its applications covering a wide range of topics, including linear programming, integer programming, nonlinear programming, combinatorial optimization, stochastic programming, and multi-objective optimization. The journal features articles that present new mathematical models, algorithms, and solution methodologies for optimization problems in diverse domains, including operations research, engineering, finance, logistics, and data science. The Internet Archive Collection contains microfilm published between 1974 and 1987 The ISSN is: 0303-3929

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.

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.

Trimester 1 2018/2019

Mathematical Programming 1992: Volume 53 , Issue Index. Digitized from IA1653230-05 . Previous issue: sim_mathematical-programming_1991-12_52_3_0 . Next issue: sim_mathematical-programming_1992-01-06_53_1_0 .

Mathematical Programming 1992: Volume 53 , Issue Index. Digitized from IA1653230-05 . Previous issue: sim_mathematical-programming_1991-12_52_3_0 . Next issue: sim_mathematical-programming_1992-01-06_53_1_0 .