The efficiency of the delivery of neonatal care in the UK. (1/65)
BACKGROUND: A recent paper in Journal of Public Health Medicine (O'Neill et al., 2000; 22(1): 108-115) used regression modelling to determine the average costs of neonatal care services for a sample of 49 units in the United Kingdom in 1990-1991, and concluded that economies of scale were present in the sample as a whole. Although this form of modelling is useful, analysis of the efficiency of production for individual units is also important. METHODS: Data envelopment analysis (DEA) was used to analyse the data set published by O'Neil et al., to determine technical efficiency of neonatal units, measuring efficiency compared with a benchmark efficient frontier, and estimating economies of scale for each unit. Potential cost savings if units were to operate efficiently are estimated. RESULTS: There is evidence of substantial levels of technical inefficiency. Economies of scale varied between units, with increasing returns in the 36 inefficient units, and mainly constant returns in the 13 efficient units. This suggests that the presence of technical inefficiency was as important as scale inefficiencies. Total cost savings, if all units were operating efficiently, are estimated at l10.4 million, equivalent to 10 extra units producing 57,000 additional days of care. CONCLUSIONS: DEA is a technique of great potential value in analysing the efficiency of health care production. As well as inefficiencies in the production of neonatal care in the United Kingdom due to differences in the scale of production, there appears to have been considerable technical inefficiency, which was not due to differences in case mix. The potential cost savings from efficiency gains are large. (+info)Linear and nonlinear programming to optimize the nutrient density of a population's diet: an example based on diets of preschool children in rural Malawi. (2/65)
BACKGROUND: Food consumption surveys are often used to detect inadequate nutrient intakes but not to determine whether inadequate nutrient intakes are due to suboptimal use of locally available foods or to insufficient availability of nutrient-dense foods. OBJECTIVES: The objectives were to describe the use of linear programming as a method to design nutrient-adequate diets of optimal nutrient density and to identify the most stringent constraints in nutritional recommendations and food consumption patterns in a population's diet. DESIGN: This analysis was conducted with the use of food consumption data collected during 2 seasons from rural Malawian children aged 3-6 y. Linear programming was used to select diets based on local foods that satisfied a set of nutritional constraints while minimizing the total energy content of the diet. Additional constraints on daily intakes of foods and food groups were also introduced to ensure that the diets were compatible with local food patterns. The strength of the constraints was assessed by analyzing nonlinear programming sensitivity. RESULTS: In the harvest season, it was possible to satisfy nutritional recommendations with little departure from the local diet. In the nonharvest season, nutritional adequacy was impaired by the low availability of riboflavin- and zinc-rich animal or vegetable foods and by the high phytate content of other foods. CONCLUSIONS: This analysis suggests that nutrition education may help improve the diets of children in the harvest season, whereas changes in the range of available foods might be needed in the nonharvest season. Linear and nonlinear programming can be used to formulate recommendations with the use of data from local food consumption surveys. (+info)Use of linear programming to estimate impact of changes in a hospital's operating room time allocation on perioperative variable costs. (3/65)
BACKGROUND: Administrators at hospitals with a fixed annual budget may want to focus surgical services on priority areas to ensure its community receives the best health services possible. However, many hospitals lack the detailed managerial accounting data needed to ensure that such a change does not increase operating costs. The authors used a detailed hospital cost database to investigate by how much a change in allocations of operating room (OR) time among surgeons can increase perioperative variable costs. METHODS: The authors obtained financial data for all patients who underwent outpatient or same-day admit surgery during a year. Linear programming was used to determine by how much changing the mix of surgeons can increase total variable costs while maintaining the same total hours of OR time for elective cases. RESULTS: Changing OR allocations among surgeons without changing total OR hours allocated will likely increase perioperative variable costs by less than 34%. If, in addition, intensive care unit hours for elective surgical cases are not increased, hospital ward occupancy is capped, and implant use is tracked and capped, perioperative costs will likely increase by less than 10%. These four variables predict 97% of the variance in total variable costs. CONCLUSIONS: The authors showed that changing OR allocations among surgeons without changing total OR hours allocated can increase hospital perioperative variable costs by up to approximately one third. Thus, at hospitals with fixed or nearly fixed annual budgets, allocating OR time based on an OR-based statistic such as utilization can adversely affect the hospital financially. The OR manager can reduce the potential increase in costs by considering not just OR time, but also the resulting use of hospital beds and implants. (+info)A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. (4/65)
BACKGROUND: Covariance models (CMs) are probabilistic models of RNA secondary structure, analogous to profile hidden Markov models of linear sequence. The dynamic programming algorithm for aligning a CM to an RNA sequence of length N is O(N3) in memory. This is only practical for small RNAs. RESULTS: I describe a divide and conquer variant of the alignment algorithm that is analogous to memory-efficient Myers/Miller dynamic programming algorithms for linear sequence alignment. The new algorithm has an O(N2 log N) memory complexity, at the expense of a small constant factor in time. CONCLUSIONS: Optimal ribosomal RNA structural alignments that previously required up to 150 GB of memory now require less than 270 MB. (+info)A cost constraint alone has adverse effects on food selection and nutrient density: an analysis of human diets by linear programming. (5/65)
Economic constraints may contribute to the unhealthy food choices observed among low socioeconomic groups in industrialized countries. The objective of the present study was to predict the food choices a rational individual would make to reduce his or her food budget, while retaining a diet as close as possible to the average population diet. Isoenergetic diets were modeled by linear programming. To ensure these diets were consistent with habitual food consumption patterns, departure from the average French diet was minimized and constraints that limited portion size and the amount of energy from food groups were introduced into the models. A cost constraint was introduced and progressively strengthened to assess the effect of cost on the selection of foods by the program. Strengthening the cost constraint reduced the proportion of energy contributed by fruits and vegetables, meat and dairy products and increased the proportion from cereals, sweets and added fats, a pattern similar to that observed among low socioeconomic groups. This decreased the nutritional quality of modeled diets, notably the lowest cost linear programming diets had lower vitamin C and beta-carotene densities than the mean French adult diet (i.e., <25% and 10% of the mean density, respectively). These results indicate that a simple cost constraint can decrease the nutrient densities of diets and influence food selection in ways that reproduce the food intake patterns observed among low socioeconomic groups. They suggest that economic measures will be needed to effectively improve the nutritional quality of diets consumed by these populations. (+info)Measuring efficiency of physician practices using data envelopment analysis. (6/65)
PURPOSE: Medical-group practices are becoming increasingly common-place, with more than a third of licensed physicians in the United States currently working in this mode. While previous studies have focused on physician practices, little attention has been focused specifically on the contribution of internal organizational factors to overall physician practice efficiency. This paper develops a model to help determine best practices of efficient physician offices while allowing for choices between inputs. Measuring how efficient practices provide services yields useful information to help improve performance of less efficient practices. DESIGN: Data for this study were obtained from the 1999 Medical Group Management Association (MGMA) Cost Report. In this study, 115 primary care physician practices are analyzed. Outputs are defined as gross charges; inputs include square footage and medical, technical, and administrative support personnel. METHODOLOGY: Data envelopment analysis (DEA) is used in this study to develop a model of practice outputs and inputs to help identify the most efficient medical groups. DEA is a linear programming technique that converts multiple input and output measures to a single comprehensive measure of efficiency. These practices are used as a reference set for comparisons with less efficient ones. CONCLUSION: The overall results indicate that size of physician practice does not increase efficiency. There does not appear to be extensive substitution among inputs. Compared to other practices, efficient practices seem to manage each input well. (+info)Inferring strengths of protein-protein interactions from experimental data using linear programming. (7/65)
MOTIVATION: Several computational methods have been proposed for inference of protein-protein interactions. Most of the existing methods assume that protein-protein interaction data are given as binary data (i.e. whether or not each protein pair interacts). However, multiple biological experiments are performed for the same protein pairs and thus the ratio (strength) of the number of observed interactions to the number of experiments is available for each protein pair. RESULTS: We propose a new method for inference of protein-protein interactions from such experimental data. This method tries to minimize the errors between the ratios of observed interactions and the predicted probabilities in training data, where this problem is formalized as a linear program based on a probabilistic model. We compared the proposed method with the association method, the EM method and the SVM-based method using real interaction data. It is shown that a variant of the method is comparable to existing methods for binary data. It is also shown that the method outperforms existing methods for numerical data. AVAILABILITY: Programs transforming input data into LP format files are available upon request. (+info)Optimization models for cancer classification: extracting gene interaction information from microarray expression data. (8/65)
MOTIVATION: Microarray data appear particularly useful to investigate mechanisms in cancer biology and represent one of the most powerful tools to uncover the genetic mechanisms causing loss of cell cycle control. Recently, several different methods to employ microarray data as a diagnostic tool in cancer classification have been proposed. These procedures take changes in the expression of particular genes into account but do not consider disruptions in certain gene interactions caused by the tumor. It is probable that some genes participating in tumor development do not change their expression level dramatically. Thus, they cannot be detected by simple classification approaches used previously. For these reasons, a classification procedure exploiting information related to changes in gene interactions is needed. RESULTS: We propose a MAximal MArgin Linear Programming (MAMA) method for the classification of tumor samples based on microarray data. This procedure detects groups of genes and constructs models (features) that strongly correlate with particular tumor types. The detected features include genes whose functional relations are changed for particular cancer types. The proposed method was tested on two publicly available datasets and demonstrated a prediction ability superior to previously employed classification schemes. AVAILABILITY: The MAMA system was developed using the linear programming system LINDO http://www.lindo.com. A Perl script that specifies the optimization problem for this software is available upon request from the authors. (+info)
Linear programming
... linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear ... the dual of a dual linear program is the original primal linear program. Additionally, every feasible solution for a linear ... See dual linear program for details and several more examples. A covering LP is a linear program of the form: Minimize: bTy, ... In 1939 a linear programming formulation of a problem that is equivalent to the general linear programming problem was given by ...
Dual linear program
The dual of a given linear program (LP) is another LP that is derived from the original (the primal) LP in the following ... Suppose we have the linear program: Maximize cTx subject to Ax ≤ b, x ≥ 0. We would like to construct an upper bound on the ... This linear combination gives us an upper bound on the objective. The variables y of the dual LP are the coefficients of this ... So we create a linear combination of the constraints, with positive coefficients, such that the coefficients of x in the ...
Linear-fractional programming
Both linear programming and linear-fractional programming represent optimization problems using linear equations and linear ... linear-fractional programming (LFP) is a generalization of linear programming (LP). Whereas the objective function in a linear ... the objective function in a linear-fractional program is a ratio of two linear functions. A linear program can be regarded as a ... Any linear-fractional program can be transformed into a linear program, assuming that the feasible region is non-empty and ...
Linear programming relaxation
The linear programming relaxation of an integer program may be solved using any standard linear programming technique. If it ... linear programming); the solution to the relaxed linear program can be used to gain information about the solution to the ... the solution quality of the linear program is at least as good as that of the integer program, because any integer program ... may have quite different linear programming relaxations: a linear programming relaxation can be viewed geometrically, as a ...
Linear programming decoding
... linear programming decoding (LP decoding) is a decoding method which uses concepts from linear programming (LP) theory to solve ... "Using linear programming to Decode Binary linear codes," J. Feldman, M.J. Wainwright and D.R. Karger, IEEE Transactions on ... idea behind LP decoding is to first represent the maximum likelihood decoding of a linear code as an integer linear program, ... and then relax the integrality constraints on the variables into linear inequalities. " ...
Configuration linear program
The configuration linear program (configuration-LP) is a linear programming technique used for solving combinatorial ... First, they construct the dual linear program of the fractional LP: maximize n ⋅ y s.t. A T y ≤ 1 and y ≥ 0 {\displaystyle {\ ... The fractional configuration LP of bin-packing It is the linear programming relaxation of the above ILP. It replaces the last ... The relaxation was first presented by Gilmore and Gomory, and it is often called the Gilmore-Gomory linear program. Example: ...
Successive linear programming
... (SLP), also known as Sequential Linear Programming, is an optimization technique for ... Sequential quadratic programming Sequential linear-quadratic programming Augmented Lagrangian method (Nocedal & Wright 2006, p ... The linearizations are linear programming problems, which can be solved efficiently. As the linearizations need not be bounded ... "Nonlinear Optimization by Successive Linear Programming". Management Science. 28 (10): 1106-1120. doi:10.1287/mnsc.28.10.1106. ...
Linear genetic programming
... should not be confused with linear tree programs in tree genetic programming, program composed of a ... "Linear genetic programming" is unrelated to "linear programming". Linear genetic programming (LGP) is a particular method of ... Like other Genetic Programming methods, Linear genetic programming requires the input of data to run the program population on ... "Linear Genetic Programming", Springer, New York, 2007 Brameier, M.: "On linear genetic programming Archived 2007-06-29 at the ...
Basic solution (linear programming)
In linear programming, a discipline within applied mathematics, a basic solution is any solution of a linear programming ... ISBN 978-1-886529-19-9. v t e (Linear programming, All stub articles, Applied mathematics stubs). ... Bertsimas, Dimitris; Tsitsiklis, John N. (1997). Introduction to linear optimization. Belmont, Mass.: Athena Scientific. p. 50 ...
Hilbert basis (linear programming)
doi:10.1016/S0304-3975(00)00229-2. v t e (Linear programming, Discrete geometry, All stub articles, Applied mathematics stubs) ...
Sequential linear-quadratic programming
Newton's method Secant method Sequential linear programming Sequential quadratic programming Jorge Nocedal and Stephen J. ... a linear program (LP) used to determine an active set, followed by an equality-constrained quadratic program (EQP) used to ... In the LP phase of SLQP, the following linear program is solved: min d f ( x k ) + ∇ f ( x k ) T d s . t . b ( x k ) + ∇ b ( x ... Sequential linear-quadratic programming (SLQP) is an iterative method for nonlinear optimization problems where objective ...
GNU Linear Programming Kit
The GNU Linear Programming Kit (GLPK) is a software package intended for solving large-scale linear programming (LP), mixed ... Linear Programming and Algorithms for Communication Networks: A Practical Guide to Network Design, Control, and Management. CRC ... However, GLPK is most commonly called from other programming languages. Wrappers exist for: Julia and the JuMP modeling package ... Free software programmed in C, Mathematics software for Linux). ... integer programming (MIP), and other related problems. It is a ...
Multi-objective linear programming
A multiple objective linear program (MOLP) is a linear program with more than one objective function. An MOLP is a special case ... multiple objective linear programming and vector linear programming". Mathematical Methods of Operations Research. 84 (2): 411- ... of a vector linear program. Multi-objective linear programming is also a subarea of Multi-objective optimization. In ... with an optimal solution of a linear program but rather parallels the set of all optimal solutions of a linear program (which ...
Fundamental theorem of linear programming
... the fundamental theorem of linear programming states, in a weak formulation, that the maxima and minima of a linear function ...
Linear complementarity problem
Murty, K. G. (1988). Linear complementarity, linear and nonlinear programming. Sigma Series in Applied Mathematics. Vol. 3. ... Cottle, R. W.; Dantzig, G. B. (1968). "Complementary pivot theory of mathematical programming". Linear Algebra and Its ... Terlaky, Tamás; Zhang, Shu Zhong (1993). "Pivot rules for linear programming: A Survey on recent theoretical developments". ... Todd, Michael J. (1985). "Linear and quadratic programming in oriented matroids". Journal of Combinatorial Theory. Series B. 39 ...
Linear congruential generator
Combined linear congruential generator Knuth, Donald (1997). Seminumerical Algorithms. The Art of Computer Programming. Vol. 2 ... It is easy to detect the structure of a linear-feedback shift register with appropriate tests such as the linear complexity ... A linear congruential generator (LCG) is an algorithm that yields a sequence of pseudo-randomized numbers calculated with a ... See the code after the test for "TYPE_0"; the GNU C library's rand() in stdlib.h uses a simple (single state) linear ...
Lis (linear algebra library)
Numerical linear algebra, Scientific simulation software, C (programming language) libraries, Fortran libraries, Free ... floating point operations Performance analysis Command-line interface to solvers and benchmarks A C program to solve the linear ... Lis (Library of Iterative Solvers for linear systems, pronounced [lis]) is a scalable parallel software library for solving ... Lis provides facilities for: Automatic program configuration NUMA aware hybrid implementation with MPI and OpenMP Exchangeable ...
Quantum algorithm for linear systems of equations
Differentiable programming Harrow, Aram W; Hassidim, Avinatan; Lloyd, Seth (2008). "Quantum algorithm for solving linear ... Due to the prevalence of linear systems in virtually all areas of science and engineering, the quantum algorithm for linear ... linear equations for various input vectors. The quantum circuit is optimized and compiled into a linear optical network with ... which is an optimized linear or non-linear binary classifier. A support vector machine can be used for supervised machine ...
Linear search
The Art of Computer Programming. Vol. 3 (2nd ed.). Reading, MA: Addison-Wesley Professional. ISBN 0-201-89685-0 (Harv and Sfn ... A linear search runs in at worst linear time and makes at most n comparisons, where n is the length of the list. If each ... the cost of linear search is only O(1). Linear search is usually very simple to implement, and is practical when the list has ... asymptotically the worst-case cost and the expected cost of linear search are both O(n). The performance of linear search ...
Basis (linear algebra)
Basis of a matroid Basis of a linear program Change of basis - Coordinate change in linear algebra Frame of a vector space - ... See also a Hilbert basis (linear programming). For a probability distribution in Rn with a probability density function, such ... "Linear combinations, span, and basis vectors". Essence of linear algebra. August 6, 2016. Archived from the original on 2021-11 ... In this case, the finite subset can be taken as B itself to check for linear independence in the above definition. It is often ...
Linear park
1939). It may also have been used in 1873, but Ngram didn't provide the source(s). "Parks and Recreation Programming Master ... Seoul Al zorah linear park, Ajman Stirling Linear Park, near Adelaide Sturt River Linear Park, near Adelaide The Goods Line, ... A linear park is a type of park that is significantly longer than it is wide.[full citation needed] These linear parks are ... Houses connected with linear parks are more common in suburban and rural areas where space is much less in-demand. A linear ...
Linear logic
Overview of linear logic programming by Dale Miller. In Linear Logic in Computer Science, edited by Ehrhard, Girard, Ruet, and ... Linear Logic Wiki Media related to Linear logic at Wikimedia Commons A Linear Logic Prover (llprover) Archived 2016-04-04 at ... Introduction to Linear Logic (Postscript) by Patrick Lincoln Introduction to Linear Logic by Torben Brauner A taste of linear ... doi:10.1016/0168-0072(92)90075-B. Kanovich, Max I. (1992-06-22). "Horn programming in linear logic is NP-complete". Seventh ...
Bayesian linear regression
ISBN 0-340-52922-9. Bayesian estimation of linear models (R programming wikibook). Bayesian linear regression as implemented in ... Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear ... The model evidence of the Bayesian linear regression model presented in this section can be used to compare competing linear ... see Bayesian multivariate linear regression. Bayes linear statistics Constrained least squares Regularized least squares ...
Linear discriminant analysis
The statistical program (or a related module) will map the results. The map will plot each product (usually in two-dimensional ... Linear classification in this non-linear space is then equivalent to non-linear classification in the original space. The most ... These linear inequalities can be selected in the standard (Fisher's) form of the linear discriminant for a rich family of ... Fisher's linear discriminant rule: Maximizes the ratio between SSbetween and SSwithin, and finds a linear combination of the ...
Nonnegative rank (linear algebra)
Expressing combinatorial optimization problems by linear programs. J. Comput. Syst. Sci., 43(3):441-466, 1991. See this blog ... In linear algebra, the nonnegative rank of a nonnegative matrix is a concept similar to the usual linear rank of a real matrix ... For example, the linear rank of a matrix is the smallest number of vectors, such that every column of the matrix can be written ... There are several equivalent definitions, all modifying the definition of the linear rank slightly. Apart from the definition ...
Linear independence
Tutorial and interactive program on Linear Independence. Introduction to Linear Independence at KhanAcademy. (CS1 maint: ... A linear dependency or linear relation among vectors v1, ..., vn is a tuple (a1, ..., an) with n scalar components such that a ... Linear dependencies among v1, ..., vn form a vector space. If the vectors are expressed by their coordinates, then the linear ... Abstraction of linear independence of vectors G. E. Shilov, Linear Algebra (Trans. R. A. Silverman), Dover Publications, New ...
Compact linear Fresnel reflector
196-207, 2010 United States Department of Energy (2009). "Solar Energy Technologies Program: Concentrating Solar Power" (PDF). ... A compact linear Fresnel reflector (CLFR) - also referred to as a concentrating linear Fresnel reflector - is a specific type ... Linear Fresnel reflectors use long, thin segments of mirrors to focus sunlight onto a fixed absorber located at a common focal ... The first linear Fresnel reflector solar power system was developed in Italy in 1961 by Giovanni Francia of the University of ...
Unification (computer science)
Syntactical first-order unification is used in logic programming and programming language type system implementation, ... Michael Stewart Paterson and M.N. Wegman (Apr 1978). "Linear unification". J. Comput. Syst. Sci. 16 (2): 158-167. doi:10.1016/ ... As a programming example, a parametric sort list(X) may be declared (with X being a type parameter as in a C++ template), and ... Program. Lang. Syst. 4 (2): 258-282. doi:10.1145/357162.357169. S2CID 10921306. Robinson (1965); nr.2.5, 2.14, p.25 Robinson ( ...
Václav Chvátal
Vašek Chvátal (1983). Linear Programming. W.H. Freeman. ISBN 978-0-7167-1587-0.. Japanese translation published by Keigaku ... 1975). Chvátal first became interested in linear programming through the influence of Jack Edmonds while Chvátal was a student ... At Stanford in the 1970s, he began writing his popular textbook, Linear Programming, which was published in 1983. Cutting ... "Some linear programming aspects of combinatorics" (PDF), Congressus Numerantium, 13: 2-30, Chvátal, V. (1975), "On certain ...
Total dual integrality
Thus, if a linear program as above is solved by the simplex algorithm, the optimal solution returned will be integer. Further, ... doi:10.1016/0024-3795(81)90005-7. Chekuri, C. "Combinatorial Optimization Lecture Notes" (PDF). (Linear programming). ... of a linear objective over the integral points of such a polyhedron can be done using techniques from linear programming. A ... bounded solution to the linear program max c T x A x ≤ b , {\displaystyle {\begin{aligned}&&\max c^{\mathrm {T} }x\\&&Ax\leq b ...
Linear programming - Wikipedia
... linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear ... the dual of a dual linear program is the original primal linear program. Additionally, every feasible solution for a linear ... See dual linear program for details and several more examples. A covering LP is a linear program of the form: Minimize: bTy, ... In 1939 a linear programming formulation of a problem that is equivalent to the general linear programming problem was given by ...
Dynamic programming and linear prediction theory. | RAND
... which applies dynamic programming theory to a particular discrete form of the linear prediction problem. 9 pp.... ... Bellman, Richard Ernest, Dynamic programming and linear prediction theory. RAND Corporation, P-2308, 1961. As of November 28, ... which applies dynamic programming theory to a particular discrete form of the linear prediction problem. 9 pp. ... programs offered at an independent public policy research organization-the RAND Corporation. ...
Linear Programming | University of Bergen
Sensor Location through Linear Programming with Arrival Angle Constraints | NIST
... we established a linear programming framework to determine sensor location from measured link distances between neighboring ... Gentile, C. and Shiu, J. (2007), Sensor Location through Linear Programming with Arrival Angle Constraints, IEEE Conference on ... In previous work, we established a linear programming framework to determine sensor location from measured link distances ... Besides providing greater accuracy compared to other techniques, linear programs in particular suit large networks since they ...
More results on Shortest Linear Programs
Shortest linear programs are essentially the minimum number of 2-input xor gates required to completely describe a linear ... presented their results on shortest linear programs for the linear layers of several well known block ciphers in literature. ... Block CiphersMDS MatricesShortest Linear Programs. Contact author(s). subhadeep banik @ epfl ch History. 2019-07-23: received. ... More results on Shortest Linear Programs. Subhadeep Banik, Yuki Funabiki, and Takanori Isobe ...
Generating Asymptotically Non-terminant Initial Variable Values for Linear Diagonalizable Programs.
Considering linear diagonalizable programs, we describe powerful computational methods that generate automatically and ... Generating Asymptotically Non-terminant Initial Variable Values for Linear Diagonalizable Programs.. 12 pages•Published: June ... Generating Asymptotically Non-terminant Initial Variable Values for Linear Diagonalizable Programs.}, booktitle = {SCSS 2013. ... Keyphrases: linear algebra, static analysis, termination analysis. In: Laura Kovács and Temur Kutsia (editors). SCSS 2013. 5th ...
Linear and Multiobjective Programming with Fuzzy Stochastic Extensions | Mathematical Association of America
egison: Programming language with non-linear pattern-matching against unfree data
Programming language with non-linear pattern-matching against unfree data. [ compilers-interpreters, library, mit, program ] [ ... An interpreter for Egison, the programming langugage that realized non-linear pattern-matching against unfree data types. With ... We can find Egison programs in lib. and sample. directories. This package also include Emacs Lisp file egison-mode.el. in elisp ... The following code is the program that determines poker-hands written in Egison. All hands are expressed in a single pattern. ...
LPDA: A new classification method based on linear programming | PLOS ONE
Algebra 2 linear programming
From algebra 2 linear programming to algebra i, we have got every part discussed. Come to Factoring-polynomials.com and learn ... Click on the pertaining software demo button found in the same line as your search term algebra 2 linear programming ... Locate the phrase you are searching for (i.e. algebra 2 linear programming) in the leftmost column below ... If you find the program demo helpful click on the buy button to obtain the program at a special price extended only to ...
Classroom Activities: Using Linear Programming in Real-Life Problems - Texas Instruments - content
... students explore linear equation models for real-life situations. ... Math: Algebra I: Linear Inequalities. *Math: Algebra I: Systems ... Find the minimum cost of baking two types of cookies (x and y) using inequalities and linear programming ... In this activity, students explore linear equation models for real-life situations. ...
USPAS | Programs | Colorado (2001) | Recirculating Linear Accelerators
Recirculating Linear Accelerators by Geoffrey Krafft and Lia Merminga was offered at the U.S. Particle Accelerator School ... Recirculating Linear Accelerators course. Sponsoring University:. University of Colorado at Boulder. Course:. Recirculating ... In the first part of the course, we identify and review the main types of recirculating linear accelerators and describe ... 1981-2023 U.S. Particle Accelerator School, a national training program managed by Fermilab ...
egison: Programming language with non-linear pattern-matching against non-free data
Programming language with non-linear pattern-matching against non-free data. [ compilers-interpreters, library, mit, program ... We can do non-linear pattern-matching against non-free data types in Egison. An non-free data type is a data type whose data ... We can find Egison programs in lib. and sample. directories. This package also include Emacs Lisp file elisp/egison-mode.el. . ... The following code is the program that determines poker-hands written in Egison. All hands are expressed in a single pattern. ...
Linear Offers Incentive Program for Installers, Contractors - Security Sales & Integration
Linear Offers Incentive Program for Installers, Contractors October 16, 2007. SSI Staff ... Linear LLC, a provider of residential low-voltage wiring systems and electronic components, announces a new incentive program ... Tagged with: Industry News Linear Business News Related Content Security Industry Association Announces 2024 SIA RISE ... Linear will offer the DMC-10 Model Home Protection from Nov. 1 to Dec. 31, during which qualified contractors and dealers will ...
An Approach to Diet Cost Optimization for Different Age Groups Using Linear Programming
For this purpose, first a linear programming optimization model is selected and then three data files defining the various food ... Linear Programming optimization is used in this paper to find minimum cost and quantity of food items for selection of proper ... Linear Programming (LP) optimization is a process where desired solution is obtained from a set of linear equations or ... As AMPL is used to construct and solve the linear equations to obtain the solution, some sample of constructed linear programs ...
TI-89 BASIC Math Programs (Linear Algebra, Vector, Matrix) - ticalc.org
This program finds the dual of a linear program. The program is self-explanatory, with examples on each dialog box. The program ... Nth order linear differential eq. solver. This is a set of programs that will find general solutions to nonhomogenous linear ... Linear Equation Tool. Linear Equation Tool. Only for the TI-89 Titanium. Allows you to input linear equations in many ways and ... This program makes multi variable calculus MUCH easier!. linearsys.zip. 1k. 02-10-23. Linear System of Equations Solver and ...
The use of linear programming to evaluate the impact of credit for investments in small goat farms | FAO
The use of linear programming to evaluate the impact of credit for investments in small goat farms. The PRONAF is a ... This mathematical model was solved using linear programming with LINDO® software. When the credit for investment was expanded ... ainfo.cnptia.embrapa.br/digital/bitstream/item/86437/1/NT-The-use-of-linear-programming.pdf ... governmental program that subsidies the credit for investment to smallholder farming and improves the social development in ...
A New Approach of Solving Linear Fractional Programming Problem (LFP) by Using Computer Algorithm
Compare our solution with other available methods and explore numerical examples and computer programs for optimal results. ... Discover a new approach for solving linear fractional programming problems with our simple and effective method. ... is used to solve this linear fractional program by solving a sequence of linear programs only re-computing the local gradient ... Naturally, there is a need for generalizing the simplex technique for linear programming to the ratio of linear functions or to ...
Suppose a linear programming (maximation) problem has been s | Quizlet
Suppose a linear programming (maximation) problem has been solved and that the optimal value of the objective function is $\$ ... Solve the following linear programming problem graphically: Maximize profit =. 4. X. +. 6. Y. =4 X+6 Y. =4X+6Y ... Solve the following linear programming problem graphically: Maximize profit =. 4. X. +. 6. Y. =4 X+6 Y. =4X+6Y ... Suppose a linear programming (maximation) problem has been solved and that the optimal value of the objective function is $. ...
KGS--Bull. 194, pt. 1--Linear Programming of Osage County Resources: A Preliminary Report
... such problems can be solved by the method of linear programming. A suitable definition of linear programming for our purposes ... Linear Programming of Osage County Resources: A Preliminary Report. by Fred E. Miller. ... An awareness of the assumptions and drawbacks of a linear programming formulation must be maintained if its results are to be ... Heretofore, no known application of linear programming to this problem is in existence. This study is an attempt to devise such ...
Noncommercial Software for Mixed-Integer Linear Programming - Optimization Online
... optimization linear programming machine learning mixed-integer linear programming mixed-integer nonlinear programming mixed- ... Noncommercial Software for Mixed-Integer Linear Programming. Published: 2004/12/23. , Updated: 2022/09/22. *Jeff Linderoth ... We present an overview of noncommercial software tools for the solution of mixed-integer linear programs (MILPs). We first ... algorithm quadratic programming robust optimization semidefinite programming stochastic optimization stochastic programming ...
Linear programming downloads and reviews: WebCab Optimization for .NET 2.6
Linear programming reviews and free downloads: WebCab Optimization for .NET 2.6 and other ... Add optimization & Linear Programming solver to your .NET and COM Applications.. Demo WebCab Optimization (J2EE Edition) 2.6 ... Barcode Linear ActiveX Control & OCX 1.3 An ActiveX Control for Linear Barcodes - Code 39, Code 128, UPC, EAN and more.. Demo ... Barcode Linear ActiveX Control & OCX 1.3 An ActiveX Control for Linear Barcodes - Code 39, Code 128, UPC, EAN and more.. Demo ...
Solving Saddle Point Formulations of Linear Programs with Frank-Wolfe
The problem of solving a linear program (LP) is ubiquitous in industry, yet in recent years the size of linear programming ... The best known first-order method for general linear programming problems is PDLP. First-order methods for linear programming ... In response to the growing size of linear programs and poor scalability of existing algorithms, researchers have started to ... We present a first-order primal-dual algorithm for solving saddle point formulations of linear programs, named FWLP (Frank- ...
Water | Free Full-Text | A Linear Programming Model for Operational Optimization of Agricultural Activity Considering a...
In this study, we proposed the integration between the hydroclimatic monitoring system and a linear programming method to ... Vanderbei, R.J. Linear Programming. In International Series in Operations Research & Management Science, 4th ed.; Hillier, F.S ... Freire-González, J.; Decker, C.A.; Hall, J.W. A linear programming approach towater allocation during a drought. Water 2018, 10 ... Luenberger, D.G.; Ye, Y. Linear and Nonlinear Programming, 4th ed.; Price, C.C., Zhu, J., Hillier, F.S., Eds.; Springer: Cham, ...
talks.cam : Iterative algorithms and Sherali-Adams linear programming relaxations of graph isomorphism
Iterative algorithms and Sherali-Adams linear programming relaxations of graph isomorphism. Add to your list(s) Download to ... We compare the WL algorithm to a hierarchy of linear programming relaxations of graph isomorphism generated by the Sherali- ... Iterative algorithms and Sherali-Adams linear programming relaxations of graph isomorphism ...
co.combinatorics - Explicit Formula of Delsarte's Linear Programming Upper Bound for $A q(n,3)$ - MathOverflow
The best are given by a linear programming approach (now improved to a semi-definite programming approach) given by Delsarte in ... I have recently been searching for an explicit formula for Delsartes Linear Programming Upper Bound for $A_q(n,3)$ in the ... Explicit Formula of Delsartes Linear Programming Upper Bound for $A_q(n,3)$. Ask Question ... cstheory.stackexchange.com/questions/40238/explicit-formula-of-delsartes-linear-programming-upper-bound-for-a-qn-3 The results ...
optimization - Linear Programming Negativity Constraints - Mathematics Stack Exchange
To set up an Linear Programming problem in Standard Form, I learned that it must be of maximization type. The constraints must ... Can someone explain how to do the change of variables in a linear programming problem? ... Converting nonlinear constraints (product of binary and continuous variables) for linear programming ...
Improving the Linear Programming Technique in the Search for Lower Bounds in Secret Sharing
Outstanding Interactive Extension Of A Linear Program Nominees / Winners 2020 Emmy Awards | Television Academy
Outstanding Interactive Extension Of A Linear Program Nominees / Winners 2020 - The 2020 Television Academy Emmy Awards Nominee ... Short Form Animated Program. Art Directors and Set Decorators *Production Design For A Narrative Contemporary Program (One Hour ... Costumes for Variety, Nonfiction Or Reality Programming. Documentary / NonFiction Programming *Directing For A Documentary/ ... Contemporary Makeup For A Variety, Nonfiction Or Reality Program (Non-prosthetic). *Prosthetic Makeup For A Series, Limited ...
Linear programming: Simplex method exampleAlgebraMixed-integerConstraintsInequalitiesConvexSimplexAlgorithmsFormulationEquationsSolverProblemAlgorithmRegressionComputationalConstraintSemidefiniteProblemsAlgebraicObjective functionTechnique2020EigenvaluesPredictionTheoretical resultsMathematical modelTheoryMethodsEquivalentEquationDescribeCorrelationOptimizeFormulateEvaluateApproachSystemResultsPythonFindsEgisonVariablesOptimalInterventionsGeneralGraphFrameworkCharacteristicsDataExercisesBasicSuccessfully
Algebra16
- A First Course in Linear Algebra is an introduction to the basic concepts of linear algebra, along with an introduction to the techniques of formal mathematics. (freecomputerbooks.com)
- It begins with systems of equations and matrix algebra before moving into the theory of abstract vector spaces, eigenvalues, linear transformations and matrix representations. (freecomputerbooks.com)
- This book is intended as a text for a graduate course that focuses on applications of linear algebra and on the algorithms used to solve the problems that arise in those applications. (freecomputerbooks.com)
- Combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. (freecomputerbooks.com)
- This textbook on linear algebra is written to be easy to digest by non-mathematicians. (freecomputerbooks.com)
- A college (or advanced high school) level text dealing with the basic principles of matrix and linear algebra. (freecomputerbooks.com)
- The focus is on numerical linear algebra, the study of how theory, algorithms, and computer arithmetic interact. (freecomputerbooks.com)
- Linear Algebra: Foundations to Frontiers (M. Myers, et al. (freecomputerbooks.com)
- This undergraduate textbook on Linear Algebra and n-Dimensional Geometry, in a self-teaching style, is invaluable for sophomore level undergraduates in mathematics, engineering, business, and the sciences. (freecomputerbooks.com)
- This book contains selected topics in linear algebra, which represent the recent contributions in the most famous and widely problems. (freecomputerbooks.com)
- It includes a wide range of theorems and applications in different branches of linear algebra, etc. (freecomputerbooks.com)
- Linear Algebra: Foundations to Frontiers (LAFF) is packed full of challenging, rewarding material that is essential for mathematicians, engineers, scientists, and anyone working with large datasets. (freecomputerbooks.com)
- The aim of the text is to achieve a balance among computational skills, theory, and applications of linear algebra. (freecomputerbooks.com)
- It is a relatively advanced introduction to the ideas and techniques of linear algebra targeted for science and engineering students. (freecomputerbooks.com)
- Hi, I would like to report that Metashape 1.8.2 crashes whenever I try to call linear algebra methods from numpy. (agisoft.com)
- Mathematics: Calculus in one and several variables and linear algebra. (lu.se)
Mixed-integer6
- We present an overview of noncommercial software tools for the solution of mixed-integer linear programs (MILPs). (optimization-online.org)
- Solving mixed-integer nonlinear problems by means of piecewise linear relaxations can be a reasonable alternative to the commonly used spatial branch-and-bound. (optimization-online.org)
- To this end, we implement eight different mixed-integer representations for piecewise linear relaxations and evaluate them on a benchmark set from the MINLPLib consisting of over 300 instances. (optimization-online.org)
- This paper aims to consider each strategy of the particle swarm optimization (PSO) as a unit in data envelopment analysis (DEA) and uses the minimax mixed-integer linear programming DEA approach to find the most suitable inertia weight strategy. (emerald.com)
- In this paper, the PSO algorithm is integrated with minimax mixed-integer linear programming to find the most suitable inertia weight strategy. (emerald.com)
- Özsoy, V.S. (2021), "The determination of the most suitable inertia weight strategy for particle swarm optimization via the minimax mixed-integer linear programming model", Engineering Computations , Vol. 38 No. 4, pp. 1933-1954. (emerald.com)
Constraints8
- More formally, linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints. (wikipedia.org)
- We see that it has to deal with the ratio of two linear functions, that's why its computational process is complicated and also when the constraints are not in canonical form then it becomes lengthy. (scirp.org)
- A suitable definition of linear programming for our purposes is the maximization or minimization of a linear objective function subject to linear constraints. (ku.edu)
- we must also set up the constraints on the problem as a linear system. (ku.edu)
- Since the payoff functions and all the constraints in our Stackelberg games may be expressed in a linear form, these games will be formalized as bilevellinear programming problems (BLPPs). (units.it)
- Linear programs are often specified by hand, using prior knowledge of relevant costs and constraints. (neurips.cc)
- Write the linear inequality constraints. (mathworks.com)
- The problem has eight variables, four linear equality constraints, and has all variables restricted to be positive. (mathworks.com)
Inequalities4
- The problem of solving a system of linear inequalities dates back at least as far as Fourier, who in 1827 published a method for solving them, and after whom the method of Fourier-Motzkin elimination is named. (wikipedia.org)
- Dantzig provided formal proof in an unpublished report "A Theorem on Linear Inequalities" on January 5, 1948. (wikipedia.org)
- Considering linear diagonalizable programs, we describe powerful computational methods that generate automatically and symbolically a semi-linear space represented by a linear system of equalities and inequalities. (easychair.org)
- Linear Programming (LP) optimization is a process where desired solution is obtained from a set of linear equations or inequalities. (scirp.org)
Convex4
- Its feasible region is a convex polytope, which is a set defined as the intersection of finitely many half spaces, each of which is defined by a linear inequality. (wikipedia.org)
- PSDBoost is based on the observation that any trace-one positive semidefinitematrix can be decomposed into linear convex combinations of trace-one rank-one matrices, which serve as base learners of PSDBoost. (nips.cc)
- For a non-linear convex optimization problem, the last 2 items still hold, since the weak duality $d^* \le p^*$ ($d^*$ is dual optimal and $p^*$ is primal optimal). (stackexchange.com)
- For predictive purposes, we construct two parallel hyperplanes, using linear programming, or two similar concentric ellipsoids, using semidefinite programming, so that the intersection of the convex hulls of the two sets is contained between the two hyperplanes, or the two ellipsoids, and the rest of the two sets, are separated completely. (ici.ro)
Simplex8
- In 1941, Frank Lauren Hitchcock also formulated transportation problems as linear programs and gave a solution very similar to the later simplex method. (wikipedia.org)
- In 1947, Dantzig also invented the simplex method that, for the first time efficiently, tackled the linear programming problem in most cases. (wikipedia.org)
- However, it takes only a moment to find the optimum solution by posing the problem as a linear program and applying the simplex algorithm. (wikipedia.org)
- Topics that are covered include the simplex method and the interior point methods for linear programming, network algorithms, duality theory and sensitivity analysis. (uib.no)
- Naturally, there is a need for generalizing the simplex technique for linear programming to the ratio of linear functions or to the case of the ratio of quadratic functions in such a situation. (scirp.org)
- MacOSXLinPro uses the simplex algorithm to solve linear programming models like ones found in operations research, operations management, and industrial engineering. (rbytes.net)
- However, both the Simplex method and interior-point methods require solving a system of linear equations at each iteration, an operation that does not scale well with the size of the problem. (uwaterloo.ca)
- Simplex algorithm was used in solving the resulting linear programming models. (scialert.net)
Algorithms1
- In response to the growing size of linear programs and poor scalability of existing algorithms, researchers have started to consider first-order methods for solving large scale linear programs. (uwaterloo.ca)
Formulation5
- In 1939 a linear programming formulation of a problem that is equivalent to the general linear programming problem was given by the Soviet mathematician and economist Leonid Kantorovich, who also proposed a method for solving it. (wikipedia.org)
- From 1946 to 1947 George B. Dantzig independently developed general linear programming formulation to use for planning problems in the US Air Force. (wikipedia.org)
- The decision variable name is assigned to 'x,' which captures all the decision variables in the linear programming formulation. (gurobi.com)
- Moreover, we study the computational performance of the column generation algorithm for solving the linear relaxation of the most promising formulation. (kobv.de)
- Here, we shall learn about some basic concepts of linear programming problems in two variables, their applications, advantages, limitations, formulation and graphical method of solution. (slideshare.net)
Equations4
- Shortest linear programs are essentially the minimum number of 2-input xor gates required to completely describe a linear system of equations. (iacr.org)
- The defined variables formulate a number of linear equations following the model and finally these equations are solved to find the target solution. (scirp.org)
- This new version of cSolve_n() finds real and complex solutions of n x n systems of non-linear equations. (ticalc.org)
- this program is designed to calculate solutions to overdetermined systems of inexact, linear equations using least-squares, linear programming, or Chebyshev (equal residuals) solution criteria. (cdc.gov)
Solver1
- Add optimization & Linear Programming solver to your .NET and COM Applications. (rbytes.net)
Problem25
- The linear programming problem was first shown to be solvable in polynomial time by Leonid Khachiyan in 1979, but a larger theoretical and practical breakthrough in the field came in 1984 when Narendra Karmarkar introduced a new interior-point method for solving linear-programming problems. (wikipedia.org)
- An extension of earlier results given in P-1147, Dynamic Programming and Mean Square Deviation, which applies dynamic programming theory to a particular discrete form of the linear prediction problem. (rand.org)
- can explain what a linear optimization problem is and how it can be solved. (uib.no)
- can analyze solutions to a linear optimization problem. (uib.no)
- We can write program to solve the travelling salesman problem in a single pattern-matching expression. (haskell.org)
- In this paper, we study a new approach for solving linear fractional programming problem (LFP) by converting it into a single Linear Programming (LP) Problem, which can be solved by using any type of linear fractional programming technique. (scirp.org)
- In the present paper, we propose a new method and develop FORTRAN programs to solve the problem. (scirp.org)
- Our proposed method of linear fractional programming (LFP) problem is very simple and easy to understand and apply. (scirp.org)
- Effati and Pakdaman [11] propose a method for solving the interval-valued linear fractional programming problem. (scirp.org)
- 12] develops a method for solving multi-objective linear plus linear fractional programming problem based on Taylor Series approximation. (scirp.org)
- What is a constraint in a linear programming problem? (quizlet.com)
- Which ones are invalid for use in a linear programming problem, and why? (quizlet.com)
- Heretofore, no known application of linear programming to this problem is in existence. (ku.edu)
- Finally we should understand the meaning of linearity as it applies to the linear programming problem. (ku.edu)
- We assume that the relationships existing among all of the variables in our problem are linear. (ku.edu)
- The problem of solving a linear program (LP) is ubiquitous in industry, yet in recent years the size of linear programming problems has grown and continues to do so. (uwaterloo.ca)
- To set up an Linear Programming problem in Standard Form, I learned that it must be of maximization type. (stackexchange.com)
- Can someone explain how to do the change of variables in a linear programming problem? (stackexchange.com)
- To solve the RAP, we need to formulate it as a linear programming problem. (gurobi.com)
- Multi-Objective Non-Linear Programming Problem for Reliability Optimization in Intuitionistic Fuzzy Environment, Frontiers in Information Systems Mathematics Applied in Information Systems (2018) 2: 197. (benthamscience.com)
- To benchmark heuristics for the problem, we develop integer programming formulations and study their properties. (kobv.de)
- Then, for a non-linear programming problem, do the first 2 items hold? (stackexchange.com)
- Title of program: CONTIN (VERSION 2DP) Catalogue Id: AAOB_v1_0 Nature of problem Many experiments are indirect in that the observed data are linear integral (or matrix) transforms of the quantities to be estimated. (digitalcommonsdata.com)
- Compare the number of steps to solve an integer programming problem both with and without an initial feasible point. (mathworks.com)
- Consider the following linear programming problem. (quickdocs.org)
Algorithm4
- A linear programming algorithm finds a point in the polytope where this function has the smallest (or largest) value if such a point exists. (wikipedia.org)
- We present a first-order primal-dual algorithm for solving saddle point formulations of linear programs, named FWLP (Frank-Wolfe Linear Programming). (uwaterloo.ca)
- We compare the WL algorithm to a hierarchy of linear programming relaxations of graph isomorphism generated by the Sherali-Adams lift-and-project method. (cam.ac.uk)
- In this paper we propose a generic branch-and-bound algorithm for solving multi-objective integer linear programming problems. (au.dk)
Regression2
Computational2
- First-order methods for linear programming are characterized by having a matrix-vector product as their primary computational cost. (uwaterloo.ca)
- When increasing the number of objective functions, however, the polyhedral structure of the linear relaxation becomes more complex, and consequently requires more computational effort to obtain. (au.dk)
Constraint2
- An additional constraint common to all linear programming problems is that the variables themselves must not be negative. (ku.edu)
- Define the linear equality constraint matrix and vector. (mathworks.com)
Semidefinite3
- Here's an example involving a primal-dual pair of semidefinite programming problems in which the primal has multiple optimal solutions but the dual is infeasible. (stackexchange.com)
- We give five methods based on linear programming techniques, and two methods based on semidefinite programming techniques. (ici.ro)
- Xing LIU, Florian A. POTRA, Pattern Separation and Prediction via Linear and Semidefinite Programming , Studies in Informatics and Control , ISSN 1220-1766, vol. 18 (1), pp. 71-82, 2009. (ici.ro)
Problems11
- About the same time as Kantorovich, the Dutch-American economist T. C. Koopmans formulated classical economic problems as linear programs. (wikipedia.org)
- All these problems are fragments of a general class of optimization problems, termed in the literature as fractional programming problems. (scirp.org)
- they obtain two equivalent LP problems and name them as EP and EN. (scirp.org)
- If any of the two problems EP or EN is unbound, then LFP is also unbound. (scirp.org)
- Under certain assumptions, primarily linearity, such problems can be solved by the method of linear programming. (ku.edu)
- The best known first-order method for general linear programming problems is PDLP. (uwaterloo.ca)
- In generai, bilevel programming problems are difficult to sol ve because of their inherent non-convexity and non-differentiability. (units.it)
- Overall, the classical incremental method Markowitz and Manne 1957 has the best performance, leading to a general recommendation of this method for solving nonlinear problems by piecewise linear relaxations. (optimization-online.org)
- Linear Programming' is a scientific tool to handle optimization problems. (slideshare.net)
- In the recent literature, competitive frameworks has been proposed for bi-objective 0-1 problems, and many of these frameworks rely on the use of the linear relaxation to obtain lower bound sets. (au.dk)
- It's designed to provide a high-level and ergonomic API for specifying linear programming problems as lisp expressions. (quickdocs.org)
Algebraic1
- Our approach is based on linear algebraic methods and results. (easychair.org)
Objective function3
- Its objective function is a real-valued affine (linear) function defined on this polyhedron. (wikipedia.org)
- Besides providing greater accuracy compared to other techniques, linear programs in particular suit large networks since they can be solved efficiently through distributed computing over the nodes without compromising the optimality of the objective function. (nist.gov)
- Another method is called updated objective function method derived from Bitran and Novaes [6] is used to solve this linear fractional program by solving a sequence of linear programs only re-computing the local gradient of the objective function. (scirp.org)
Technique3
- To select the optimum plan a quantitative technique named linear programming can be used. (scirp.org)
- The conflicting nature of the objectives is resolved with the help of intuitionistic fuzzy programming technique by recognizing the linear, as well as non-linear membership functions. (benthamscience.com)
- Linear Programming (LP) technique was used to investigate, analyse and determine the most efficient way of compounding the least-cost ration. (scialert.net)
20202
Eigenvalues1
- This textbook covers linear systems and Gauss' method, vector spaces, linear maps and matrices, determinants, and eigenvectors and eigenvalues. (freecomputerbooks.com)
Prediction2
Theoretical results1
- Furthermore, bilevel programming is rich of theoretical results and numerica! (units.it)
Mathematical model2
- Linear programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. (wikipedia.org)
- This mathematical model was solved using linear programming with LINDO® software. (fao.org)
Theory3
- The theory behind linear programming drastically reduces the number of possible solutions that must be checked. (wikipedia.org)
- Section 2 describes the general Linear Programming Optimization theory. (scirp.org)
- https://cstheory.stackexchange.com/questions/40238/explicit-formula-of-delsartes-linear-programming-upper-bound-for-a-qn-3 The results on $A_q(n,d)$ are often published in top combinatorics journals (Journal of Combinatorial Theory, Series A, for instance), and so I think it is appropriate and hopefully of interest to MO users as well. (mathoverflow.net)
Methods3
- The course contains solution methods for linear optimization models. (uib.no)
- Experiments show that our method successfully learns synthetic linear programs and minimum-cost multi-commodity flow instances for which previous methods are not directly applicable. (neurips.cc)
- Methods: 95 participants were assigned to either the HiBalance or an active control speech training program. (lu.se)
Equivalent1
- Thus from a practical point of view, reducing circuit size in presence of these gates is no longer equivalent to solving the shortest linear program. (iacr.org)
Equation2
- In this activity, students explore linear equation models for real-life situations. (ti.com)
- The Programm can do invers matrix, Gauss-Jordan for solving linear Equation systems, and linear optimization using the Simplexalgorithm. (ticalc.org)
Describe2
- In the first part of the course, we identify and review the main types of recirculating linear accelerators and describe specific examples of each, including microtrons, CEBAF-type accelerators, and energy-recovering linacs. (fnal.gov)
- In this report, we describe the application of MALDI-TOF MS, as well as statistical analysis including linear discriminant and random forest analysis, to 16 medically relevant strains from four species of mycobacteria, M. tuberculosis, M. avium, M. intracellulare, and M. kansasii. (cdc.gov)
Correlation1
- In repeated measures correlation a positive linear, non-significant relationship between gait speed and putamen volume was revealed. (lu.se)
Optimize3
- Hence when linear circuits are synthesized by logic compilers (with specific instructions to optimize for area), most of them would return a solution circuit containing both 2 and 3-input xor gates. (iacr.org)
- In this study, we proposed the integration between the hydroclimatic monitoring system and a linear programming method to optimize the agricultural net benefit considering the scenarios of rainfall delay or reduction. (mdpi.com)
- In linear programming, decision variables represent the unknowns we seek to optimize. (gurobi.com)
Formulate1
- In this article, we will explore how to formulate and solve the RAP using linear programming techniques and the Gurobi Python API. (gurobi.com)
Evaluate3
- To evaluate reach, we estimated the percentage of eligible veterans who participated in the program and their representativeness. (cdc.gov)
- Secondary aims were to evaluate effectiveness in subgroups and assess implementation and adoption of the program. (cdc.gov)
- To evaluate the effectiveness of the collective procedures (CPs = oral health educational and preventive annual programs developed in public schools by the public health system) for preventing dental caries. (bvsalud.org)
Approach1
- The best are given by a linear programming approach (now improved to a semi-definite programming approach) given by Delsarte in the late 70s. (mathoverflow.net)
System4
- Linear LLC, a provider of residential low-voltage wiring systems and electronic components, announces a new incentive program for installers and contractors to promote its DMC-10 intercom system. (securitysales.com)
- Linear will offer the DMC-10 Model Home Protection from Nov. 1 to Dec. 31, during which qualified contractors and dealers will be eligible to receive an unlimited number of free intercom system kits. (securitysales.com)
- 2) Simultaneous linear inequations in two variables : Since the solution set of a system of simultaneous linear inequations is the set of all points in two dimensional space which satisfy all the inequations simultaneously. (slideshare.net)
- The WTC Health Program uses a multilayer administrative claims system to process members' authorized program health benefits. (cdc.gov)
Results2
- presented their results on shortest linear programs for the linear layers of several well known block ciphers in literature. (iacr.org)
- We conducted an evaluation of the program in 1 of the 21 regional VA networks and used the RE-AIM framework (reach, effectiveness, adoption, implementation, and maintenance) for organizing our analysis, results, and interpretation, focusing mainly on reach and effectiveness (9). (cdc.gov)
Python1
- The programming environment will be python (Jupyter notebook) together with the deep learning libraries Keras and Tensorflow. (lu.se)
Finds1
- This program finds the dual of a linear program. (ticalc.org)
Egison5
- An interpreter for Egison, the programming langugage that realized non-linear pattern-matching against unfree data types. (haskell.org)
- The following code is the program that determines poker-hands written in Egison. (haskell.org)
- An interpreter for Egison, a **pattern-matching-oriented**, purely functional programming language. (haskell.org)
- We can do non-linear pattern-matching against non-free data types in Egison. (haskell.org)
- Egison makes programming dramatically simple. (haskell.org)
Variables2
- The error rate for classification of individual strains using linear discriminant analysis was 0.053 using 37 m/z variables, whereas the error rate for classification of individual strains using random forest analysis was 0.023 using only 18 m/z variables. (cdc.gov)
- Sums and linear combination of random variables. (lu.se)
Optimal5
- The optimal LFP solution procedure is illustrated with numerical examples and also by a computer program. (scirp.org)
- If EP or EN has an optimal solution and other is inconsistence, then LFP also has an optimal solution. (scirp.org)
- We propose a flexible gradient-based framework for learning linear programs from optimal decisions. (neurips.cc)
- In some applications, linear programs must instead be learned from observations of optimal decisions. (neurips.cc)
- We also address challenges specific to learning linear programs, such as empty feasible regions and non-unique optimal decisions. (neurips.cc)
Interventions2
- Participants in intensive lifestyle interventions such as those tested in the Diabetes Prevention Program and the Look Ahead trials achieved clinically significant weight loss (6,7). (cdc.gov)
- Translating these successful interventions into programs that can be disseminated widely and implemented in clinical and community settings is a key to reducing the prevalence of obesity. (cdc.gov)
General1
- This framework emphasizes that for a program to be effective in the general population, evaluation of components other than efficacy is important. (cdc.gov)
Graph1
- 1) Graph of linear inequations (i) Linear inequation in one variable : etc. are called linear inequations in one variable. (slideshare.net)
Framework1
- In previous work, we established a linear programming framework to determine sensor location from measured link distances between neighboring nodes in a network. (nist.gov)
Characteristics3
- Finally, the games exhibit linear characteristics and can be solved using bilevel linear programming. (units.it)
- Little is known about the proportion of eligible VA patients ("candidates") who participate in the program, the characteristics of participants, or the program's effectiveness. (cdc.gov)
- This study aimed to know the nutritional status of women from Maranhão, assisted by the Family Health Strategy (FHS) program, aiming at proposals that are consistent with their social characteristics. (bvsalud.org)
Data2
- For this purpose, first a linear programming optimization model is selected and then three data files defining the various food items with their corresponding cost and nutrition elements appropriate to three distinct age groups are constructed. (scirp.org)
- We extracted data on program involvement, demographics, medical conditions, and outcomes from VA administrative databases in 4 Western states. (cdc.gov)
Exercises1
- These materials keep the learner engaged by intertwining text, videos, exercises, and programming activities in consumable chunks. (freecomputerbooks.com)
Basic1
- Basic programming knowledge. (lu.se)
Successfully1
- The Q_LM package was successfully tested by evaluating the performance of individual programs versus that of analogous programs run on a mainframe computer. (cdc.gov)
