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2020년 12월 26일 (토) 05:11 판

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  1. Linear programming, mathematical modeling technique in which a linear function is maximized or minimized when subjected to various constraints.[1]
  2. During World War II, linear programming was used extensively to deal with transportation, scheduling, and allocation of resources subject to certain restrictions such as costs and availability.[1]
  3. Linear programming is the process of taking various linear inequalities relating to some situation, and finding the "best" value obtainable under those conditions.[2]
  4. In "real life", linear programming is part of a very important area of mathematics called "optimization techniques".[2]
  5. Linear programming (LP) is one of the simplest ways to perform optimization.[3]
  6. For some reason, LP doesn’t get as much attention as it deserves while learning data science.[3]
  7. I decided to write an article that explains Linear programming in simple English.[3]
  8. Linear programming is a simple technique where we depict complex relationships through linear functions and then find the optimum points.[3]
  9. Linear programming can be applied to various fields of study.[4]
  10. Industries that use linear programming models include transportation, energy, telecommunications, and manufacturing.[4]
  11. Linear programming is a widely used field of optimization for several reasons.[4]
  12. A number of algorithms for other types of optimization problems work by solving LP problems as sub-problems.[4]
  13. Linear programming techniques are common approaches to solve optimization problems that can be expressed in the standard form given by Eq.[5]
  14. However, any other mixed integer linear programming solver also can be used.[5]
  15. Based on the selected literature (52 papers), LP can be applied to a variety of diet problems, from food aid, national food programmes, and dietary guidelines to individual issues.[6]
  16. Future possibilities lie in finding LP solutions for diets by combining nutritional, costs, ecological and acceptability constraints.[6]
  17. This paper reviews the application of linear programming to optimize diets with nutritional, economic, and environmental constraints.[6]
  18. These results led to upper bounds being added to LP for the first time (10).[6]
  19. The first step in any linear programming problem is to define the variables and the objective function.[7]
  20. Linear programming (LP) is a powerful framework for describing and solving optimization problems.[8]
  21. The set of applications of linear programming is literally too long to list.[8]
  22. The first algorithm for solving linear programming problems was the simplex method, proposed by George Dantzig in 1947.[8]
  23. However, the sheer variety of different LP models, and the many different ways in which LP is used, mean that neither algorithm dominates the other in practice.[8]
  24. There were three models produced by linear programming.[9]
  25. Table 2 shows all nutrients constrains and the food groups of the three different models produced by LP based on the dietary guidelines of WCRF/AICR 2007, MDG 2010 and RNI 2017.[9]
  26. Table 3 shows the three menus produced, according to raw food items at the lowest possible cost based on WCRF/AICR, MDG, RNI and palatability constraints by using LP.[9]
  27. The production of every menu is different from another as it follows the list of food ingredients selected according to the LP models.[9]
  28. A linear programming problem involves constraints that contain inequalities.[10]
  29. The objective function along with the three corner points above forms a bounded linear programming problem.[10]
  30. If this is the case, then you have a bounded linear programming problem.[10]
  31. If a solution exists to a bounded linear programming problem, then it occurs at one of the corner points.[10]
  32. Linear programming is a form of mathematical optimisation that seeks to determine the best way of using limited resources to achieve a given objective.[11]
  33. Before we continue, it's important to note that this article is not intended to be an exhaustive course in linear programming.[11]
  34. This example provides one setting where linear programming can be applied.[11]
  35. A short explanation is given what Linear programming is and some basic knowledge you need to know.[12]
  36. So a linear programming model consists of one objective which is a linear equation that must be maximized or minimized.[12]
  37. It is the usual and most intuitive form of describing a linear programming problem.[12]
  38. See Formulation of an lp problem in lpsolve for a practical example.[12]
  39. If you think about the geometry in the above graph, in any linear optimization problem at least one vertex of the feasible region must be an optimal solution.[13]
  40. Linear programming is a mathematical technique that determines the best way to use available resources.[14]
  41. Note: You can use linear programming only if there is a linear relationship between the variables you're looking at.[14]
  42. To help you understand linear programming, we'll work through an example.[14]
  43. Linear programming software programs can solve the equations quickly and easily, and they provide a great deal of information about the various points within the possible set.[14]
  44. This paper studies the problem of tangible assets acquisition within the company by proposing a new hybrid model that uses linear programming and fuzzy numbers.[15]
  45. Regarding linear programming, two methods were implemented in the model, namely: the graphical method and the primal simplex algorithm.[15]
  46. Solving the primal simplex algorithm using fuzzy numbers and coefficients, allowed the results of the linear programming problem to also be in the form of fuzzy variables.[15]
  47. Linear programming, sometimes known as linear optimization, is the problem of maximizing or minimizing a linear function over a convex polyhedron specified by linear and non-negativity constraints.[16]
  48. Linear programming theory falls within convex optimization theory and is also considered to be an important part of operations research.[16]
  49. Linear programming can be solved using the simplex method (Wood and Dantzig 1949, Dantzig 1949) which runs along polytope edges of the visualization solid to find the best answer.[16]
  50. Linear programming has proven to be an extremely powerful tool, both in modeling real-world problems and as a widely applicable mathematical theory.[17]
  51. Linear Programming (LP) is a mathematical procedure for determining optimal allocation of scarce resources.[17]
  52. LP is a procedure that has found practical application in almost all facets of business, from advertising to production planning.[17]
  53. Linear programming deals with a class of programming problems where both the objective function to be optimized is linear and all relations among the variables corresponding to resources are linear.[17]
  54. Linear programming (LP) is one of the most widely-applied techniques in operations research.[18]
  55. Many methods have been developed and several others are being proposed for solving LP problems, including the famous simplex method and interior point algorithms.[18]
  56. This study was aimed at introducing a new method for solving LP problems.[18]
  57. Linear programming (LP) dates from 1939 when Leonid Kan-tarovich first expressed a problem in economics in linear form (Bazaraa et al., 1998).[18]
  58. As was stated earlier, a linear programming problem that has minimum constraints does not work with the simplex algorithm.[19]
  59. This method is viable for any linear programming problem that does not match the forms of the previous section .[19]
  60. All linear programming problems aim to either maximize or minimize some numerical value representing profit, cost, production quantity, etc.[20]
  61. A feasible solution to the linear programming problem should satisfy the constraints and non-negativity restrictions.[20]
  62. In Mathematics, linear programming is a method of optimising operations with some constraints.[21]
  63. The main objective of linear programming is to maximize or minimize the numerical value.[21]
  64. Linear programming is considered as an important technique which is used to find the optimum resource utilisation.[21]
  65. The term “linear programming” consists of two words such as linear and programming.[21]
  66. To obtain the initial feasible vertex, one can set up another LP problem (called the phase I problem) to which there is always a known feasible vertex, and apply the simplex method to that problem.[22]
  67. Within a few years of its introduction, LP had become a central—perhaps the central—paradigm of operations research.[22]
  68. The Linear Programming FAQ, established by John W. Gregory and maintained for many years by Robert Fourer, was last updated in 2005.[23]
  69. Since the LP FAQ is no longer maintained, the content has been incorporated into the relevant sections of the NEOS Optimization Guide.[23]
  70. The importance of linear programming derives in part from its many applications (see further below) and in part from the existence of good general-purpose techniques for finding optimal solutions.[24]
  71. These techniques take as input only an LP in the above Standard Form, and determine a solution without reference to any information concerning the LP's origins or special structure.[24]
  72. The related problem of integer programming (or integer linear programming, strictly speaking) requires some or all of the variables to take integer (whole number) values.[24]
  73. Industries that make use of LP and its extensions include transportation, energy, telecommunications, and manufacturing of many kinds.[24]
  74. This problem involves the allocation of resources and can be modeled as a linear programming problem as we will discuss.[25]
  75. To model and solve this problem, we can use linear programming.[25]
  76. Modern linear programming was the result of a research project undertaken by the US Department of Air Force under the title of Project SCOOP (Scientific Computation of Optimum Programs).[25]
  77. One of the SCOOP team members, George Dantzig, developed the simplex algorithm for solving simultaneous linear programming problems.[25]
  78. In Section 4, the problem is formulated as a mixed integer linear programming model.[26]
  79. However, even though the proposed model is a MILP model, it can be further transformed into a pure 0-1 linear programming model.[26]

소스

  1. ↑ 1.0 1.1 linear programming | Definition & Facts
  2. ↑ 2.0 2.1 Linear Programming: Introduction
  3. ↑ 3.0 3.1 3.2 3.3 Applications Of Linear Programming
  4. ↑ 4.0 4.1 4.2 4.3 Linear programming
  5. ↑ 5.0 5.1 Linear Programming - an overview
  6. ↑ 6.0 6.1 6.2 6.3 A Review of the Use of Linear Programming to Optimize Diets, Nutritiously, Economically and Environmentally
  7. ↑ F5 Performance Management
  8. ↑ 8.0 8.1 8.2 8.3 Linear Programming (LP)
  9. ↑ 9.0 9.1 9.2 9.3 Diet optimization using linear programming to develop low cost cancer prevention food plan for selected adults in Kuala Lumpur, Malaysia
  10. ↑ 10.0 10.1 10.2 10.3 3.2a. Solving Linear Programming Problems Graphically
  11. ↑ 11.0 11.1 11.2 Solve problems with linear programming and Excel
  12. ↑ 12.0 12.1 12.2 12.3 Linear programming basics
  13. ↑ The Glop Linear Solver
  14. ↑ 14.0 14.1 14.2 14.3 Decision-Making Skills Training from MindTools.com
  15. ↑ 15.0 15.1 15.2 Linear Programming and Fuzzy Optimization to Substantiate Investment Decisions in Tangible Assets
  16. ↑ 16.0 16.1 16.2 Linear Programming -- from Wolfram MathWorld
  17. ↑ 17.0 17.1 17.2 17.3 Linear Optimization
  18. ↑ 18.0 18.1 18.2 18.3 A new algorithm for solving linear programming problems
  19. ↑ 19.0 19.1 Brilliant Math & Science Wiki
  20. ↑ 20.0 20.1 Elements of a Linear Programming Problem (LPP)
  21. ↑ 21.0 21.1 21.2 21.3 Linear Programming (Definition, Characteristics, Method & Example)
  22. ↑ 22.0 22.1 9 Probabilistic Analysis in Linear Programming
  23. ↑ 23.0 23.1 Linear Programming FAQ
  24. ↑ 24.0 24.1 24.2 24.3 Linear Programming FAQ
  25. ↑ 25.0 25.1 25.2 25.3 Modeling and Linear Programming in Engineering Management
  26. ↑ 26.0 26.1 A Mixed Integer Linear Programming Model for Rolling Stock Deadhead Routing before the Operation Period in an Urban Rail Transit Line

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