최적화 문제

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  1. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics.[1]
  2. Optimization problems are often expressed with special notation.[1]
  3. The term "linear programming" for certain optimization cases was due to George B. Dantzig, although much of the theory had been introduced by Leonid Kantorovich in 1939.[1]
  4. Adding more than one objective to an optimization problem adds complexity.[1]
  5. Mathematical programming allows you to capture the key features of a complex real-world problem as an optimization model.[2]
  6. Mathematical programming technologies are being used by leading companies, often resulting in tens or even hundreds of millions of dollars in cost savings and revenue.[2]
  7. New York ISO uses optimization to choose the most cost-effective way to deliver electricity to customers.[2]
  8. Betterment uses optimization to choose the optimal mix of assets, which maximize after-tax returns while minimizing risk.[2]
  9. Optimization techniques represent analytical tools available to the researcher in his search for the best possible solution to a particular problem.[3]
  10. Pharmaceutical product and process design problems were structured as constrained optimization problems and subsequently solved by the Lagrangian method of optimization.[3]
  11. You'll start with a solid foundation in math, including combinatorics, linear optimization, modeling, scheduling, forecasting, decision theory, and computer simulation.[4]
  12. Apply to Mathematics and choose Mathematical Optimization as your major.[4]
  13. What can you do with a degree in Mathematical Optimization?[4]
  14. Waterloo Mathematical Optimization graduates commonly pursue careers in software development, business analysis, and operations.[4]
  15. This course treats two different areas of optimization: nonlinear optimization and combinatorial optimization.[5]
  16. Combinatorial optimization deals with situations that a best solution from a finite number of available solutions must be chosen.[5]
  17. Mathematical optimization is the process of maximizing or minimizing an objective function by finding the best available values across a set of inputs.[6]
  18. Some variation of optimization is required for all deep learning models to function, whether using supervised or unsupervised learning.[6]
  19. Global optimization locates the maximum or minimum over all available input values, whereas local optimization determines local minima or maxima in a particular sample (subpopulation).[6]
  20. The course treats selected topics in convexity, optimization and matrix theory.[7]
  21. Possible topics include: combinatorial optimization, combinatorial matrix theory, convex analysis, and convex optimization.[7]
  22. Research in optimization involves the analysis of such mathematical problems and the design of efficient algorithms for solving them.[8]
  23. Optimization technologies are shining examples of how deep mathematical techniques help to provide concrete computational tools for solving a diverse suite of problems.[8]
  24. For more details about our members, research, and course offerings in operations research and optimization, please explore the additional tabs.[8]
  25. What you maybe do not know is that google is actually representing and solving your query as an optimization problem.[9]
  26. There are several classifications of mathematical optimization problems, depending on the form of the objectives and of the constraints.[9]
  27. If S is finite, then we have a combinatorial optimization problem also called discrete optimization problem.[9]
  28. In online optimization, problem data arrives piecewise (online) and needs to be processed before the full input is known.[10]
  29. The usage of factorial experiments combined with mathematical optimization is a novel approach to address supply chain network design problems.[11]
  30. It is worthy to mention that optimization models can be solved using different techniques depending on the computational complexity of the model and the instance.[11]
  31. The most direct approach is using commercial software that applies mathematical programming, which is limited to solving small or medium size instances for NP-hard problems.[11]
  32. To the best of our knowledge, there are few studies that combine DOE and optimization to solve supply chain design problems.[11]
  33. In optimization, one characterizes values of decision variables in order to satisfy an objective subject to a given set of constraints.[12]
  34. In mathematical optimization, the objective and constraints are given as models of real-world phenomena.[12]
  35. Optimization problems often exhibit rich structures that can be leveraged when one seeks solutions or characterizations thereof.[12]
  36. At Lehigh ISE, we investigate a wide spectrum of challenging optimization problems for which we also develop, analyze, and implement efficient and reliable algorithms.[12]
  37. Yet without real-time mathematical optimization, this is exactly what could happen.[13]
  38. That's where algorithms and mathematical optimization come into play.[13]
  39. Proper mathematical optimization can predict a negative trend before it wreaks havoc on those managing operations.[13]
  40. Without algorithms that enable mathematical optimization, it's hard to tell whether or not a train should still route to that mine, or head to the company's other location.[13]
  41. Optimization modeling is a form of mathematics that attempts to determine the optimal maximin or minimum value of a complex equation.[14]
  42. It's tempting to start dabbling with optimization modeling using one of the many Excel solver add-ins.[14]
  43. It has numerous libraries available to help perform optimization and modeling.[14]
  44. Given time and resources, Python can be used to create highly complex optimization models with large numbers of constraints and variables.[14]
  45. The Mathematical and Resource Optimization program supports basic research in optimization — focusing on the development of theory and algorithms for large-scale optimization problems.[15]
  46. Innovative strategies for dealing with uncertainty from stochastic optimization, robust optimization, and simulation-based optimization are of growing interest.[15]
  47. The program goal is the development of mathematical methods for the optimization of large and complex models that will address future decision problems of interest to the U.S. Air Force.[16]

소스

  1. ↑ 1.0 1.1 1.2 1.3 Mathematical optimization
  2. ↑ 2.0 2.1 2.2 2.3 What is Mathematical Optimization?
  3. ↑ 3.0 3.1 Mathematical Optimization Techniques in Drug Product Design and Process Analysis
  4. ↑ 4.0 4.1 4.2 4.3 Mathematical Optimization
  5. ↑ 5.0 5.1 Mathematical Optimization
  6. ↑ 6.0 6.1 6.2 Mathematical Optimization
  7. ↑ 7.0 7.1 MAT9120 – Mathematical Optimization
  8. ↑ 8.0 8.1 8.2 Operations Research & Optimization
  9. ↑ 9.0 9.1 9.2 Mathematical optimization
  10. ↑ Mathematische Optimierung / Mathematical Optimization
  11. ↑ 11.0 11.1 11.2 11.3 Combined Use of Mathematical Optimization and Design of Experiments for the Maximization of Profit in a Four-Echelon Supply Chain
  12. ↑ 12.0 12.1 12.2 12.3 P.C. Rossin College of Engineering & Applied Science
  13. ↑ 13.0 13.1 13.2 13.3 Algorithms, mathematical optimization & Business: Part V
  14. ↑ 14.0 14.1 14.2 14.3 Optimization Modeling: Everything You Need to Know
  15. ↑ 15.0 15.1 Mathematical and Resource Optimization
  16. ↑ Mathematical Optimization

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Spacy 패턴 목록

  • [{'LOWER': 'mathematical'}, {'LEMMA': 'optimization'}]
  • [{'LOWER': 'mathematical'}, {'LEMMA': 'optimisation'}]
  • [{'LOWER': 'mathematical'}, {'LEMMA': 'programming'}]
  • [{'LEMMA': 'optimization'}]
  • [{'LEMMA': 'optimisation'}]
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