2021년 2월 17일 (수) 08:18에 Pythagoras0님의 편집

2021-02-17T08:18:11Z

Pythagoras0: /* 메타데이터 */ 새 문단

2020-12-26T12:23:01Z

메타데이터: 새 문단

Pythagoras0: /* 노트 */ 새 문단

2020-12-21T09:06:38Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q141495 Q141495]
===말뭉치===
# The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics.<ref name="ref_fc9c24eb">[https://en.wikipedia.org/wiki/Mathematical_optimization Mathematical optimization]</ref>
# Optimization problems are often expressed with special notation.<ref name="ref_fc9c24eb" />
# 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.<ref name="ref_fc9c24eb" />
# Adding more than one objective to an optimization problem adds complexity.<ref name="ref_fc9c24eb" />
# Mathematical programming allows you to capture the key features of a complex real-world problem as an optimization model.<ref name="ref_677066e6">[https://www.gurobi.com/resources/mathematical-optimization-web-page/ What is Mathematical Optimization?]</ref>
# 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.<ref name="ref_677066e6" />
# New York ISO uses optimization to choose the most cost-effective way to deliver electricity to customers.<ref name="ref_677066e6" />
# Betterment uses optimization to choose the optimal mix of assets, which maximize after-tax returns while minimizing risk.<ref name="ref_677066e6" />
# Optimization techniques represent analytical tools available to the researcher in his search for the best possible solution to a particular problem.<ref name="ref_ca4fdb52">[https://www.sciencedirect.com/science/article/pii/S0022354915376048 Mathematical Optimization Techniques in Drug Product Design and Process Analysis]</ref>
# Pharmaceutical product and process design problems were structured as constrained optimization problems and subsequently solved by the Lagrangian method of optimization.<ref name="ref_ca4fdb52" />
# You'll start with a solid foundation in math, including combinatorics, linear optimization, modeling, scheduling, forecasting, decision theory, and computer simulation.<ref name="ref_b36e490b">[https://uwaterloo.ca/future-students/programs/mathematical-optimization Mathematical Optimization]</ref>
# Apply to Mathematics and choose Mathematical Optimization as your major.<ref name="ref_b36e490b" />
# What can you do with a degree in Mathematical Optimization?<ref name="ref_b36e490b" />
# Waterloo Mathematical Optimization graduates commonly pursue careers in software development, business analysis, and operations.<ref name="ref_b36e490b" />
# This course treats two different areas of optimization: nonlinear optimization and combinatorial optimization.<ref name="ref_03ba93f1">[https://www.maastrichtuniversity.nl/meta/393504/mathematical-optimization Mathematical Optimization]</ref>
# Combinatorial optimization deals with situations that a best solution from a finite number of available solutions must be chosen.<ref name="ref_03ba93f1" />
# Mathematical optimization is the process of maximizing or minimizing an objective function by finding the best available values across a set of inputs.<ref name="ref_52b74536">[https://deepai.org/machine-learning-glossary-and-terms/mathematical-optimization Mathematical Optimization]</ref>
# Some variation of optimization is required for all deep learning models to function, whether using supervised or unsupervised learning.<ref name="ref_52b74536" />
# 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).<ref name="ref_52b74536" />
# The course treats selected topics in convexity, optimization and matrix theory.<ref name="ref_cc830bc6">[https://www.uio.no/studier/emner/matnat/math/MAT9120/index.html MAT9120 – Mathematical Optimization]</ref>
# Possible topics include: combinatorial optimization, combinatorial matrix theory, convex analysis, and convex optimization.<ref name="ref_cc830bc6" />
# Research in optimization involves the analysis of such mathematical problems and the design of efficient algorithms for solving them.<ref name="ref_9c677069">[https://engineering.jhu.edu/ams/operations-research-optimization/ Operations Research & Optimization]</ref>
# Optimization technologies are shining examples of how deep mathematical techniques help to provide concrete computational tools for solving a diverse suite of problems.<ref name="ref_9c677069" />
# For more details about our members, research, and course offerings in operations research and optimization, please explore the additional tabs.<ref name="ref_9c677069" />
# What you maybe do not know is that google is actually representing and solving your query as an optimization problem.<ref name="ref_5d780b2f">[https://www.sintef.no/en/digital/applied-mathematics/optimization/mathematical-optimization/ Mathematical optimization]</ref>
# There are several classifications of mathematical optimization problems, depending on the form of the objectives and of the constraints.<ref name="ref_5d780b2f" />
# If S is finite, then we have a combinatorial optimization problem also called discrete optimization problem.<ref name="ref_5d780b2f" />
# In online optimization, problem data arrives piecewise (online) and needs to be processed before the full input is known.<ref name="ref_02023b29">[https://www.uni-siegen.de/fb6/aan/optimierung/ Mathematische Optimierung / Mathematical Optimization]</ref>
# The usage of factorial experiments combined with mathematical optimization is a novel approach to address supply chain network design problems.<ref name="ref_e65e63d7">[https://www.hindawi.com/journals/complexity/2018/8731027/ Combined Use of Mathematical Optimization and Design of Experiments for the Maximization of Profit in a Four-Echelon Supply Chain]</ref>
# 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.<ref name="ref_e65e63d7" />
# 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.<ref name="ref_e65e63d7" />
# To the best of our knowledge, there are few studies that combine DOE and optimization to solve supply chain design problems.<ref name="ref_e65e63d7" />
# In optimization, one characterizes values of decision variables in order to satisfy an objective subject to a given set of constraints.<ref name="ref_e601b1d9">[https://engineering.lehigh.edu/ise/research/research-directions/mathematical-optimization P.C. Rossin College of Engineering & Applied Science]</ref>
# In mathematical optimization, the objective and constraints are given as models of real-world phenomena.<ref name="ref_e601b1d9" />
# Optimization problems often exhibit rich structures that can be leveraged when one seeks solutions or characterizations thereof.<ref name="ref_e601b1d9" />
# At Lehigh ISE, we investigate a wide spectrum of challenging optimization problems for which we also develop, analyze, and implement efficient and reliable algorithms.<ref name="ref_e601b1d9" />
# Yet without real-time mathematical optimization, this is exactly what could happen.<ref name="ref_329a83e3">[https://www.riverlogic.com/blog/algorithms-mathematical-optimization-and-your-business Algorithms, mathematical optimization & Business: Part V]</ref>
# That's where algorithms and mathematical optimization come into play.<ref name="ref_329a83e3" />
# Proper mathematical optimization can predict a negative trend before it wreaks havoc on those managing operations.<ref name="ref_329a83e3" />
# 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.<ref name="ref_329a83e3" />
# Optimization modeling is a form of mathematics that attempts to determine the optimal maximin or minimum value of a complex equation.<ref name="ref_e580a465">[https://www.riverlogic.com/blog/optimization-modeling-everything-you-need-to-know Optimization Modeling: Everything You Need to Know]</ref>
# It's tempting to start dabbling with optimization modeling using one of the many Excel solver add-ins.<ref name="ref_e580a465" />
# It has numerous libraries available to help perform optimization and modeling.<ref name="ref_e580a465" />
# Given time and resources, Python can be used to create highly complex optimization models with large numbers of constraints and variables.<ref name="ref_e580a465" />
# The Mathematical and Resource Optimization program supports basic research in optimization — focusing on the development of theory and algorithms for large-scale optimization problems.<ref name="ref_afbc833e">[https://www.onr.navy.mil/en/Science-Technology/Departments/Code-31/All-Programs/311-Mathematics-Computers-Research/mathematical-optimization Mathematical and Resource Optimization]</ref>
# Innovative strategies for dealing with uncertainty from stochastic optimization, robust optimization, and simulation-based optimization are of growing interest.<ref name="ref_afbc833e" />
# 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.<ref name="ref_52ae2135">[https://community.apan.org/wg/afosr/w/researchareas/7669/mathematical-optimization Mathematical Optimization]</ref>
===소스===
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← 이전 판		2020년 12월 26일 (토) 12:23 판
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		+	* ID : [https://www.wikidata.org/wiki/Q141495 Q141495]

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2021년 2월 17일 (수) 08:18에 Pythagoras0님의 편집

Pythagoras0: /* 메타데이터 */ 새 문단

Pythagoras0: /* 노트 */ 새 문단