Titlebook: Nonlinear Optimization; Methods and Applicat H. A. Eiselt,Carl-Louis Sandblom Textbook 2019 Springer Nature Switzerland AG 2019 Determinist

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書(shū)目名稱Nonlinear Optimization影響因子(影響力)




書(shū)目名稱Nonlinear Optimization影響因子(影響力)學(xué)科排名




書(shū)目名稱Nonlinear Optimization網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Nonlinear Optimization網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Nonlinear Optimization被引頻次




書(shū)目名稱Nonlinear Optimization被引頻次學(xué)科排名




書(shū)目名稱Nonlinear Optimization年度引用




書(shū)目名稱Nonlinear Optimization年度引用學(xué)科排名




書(shū)目名稱Nonlinear Optimization讀者反饋




書(shū)目名稱Nonlinear Optimization讀者反饋學(xué)科排名

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Textbook 2019 continuous optimization. It begins with a summary of classical results on unconstrained optimization, followed by a wealth of applications from a diverse mix of fields, e.g. location analysis, traffic planning, and water quality management, to name but a few. In turn, the book presents a formal des

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0884-8289 on techniques.Each method is formally described, and then fuThis book provides a comprehensive introduction to nonlinear programming, featuring a broad range of applications and solution methods in the field of continuous optimization. It begins with a summary of classical results on unconstrained o

敲竹杠

Introduction,near functions; first in the one-dimensional case, and then for functions of several variables. The third and final section of this chapter introduces the concept of convexity and relates it to the study of optimization.

agnostic

Optimality Conditions and Duality Theory,ents the foundation for the development of algorithms which will be introduced in the following chapters. Apart from their intrinsic relevance, some of these results also provide useful information about the sensitivity of an optimal solution.

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Psa617

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Methods for Linearly Constrained Problems,more specialized techniques for more specialized problems. Consequently, for a given problem one should select a method (covered in the previous, this, or the next two chapters) from a box as high up in Table 6.1 as possible. The third section considers problems in which the objective function is quadratic, but concave, a difficult case.

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