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Vector Optimization Theory Applications and Extensions

Vector Optimization Theory Applications and Extensions
2018-04-15 上传大小:2.36MB
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Topology optimization-Theory, Methods and Applications

Topology optimization-Theory, Methods and Applications

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Global Optimization Algorithms--Theory and Application

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convex optimization高清PDF版

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Combinatorial-Optimization-Theory-and-Algorithm

这是关于组合优化算法的电子书,高清,最新版本,经典著作,英文版

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Optimization by vector space methods

Optimization by vector space methods

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Optimization Theory and Methods_Nonlinear Programming

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《Theory of Convex Optimization for Machine Learning》2015版

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Non-convex Optimization for Machine Learning.pdf

机器学习中,常用优化算法,采用的是凸函数优化。随着深度学习的发展,在深度学习等训练是,经常涉及到非凸函数优化问题,该书做了相关的研究和说明。

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Convex Optimization pdf

凸优化,有助于理解SVM中的对偶问题,证明位于Page-234, 5.3.2节

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Practical Optimization:Algorithms and Engineering Application

讲解各种实用优化算法的经典书籍,理论深入浅出,通俗易懂,工程应用举例贴切,附带伪代码 文字版,非常清晰

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Modeling and Optimization Theory and Applications

Stochastic Decision Problems with Multiple Risk-Averse Agents;Optimal Packing of General Ellipses in a Circle;Column Generation Approach to the Convex Recoloring Problem on a Tree;A Variational Inequality Formulation of a Migration Model with Random Data;Identification in Mixed Variational Problems by Adjoint Methods with Applications;

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Convex Optimization - Algorithms and Complexity

Convex Optimization - Algorithms and Complexity Sébastien Bubeck Theory Group, Microsoft Research

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convex optimization theory_bersekas

经典书籍不用多介绍,优化大师BertSekas经典力作,凸优化理论,包含最新版的第6章算法部分

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Optimization has been expanding in all directions at an astonishing rate during the last few decades. New algorithmic and theoretical techniques have been developed, the diffusion into other disciplines has proceeded at a rapid pace, and our knowledge of all aspects of the field has grown even more profound. At the same time, one of the most striking trends in optimization is the constantly increasing emphasis on the interdisciplinary nature of the field. Optimization has been a basic tool in all areas of applied mathematics, engineering, medicine, economics and other sciences.

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DIMITRI BERTSEKAS_Convex Optimization Theory_solutions

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Machine Learning, Optimization,Big Data_Third International Conference, MOD 2017

MOD is an international conference embracing the fields of machine learning, opti- mization, and data science. The third edition, MOD 2017, was organized during September 14–17, 2017 in Volterra (Pisa, Italy), a stunning medieval town dominating the picturesque countryside of Tuscany. The key role of machine learning, reinforcement learning, artificial intelligence, large-scale optimization, and big data for developing solutions to some of the greatest challenges we are facing is undeniable. MOD 2017 attracted leading experts from the academic world and industry with the aim of strengthening the connection between these institutions. The 2017 edition of MOD represented a great opportunity for professors, scientists, industry experts, and postgraduate students to learn about recent developments in their own research areas and to learn about research in contiguous research areas, with the aim of creating an environment to share ideas and trigger new collaborations. As chairs, it was an honor to organize a premiere conference in these areas and to have received a large variety of innovative and original scientific contributions. During this edition, six plenary lectures were presented: Yi-Ke Guo, Department of Computing, Faculty of Engineering, Imperial College London, UK. Founding Director of Data Science Institute Panos Pardalos, Department of Systems Engineering, University of Florida, USA. Director of the Center for Applied Optimization Ruslan Salakhutdinov, Machine Learning Department, School of Computer Science at Carnegie Mellon University, USA. Director of AI Research at Apple My Thai, Department of Computer and Information Science and Engineering, University of Florida, USA Jun Pei, Hefei University of Technology, China Vincenzo Sciacca, Cloud and Cognitive Division – IBM Rome, Italy There were also two tutorial speakers: Domenico Talia, Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica Università della Calabria, Italy Xin–She Yang, School of Science and Technology – Middlesex University London, UK Moreover, the conference hosted the second edition of the industrial session on “Machine Learning, Optimization and Data Science for Real-World Applications”: Luca Maria Aiello, Nokia Bell Labs, UK Pierpaolo Basile, University of Bari, Italy Carlos Castillo, Universitat Pompeu Fabra in Barcelona, Spain Moderator: Aris Anagnostopoulos, Sapienza University of Rome, Italy We received 126 submissions from 46 countries and five continents; each manu- script was independently reviewed by a committee formed by at least five members through a blind review process. These proceedings contain 49 research articles written by leading scientists in the fields of machine learning, artificial intelligence, rein- forcement learning, computational optimization, and data science presenting a sub- stantial array of ideas, technologies, algorithms, methods, and applications. For MOD 2017, Springer generously sponsored the MOD Best Paper Award. This year, the paper by Khaled Sayed, Cheryl Telmer, Adam Butchy, and Natasa Miskov-Zivanov titled “Recipes for Translating Big Data Machine Reading to Exe- cutable Cellular Signaling Models” received the MOD Best Paper Award. This conference could not have been organized without the contributions of these researchers, and so we thank them all for participating. A sincere thank you also goes to all the Program Committee, formed by more than 300 scientists from academia and industry, for their valuable work of selecting the scientific contributions. Finally, we would like to express our appreciation to the keynote speakers, tutorial speakers, and the industrial panel who accepted our invitation, and to all the authors who submitted their research papers to MOD 2017.

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An introduction to Optimization 最优化导论_第四版_中文版

最优化导论第四版中文PDF,内容清晰,属于完整版,是机器学习算法,最优化不可多得的好书

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numerical optimization

Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.

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