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This book offers essential information on the life and career of the recently deceased Giorgio P. Szegö, particularly his important contributions in various areas of mathematical programming and applications to financial markets. It highlights the developments in the fields of stability theory and dynamical systems brought about by his work in the early 1960s and 1970s, then moves on to address his valuable contributions to portfolio theory in the late 1970s and early 1980s, and, finally, examines his work in the field of risk management and the role of financial regulation in the late 1990s. The book explores Giorgio P. Szegö’s contributions in diverse research areas ranging from global optimization, theory of stability and dynamical systems to applications of financial mathematics to portfolio theory, risk measurement and financial regulation. It also covers his consulting work for such major international institutions as the IMF, World Bank and OECD.
This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 14, held in Athens, Greece, in May 2020. The 37 full papers presented together with one invited paper have been carefully reviewed and selected from 75 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components. Due to the COVID-19 pandemic, LION 14 was not held as a physical meeting.
This book constitutes the thoroughly refereed pChania, Crete, Greece, in May 2019. The 38 full papers presented have been carefully reviewed and selected from 52 submissions. The papers focus on advancedresearch developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence and describe advanced ideas, technologies, methods, and applications in optimization and machine learning.
This book gathers the contributions of the international conference “Optimization and Decision Science” (ODS2018), which was held at the Hotel Villa Diodoro, Taormina (Messina), Italy on September 10 to 13, 2018, and was organized by AIRO, the Italian Operations Research Society, in cooperation with the DMI (Department of Mathematics and Computer Science) of the University of Catania (Italy). The book offers state-of-the-art content on optimization, decisions science and problem solving methods, as well as their application in industrial and territorial systems. It highlights a range of real-world problems that are both challenging and worthwhile, using models and methods based on continuous and discrete optimization, network optimization, simulation and system dynamics, heuristics, metaheuristics, artificial intelligence, analytics, and multiple-criteria decision making. Given its scope of coverage, it will benefit not only researchers and practitioners working in these areas, but also the operations research community as a whole.
This book constitutes the refereed proceedings of the 17th International Conference on Learning and Intelligent Optimization, LION-17, held in Nice, France, during June 4–8, 2023. The 40 full papers presented have been carefully reviewed and selected from 83 submissions. They focus on all aspects of unleashing the potential of integrating machine learning and optimization approaches, including automatic heuristic selection, intelligent restart strategies, predict-then-optimize, Bayesian optimization, and learning to optimize.
Patient engagement should be envisaged as a key priority today to innovate healthcare services delivery and to make it more effective and sustainable. The experience of engagement is a key qualifier of the exchange between the demand (i.e. citizens/patients) and the supply process of healthcare services. To understand and detect the strategic levers that sustain a good quality of patients’ engagement may thus allow not only to improve clinical outcomes, but also to increase patients’ satisfaction and to reduce the organizational costs of the delivery of services. By assuming a relational marketing perspective, the book offers practical insights about the developmental process of patients’ engagement, by suggesting concrete tools for assessing the levels of patients’ engagement and strategies to sustain it. Crucial resources to implement these strategies are also the new technologies that should be (1) implemented according to precise guidelines and (2) designed according to a user-centered design process. Furthermore, the book describes possible fields of patients’ engagement application by describing the best practices and experiences matured in different fields
This book constitutes the thoroughly refereed post-conference proceedings of the 11th International Conference on Learning and Intelligent Optimization, LION 11, held in Nizhny,Novgorod, Russia, in June 2017. The 20 full papers (among these one GENOPT paper) and 15 short papers presented have been carefully reviewed and selected from 73 submissions. The papers explore the advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning.
This book contains 112 papers selected from about 250 submissions to the 6th World Congress on Global Optimization (WCGO 2019) which takes place on July 8–10, 2019 at University of Lorraine, Metz, France. The book covers both theoretical and algorithmic aspects of Nonconvex Optimization, as well as its applications to modeling and solving decision problems in various domains. It is composed of 10 parts, each of them deals with either the theory and/or methods in a branch of optimization such as Continuous optimization, DC Programming and DCA, Discrete optimization & Network optimization, Multiobjective programming, Optimization under uncertainty, or models and optimization methods in a specific application area including Data science, Economics & Finance, Energy & Water management, Engineering systems, Transportation, Logistics, Resource allocation & Production management. The researchers and practitioners working in Nonconvex Optimization and several application areas can find here many inspiring ideas and useful tools & techniques for their works.
This book constitutes the refereed proceedings of 6 international workshops held in conjunction with the 4th International Conference on Business Process Management, BPM 2006, in Vienna, Austria in September 2006. The 40 revised full papers presented were carefully reviewed and selected from a total of 94 overall submissions to six international workshops.