Online Optimization is a methodology that has arisen out of computationally oriented problems that have the following characteristics: (1) the decision or solution needs to be made very quickly, (2) significant information or data is missing from the problem, (3) typically problems deal with the uncertainty of a future event or aspect, (4) and the problems are dynamical in nature. Moreover, the emphasis here on the word "online" reflects the foregoing characteristics of problems in which solutions/decisions must be made quickly-many times in "real time"-and with limited information or data. This is a class of problems that can occur in any of the branches of optimization: linear, nonlinear, combinatorial, and stochastic.

ONLINE OPTIMIZATION's objective is to provide a systematic survey of the methodology. From the methodological survey, the book then covers a variety of applications of online optimization methods in the domain of Operations Research and Management Science. These applications include a range of problem types, which include the multiple scheduling complex transportation systems, optimizing financial decision problems in "real time", and complex production problems of all sorts (e.g., whether costs should be reduced or profits should be maximized or scarce resources should be used wisely, etc.). With online optimization the issue of incomplete data is an essential aspect of the scientific challenge. Hence, how well online algorithms can perform and how one can guarantee solution quality-even without knowing all data in advance-are the primary challenges of the online optimization methodology.