This manuscript portrays optimization as a process. In many practical
applications the environment is so complex that it is infeasible to lay out a
comprehensive theoretical model and use classical algorithmic theory and
mathematical optimization. It is necessary as well as beneficial to take a
robust approach, by applying an optimization method that learns as one goes
along, learning from experience as more aspects of the problem are observed.
This view of optimization as a process has become prominent in varied fields
and has led to some spectacular success in modeling and systems that are now
part of our daily lives.

本文研究将优化视为一种过程，强调学习方法的应用，提倡使用鲁棒性的算法并学习实践经验以应对实际应用中复杂环境下的问题。

在线凸优化简介

Introduction to Online Convex Optimization

Locally-biased graph algorithms are algorithms that attempt to find local or
small-scale structure in a large data graph. In some cases, this can be
accomplished by adding some sort of locality constraint and calling a
traditional graph algorithm; but more interesting are locally-biased graph
algorithms that compute answers by running a procedure that does not even look
at most of the input graph. This corresponds more closely to what practitioners
from various data science domains do, but it does not correspond well with the
way that algorithmic and statistical theory is typically formulated. Recent
work from several research communities has focused on developing locally-biased
graph algorithms that come with strong complementary algorithmic and
statistical theory and that are useful in practice in downstream data science
applications. We provide a review and overview of this work, highlighting
commonalities between seemingly-different approaches, and highlighting
promising directions for future work.

本文综述了最近几年来几个研究社区对局部偏差图算法的发展和应用，以及这些算法和算法论和统计理论的相互关系。