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Introduction to Online Convex Optimization
[article]
<span title="2021-12-19">2021</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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<span title="2019-09-07">2019</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.05207v1">arXiv:1909.05207v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k2msh7silng2dhrybxf7jao5xi">fatcat:k2msh7silng2dhrybxf7jao5xi</a> </span>
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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
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.05207v2">arXiv:1909.05207v2</a>
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... rominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.
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