15.099 2003年秋季学期主题:(连续性)确定性最优化的随机方法
为了与过去20年左右的传统保持一致,最优化书报讨论课程的重点放在麻省理工学院一部分最优化团体感兴趣的一个高级主题上。和这个讨论会过去的很多形式一样,我们常常选择一个最优化团体目前的兴趣点(可能甚至是“热点”)作为主题,或者选择一个在实际中引起讨论的话题。确定性最优化的随机方法符合第一条标准(如果不同时符合第二条标准的话)。
传统的最优化算法中迭代的计算和分析具有确定性,与之相比,随机方法依靠随机过程和随机数字/向量的生成作为算法和(或)算法分析的一部分。例如,对于单纯型法,人们可以根据随机过程,通过选择引入列和整个基数的方法开发出单纯型法的随机版本。对于更多一般的非线性最优化问题,简单的随机算法会根据随机选择的向量为搜索方向;当然,向量的分布可能和迭代相关。有一些关于凸型和非凸型最优化的非常复杂的随机算法,它们依赖于最近研究出的一些方法,在凸型集合中近似地产生平均分布的随机变量。根据复杂度分析,很多随机算法具有非常好的特性:它们可以在多项式时间内解决问题,并达到高正确度和高概率;或者它们期望的运行时间是多项式的。当然,这些表述是模糊的,直到例如我们给“高概率”一个定义。在这个讨论会中,我们将会研读关于这个主题的一些新近的文章,其中很多作者是麻省理工学院的老师,同时我们也会研读已有文献中的一些旧文章,这些文章直到现在才引起人们的关注。
讨论会形式
讨论会每周一次,每次两小时。每一堂课中,一个或以上的学生或者一个教师会领导讨论。通常每次课会涉及文献(发表的或仍在研究中的论文)中的一至两篇文章。领导讨论的学生负责准备达到学术会议标准的陈述报告(和投影资料),还有课堂需要的幻灯片讲义。因此,本课程也会提供一个友好的场景,以发展陈述技巧和实践教学。课程希望能成为学生和老师之间讨论和互动的非正式论坛,在每人都出一分力下可以做到最好。
(确实,过去在讨论会“充满生气”和一些人通过提出意见参与到讨论领导者的陈述报告中时,讨论会是最成功的。)因为我们是在共同探讨一个高级题目,讨论领导者无需觉得他(她)需要完全掌握所有报告内容——他们常常会带着某个技术点上的问题来到课堂,带动最活跃的讨论和最有效的学习。
评分
这是一个高级讨论会,所以对学生表现评分和区别并不是非常重要(至少对我来讲是这样)。诚实努力去做好报告,并且尽了自己最大能力的学生将会得到好成绩(A)。
课时范围
因为这是非正式讨论会,我们不会遵循固定的时间表。如果我们正讨论一篇文章,并发现需要更多时间来继续,我们通常会把讨论延长到下一次课并且顺延后面的讨论。或者,如果有人发现有一些可能我们想讨论的新材料,我们也会加入我们的安排中。教学时程中有课时大纲。
15.099 Topic for Fall 2003: Randomized Methods for (Continuous) Deterministic Optimization
In keeping with the tradition of the last twenty-some years, the Readings in Optimization seminar will focus on an advanced topic of interest to a portion of the MIT optimization community. As has been the case for many previous versions of this seminar, we often choose a topic that is of current interest (perhaps even "hot") in the optimization community, or that is topical in practice. Randomized methods for deterministic optimization certainly meets the first criterion (if not the second as well).
In contrast to conventional optimization algorithms whose iterates are computed and analyzed deterministically, randomized methods rely on stochastic processes and random number/vector generation as part of the algorithm and/or its analysis. Consider the simplex method, for example. One can develop a randomized version of the simplex method by choosing incoming columns or entire bases according to some stochastic process. For more general nonlinear optimization problems, a simple randomized algorithm would be to base the search direction on a randomly chosen vector; the distribution of the vector might be iteration-dependent, of course. There are very sophisticated randomized algorithms for convex and non-convex optimization that rely on recently-developed methods for approximately generating uniformly distributed random variables on convex sets. In terms of complexity analysis, many randomized algorithms have excellent properties: they solve problems to a high accuracy in polynomial time with high probability or their expected running time is polynomial. Of course, these sorts of statements are vague until we define "high probability", etc. In the seminar, we will study some very recent papers on this topic, many by MIT faculty, as well as some older papers from the existing literature that are only now receiving attention.
Seminar Format
The seminar will meet once a week for two hours. In each session, one or more students or a faculty member will have the responsibility to lead the discussion. Typically, each session will cover one or two papers from the literature (published or working papers). Student discussion leaders have responsibility for preparing a conference quality presentation (with overheads) and for preparing a handout of transparencies for the class. Therefore, the seminar also provides a friendly setting for developing presentation skills and practicing teaching. The seminar is intended to be an informal forum for discussion and for interaction among the student and faculty participants, and works best when everyone contributes. (Indeed, in the past the seminar has been most successful when it is "alive" and several individuals are adding to the discussion leader's presentation by suggesting material on the board.) Since we are collectively exploring an advanced topic, the discussion leader need not feel that he or she must have a complete grasp of everything being presented — often coming to class with questions about a technical point leads to the most lively discussion and the greatest learning.
Grades
Since this is an advanced seminar, grading and distinguishing between student performance is not very important (at least to me). Students who make an honest effort to make good presentations and to contribute to the best of their ability will receive a good grade (an A).
Session Coverage
Since this is an informal seminar, we do not typically keep to any fixed schedule. If we find that we need more time for a paper once we are discussing it, then we will typically extend the discussion to the next session and delay subsequent sessions. Or, if someone discovers some new material that we might like to discuss, then we might add that to the agenda. The calendar provides an outline of the coverage for the course sessions.