CS369N: Beyond Worst-Case Analysis Lecture #2: Models of Data in Online Paging
نویسنده
چکیده
∗ c ©2009–2010, Tim Roughgarden. Department of Computer Science, Stanford University, 462 Gates Building, 353 Serra Mall, Stanford, CA 94305. Email: [email protected]. A good general reference for this section and the next is [4, Chapter 3]. A more general model allows arbitrary changes to the cache at every time step, whether or not there is a hit or miss, with the cost incurred equal to the number of changes. We will focus on the stated model, which corresponds to “demand paging” algorithms.
منابع مشابه
CS 369 N : Beyond Worst - Case Analysis Lecture # 8 : Resource Augmentation ∗
We’ve finished our discussion of models of data and now conclude the course with two lectures on novel ways of proving relative approximation guarantees. This lecture is about resource augmentation, where the idea to compare a protagonist (like your algorithm) that is endowed with “extra resources” to an all-powerful opponent that is handicapped by “less resources”. Subject to this, we use stan...
متن کاملCS264: Beyond Worst-Case Analysis Lecture #3: Online Paging and Resource Augmentation
This course covers many different methods of analyzing and comparing algorithms. Periodically, as in Section 2, we pause to review the “big picture” and suggest methods for keeping tracking of the course’s main ideas, their goals, and the problems for which they are most likely to be useful. Section 3 begins our study of the online paging problem — introduced briefly in Lecture #1 — we’ll also ...
متن کاملCS369N: Beyond Worst-Case Analysis Lecture #3: Deterministic Planted Models for Clustering and Graph Partitioning∗
Last lecture motivated the need for a model of data to gain insight into the relative merits of different online paging algorithms. In this lecture and the next, we explore models of data for clustering and graph partitioning problems. We cover deterministic data models in this lecture, and probabilistic ones in the next. In some optimization problems, the objective function can be taken quite ...
متن کاملCS369N: Beyond Worst-Case Analysis Lecture #5: Self-Improving Algorithms
Last lecture concluded with a discussion of semi-random graph models, an interpolation between worst-case analysis and average-case analysis designed to identify robust algorithms in the face of strong impossibility results for worst-case guarantees. This lecture and the next two give three more analysis frameworks that blend aspects of worstand average-case analysis. Today’s model, of self-imp...
متن کاملCS369N: Beyond Worst-Case Analysis Lecture #4: Probabilistic and Semirandom Models for Clustering and Graph Partitioning∗
1.1 Learning Mixtures of Gaussians We consider k distributions D1, D2, . . . , Dk on Rn. Suppose that Di has a mixing weight wi, where the mixing weights are nonnegative and sum to 1. We consider the following 2-stage sampling procedure (see Figure 1): first, we pick a distribution Di randomly according to the mixing weights; then we pick a random point x ∈ Rn according to Di. To illustrate the...
متن کامل