CS 369 N : Beyond Worst - Case Analysis Lecture # 8 : Resource Augmentation ∗
نویسنده
چکیده
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 standard worst-case analysis (with no model of the data). Resource augmentation was perhaps first used by Sleator and Tarjan [?] in the context of online paging, a problem that we discussed at length in Lecture #2. Recall the proof the the LRU (Least Recently Used) algorithm, among others, is a k-competitive online algorithm for the paging problem.
منابع مشابه
CS 369 N : Beyond Worst - Case Analysis Lecture
This lecture is last on flexible and robust models of “non-worst-case data”. The idea is again to assume that there is some “random aspect” to the data, while stopping well short of average-case analysis. Recall our critique of the latter: it encourages overfitting a brittle algorithmic solution to an overly specific data model. Thus far, we’ve seen two data models that assume only that there i...
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 کاملCS 264 : Beyond Worst - Case Analysis Lecture # 20 : Algorithm - Specific Algorithm Selection ∗
A major theme of CS264 is to use theory to derive good guidance about which algorithm to use to solve a given problem in a given domain. For most problems, there is no “one size fits all” algorithm, and the right algorithm to use depends on the set of inputs relevant for the application. In today’s lecture, we’ll turn this theme into a well-defined mathematical problem, formalized via statistic...
متن کاملCS 264 : Beyond Worst - Case Analysis Lecture # 19 : Self - Improving Algorithms ∗
The last several lectures discussed several interpolations 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 gives another analysis framework that blends aspects of worstand average-case analysis. In today’s model of self-improving algorithms, an adversary picks an inpu...
متن کاملCS 264 : Beyond Worst - Case Analysis Lecture
The last few lectures discussed several interpolations between worst-case and average-case analysis designed to identify robust algorithms in the face of strong impossibility results for worst-case guarantees. This lecture gives another analysis framework that blends aspects of worstand average-case analysis. In today’s model of self-improving algorithms, an adversary picks an input distributio...
متن کامل