检索结果-内蒙古大学图书馆

Sparsity-Exploiting anytime algorithms for Model Predictive Control: A Relaxed Barrier Approach

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2020年第2期28卷 425-435页

作者： Feller, Christian Ebenbauer, Christian Univ Stuttgart Inst Syst Theory & Automat Control D-70550 Stuttgart Germany

We present and analyze a novel class of stabilizing and numerically efficient model predictive control (MPC) algorithms for discrete-time linear systems subject to polytopic input and state constraints. The proposed approach combines the previously presented concept of relaxed barrier function-based MPC with suitable warm-starting and sparsity-exploiting factorization techniques and allows to rigorously prove important stability and constraint satisfaction properties of the resulting closed-loop system independently of the number of performed Newton iterations. The effectiveness of the proposed approach is demonstrated by means of a numerical benchmark example.

关键词： Closed loop systems Prediction algorithms Stability analysis Optimal control Numerical stability Cost function anytime algorithms barrier functions model predictive control real-time optimization stability analysis

来源：评论

学校读者我要写书评

暂无评论

Harnessing the strengths of anytime algorithms for constant data streams

引用

DATA MINING AND KNOWLEDGE DISCOVERY 2009年第2期19卷 245-260页

作者： Kranen, Philipp Seidl, Thomas Rhein Westfal TH Aachen Data Management & Data Explorat Grp D-52056 Aachen Germany

anytime algorithms have been proposed for many different applications, e.g., in data mining. Their strengths are the ability to first provide a result after a very short initialization and second to improve their result with additional time. Therefore, anytime algorithms have so far been used when the available processing time varies, e.g., on varying data streams. In this paper we propose to employ anytime algorithms on constant data streams, i.e., for tasks with constant time allowance. We introduce two approaches that harness the strengths of anytime algorithms on constant data streams and thereby improve the over all quality of the result with respect to the corresponding budget algorithm. We derive formulas for the expected performance gain and demonstrate the effectiveness of our novel approaches using existing anytime algorithms on benchmark data sets.

关键词： anytime algorithms Stream data mining Classification confidence

来源：评论

学校读者我要写书评

暂无评论

Monitoring and control of anytime algorithms: A dynamic programming approach

引用

ARTIFICIAL INTELLIGENCE 2001年第1-2期126卷 139-157页

作者： Hansen, EA Zilberstein, S Mississippi State Univ Dept Comp Sci Mississippi State MS 39762 USA Univ Massachusetts Dept Comp Sci Amherst MA 01002 USA

anytime algorithms offer a tradeoff between solution quality and computation time that has proved useful in solving time-critical problems such as planning and scheduling, belief network evaluation, and information gathering. To exploit this tradeoff, a system must be able to decide when to stop deliberation and act on the currently available solution. This paper analyzes the characteristics of existing techniques for meta-level control of anytime algorithms and develops a new framework for monitoring and control. The new framework handles effectively the uncertainty associated with the algorithm's performance profile, the uncertainty associated with the domain of operation, and the cost of monitoring progress. The result is an efficient non-myopic solution to the meta-level control problem for anytime algorithms. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： anytime algorithms flexible computation meta-level control monitoring resource-bounded reasoning real-time deliberation

来源：评论

学校读者我要写书评

暂无评论

Optimal schedules for monitoring anytime algorithms

引用

ARTIFICIAL INTELLIGENCE 2001年第1-2期126卷 63-108页

作者： Finkelstein, L Markovitch, S Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

Monitoring anytime algorithms can significantly improve their performance. This work deals with the problem of off-line construction of monitoring schedules. We study a model where queries are submitted to the monitored process in order to detect satisfaction of a given goal predicate. The queries consume time from the monitored process, thus delaying the time of satisfying the goal condition. We present a formal model for this class of problems and provide a theoretical analysis of the class of optimal schedules. We then introduce an algorithm for constructing optimal monitoring schedules and prove its correctness. We continue with distribution-based analysis for common distributions, accompanied by experimental results. We also provide a theoretical comparison of our methodology with existing monitoring techniques. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： monitoring anytime algorithms meta-reasoning

来源：评论

学校读者我要写书评

暂无评论

anytime algorithms for the longest common palindromic subsequence problem

引用

COMPUTERS & OPERATIONS RESEARCH 2020年 114卷 104827-000页

作者： Djukanovic, Marko Raidl, Guenther R. Blum, Christian TU Wien Inst Log & Computat Vienna Austria CSIC Artificial Intelligence Res Inst IIIA Campus UAB Bellaterra Spain

The longest common palindromic subsequence (LCPS) problem aims at finding a longest string that appears as a subsequence in each of a set of input strings and is a palindrome at the same time. The problem is a special variant of the well known longest common subsequence problem and has applications in particular in genomics and biology, where strings correspond to DNA or protein sequences and similarities among them shall be detected or quantified. We first present a more traditional A* search that makes use of an advanced upper bound calculation for partial solutions. This exact approach works well for instances with two input strings and, as shown in experiments, outperforms several other exact methods from the literature. However, the A* search also has natural limitations when a larger number of strings shall be considered due to the problem's complexity. To effectively deal with this case in practice, anytime A* search variants are investigated, which are able to return a reasonable heuristic solution at almost any time and are expected to find better and better solutions until reaching a proven optimum when enough time given. In particular a novel approach is proposed in which anytime Column Search (ACS) is interleaved with traditional A* node expansions. The ACS iterations are guided by a new heuristic function that approximates the expected length of an LCPS in subproblems usually much better than the available upper bound calculation. This A*+ACS hybrid is able to solve small to medium-sized LCPS instances to proven optimality while returning good heuristic solutions together with upper bounds for large instances. In rigorous experimental evaluations we compare A*+ACS to several other anytime A* search variants and observe its superiority. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： The longest common palindromic subsequence problem anytime algorithms A* Search Beam search Hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

anytime algorithms for Recommendation Service Providers

引用

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2016年第3期7卷 43-43页

作者： Ben-Shimon, David Rokach, Lior Shani, Guy Shapira, Bracha Ben Gurion Univ Negev Informat Syst Engn Dept IL-84105 Beer Sheva Israel

Recommender systems (RS) can now be found in many commercial Web sites, often presenting customers with a short list of additional products that they might purchase. Many commercial sites do not typically have the ability and resources to develop their own system and may outsource the RS to a third party. This had led to the growth of a recommendation as a service industry, where companies, referred to as RS providers, provide recommendation services. These companies must carefully balance the cost of building recommendation models and the payment received from the e-business, as these payments are expected to be low. In such a setting, restricting the computational time required for model building is critical for the RS provider to be profitable. In this article, we propose anytime algorithms as an attractive method for balancing computational time and the recommendation model performance, thus tackling the RS provider problem. In an anytime setting, an algorithm can be stopped after any amount of computational time, always ensuring that a valid, although suboptimal, solution will be returned. Given sufficient time, however, the algorithm should converge to an optimal solution. In this setting, it is important to evaluate the quality of the returned solution over time, monitoring quality improvement. This is significantly different from traditional evaluation methods, which mostly estimate the performance of the algorithm only after its convergence is given sufficient time. We show that the popular item-item top-N recommendation approach can be brought into the anytime framework by smartly considering the order by which item pairs are being evaluated. We experimentally show that the time-accuracy trade-off can be significantly improved for this specific problem.

关键词： algorithms Recommenders Evaluation Performance Experimentation Recommender systems collaborative filtering anytime algorithms

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Use of anytime algorithms on an Unmanned Aerial Vehicle

Analyzing the Use of Anytime Algorithms on an Unmanned Aeria...

引用

3rd Brazilian Symposium on Computing Systems Engineering (SBESC)

作者： Goncalves, F. S. Becker, L. B. Raffo, G. V. Univ Fed Santa Catarina Dept Automat & Syst BR-88040900 Florianopolis SC Brazil Univ Fed Minas Gerais Dept Elect Engn BR-88040900 Belo Horizonte MG Brazil

来源：评论

学校读者我要写书评

暂无评论

Please hold on: more time = more patches? Automated program repair as anytime algorithms 2

Please hold on: more time = more patches? Automated program ...

引用

2nd IEEE/ACM International Workshop on Automated Program Repair (APR)

作者： Vu, Duc-Ly Pashchenko, Ivan Massacci, Fabio Univ Trento Trento Italy Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781665444729

Current evaluations of automatic program repair (APR) techniques focus on tools' effectiveness, while little is known about the practical aspects of using APR tools, such as how long one should wait for a tool to generate a bug fix. In this work, we empirically study whether APR tools are any time algorithms (e.g., the more time they have, the more fixes they generate, so it makes sense to trade off longer time for better quality). Our preliminary experiment shows that the amount of plausible patches, given exponentially greater time, only increases linearly or not at all.

关键词： Automated Program Repair anytime algorithms Empirical Software Engineering

来源：评论

学校读者我要写书评

暂无评论

Algorithm Selection of anytime algorithms

Algorithm Selection of Anytime Algorithms

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Jesus, Alexandre D. Liefooghe, Arnaud Derbel, Bilel Paquete, Luis Univ Lille INRIA Cent Lille CNRSUMR 9189 CRIStAL F-59000 Lille France Univ Tokyo JFLI CNRS IRL 3527 Tokyo Japan Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9781450371285

anytime algorithms for optimization problems are of particular interest since they allow to trade off execution time with result quality. However, the selection of the best anytime algorithm for a given problem instance has been focused on a particular budget for execution time or particular target result quality. Moreover, it is often assumed that these anytime preferences are known when developing or training the algorithm selection methodology. In this work, we study the algorithm selection problem in a context where the decision maker's anytime preferences are defined by a general utility function, and only known at the time of selection. To this end, we first examine how to measure the performance of an anytime algorithm with respect to this utility function. Then, we discuss approaches for the development of selection methodologies that receive a utility function as an argument at the time of selection. Then, to illustrate one of the discussed approaches, we present a preliminary study on the selection between an exact and a heuristic algorithm for a bi-objective knapsack problem. The results show that the proposed methodology has an accuracy greater than 96% in the selected scenarios, but we identify room for improvement.

关键词： Algorithm Selection anytime algorithms anytime Performance Measure

来源：评论

学校读者我要写书评

暂无评论

anytime algorithms for mining groups with maximum coverage 12

Anytime algorithms for mining groups with maximum coverage

引用

Proceedings of the Tenth Australasian Data Mining Conference - Volume 134

作者： Satya Gautam Vadlamudi Partha Pratim Chakrabarti Sudeshna Sarkar Indian Institute of Technology Kharagpur Kharagpur West Bengal India

ISBN: (纸本)9781921770142

Mining maximal groups from spatio-temporal data of mobile users is a well known problem. However, number of such groups mined can be very large, demanding further processing to come up with a readily usable set of groups. In this paper, we introduce the problem of mining a set of K maximal groups which covers maximum number of users. Such a set of groups can be useful for businesses which plan to distribute a set of K offers targeting groups of users such that a large number of users are covered. We propose efficient methods to solve this hard problem, which do not mine the total set of groups apriori (avoiding the large amount of time consumed upfront), instead intelligently decide during their execution as to which of the search space is to be explored to mine the next group so that a set of K groups covering large number of users is quickly produced, and then improve the K-set as time progresses (anytime nature). Experimental results on several synthetic spatio-temporal datasets as well as real datasets that are publicly available show the efficacy and scalability of the proposed methods across various parametric inputs.

关键词： spatio-temporal data mining valid groups coverage top k anytime algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：