146 research outputs found

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    University of Windsor Undergraduate Calendar 2022 Winter

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    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1017/thumbnail.jp

    Online Algorithms for Matching Platforms with Multi-Channel Traffic

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    Two-sided platforms rely on their recommendation algorithms to help visitors successfully find a match. However, on platforms such as VolunteerMatch (VM) -- which has facilitated millions of connections between volunteers and nonprofits -- a sizable fraction of website traffic arrives directly to a nonprofit's volunteering page via an external link, thus bypassing the platform's recommendation algorithm. We study how such platforms should account for this external traffic in the design of their recommendation algorithms, given the goal of maximizing successful matches. We model the platform's problem as a special case of online matching, where (using VM terminology) volunteers arrive sequentially and probabilistically match with one opportunity, each of which has finite need for volunteers. In our framework, external traffic is interested only in their targeted opportunity; by contrast, internal traffic may be interested in many opportunities, and the platform's online algorithm selects which opportunity to recommend. In evaluating different algorithms, we parameterize the competitive ratio based on the amount of external traffic. After demonstrating the shortcomings of a commonly-used algorithm that is optimal in the absence of external traffic, we propose a new algorithm -- Adaptive Capacity (AC) -- which accounts for matches differently based on whether they originate from internal or external traffic. We provide a lower bound on AC's competitive ratio that is increasing in the amount of external traffic and that is close to the parameterized upper bound we establish on the competitive ratio of any online algorithm. Our analysis utilizes a path-based, pseudo-rewards approach, which we further generalize to settings where the platform can recommend a ranked set of opportunities. Beyond our theoretical results, we show the strong performance of AC in a case study motivated by VM data

    University of Windsor Undergraduate Calendar 2022 Fall

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    https://scholar.uwindsor.ca/universitywindsorundergraduatecalendars/1019/thumbnail.jp

    International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts

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    The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the International Conference on Mathematical Analysis and Applications in Science and Engineering – ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days). Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering, industry, and technology, to discuss the development of new mathematical models, theories, and applications that contribute to the advancement of scientific knowledge and practice. Authors proposed research in topics including partial and ordinary differential equations, integer and fractional order equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization, control, probability, computational mathematics, amongst others. The conference was designed to maximize the involvement of all participants and will present the state-of- the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio

    LIPIcs, Volume 244, ESA 2022, Complete Volume

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    LIPIcs, Volume 244, ESA 2022, Complete Volum

    New benchmarking techniques in resource allocation problems: theory and applications in cloud systems

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    Motivated by different e-commerce applications such as allocating virtual machines to servers and online ad placement, we study new models that aim to capture unstudied tensions faced by decision-makers. In online/sequential models, future information is often unavailable to decision-makers---e.g., the exact demand of a product for next week. Sometimes, these unknowns have regularity, and decision-makers can fit random models. Other times, decision-makers must be prepared for any possible outcome. In practice, several solutions are based on classical models that do not fully consider these unknowns. One reason for this is our present technical limitations. Exploring new models with adequate sources of uncertainty could be beneficial for both the theory and the practice of decision-making. For example, cloud companies such as Amazon WS face highly unpredictable demands of resources. New management planning that considers these tensions have improved capacity and cut costs for the cloud providers. As a result, cloud companies can now offer new services at lower prices benefiting thousands of users. In this thesis, we study three different models, each motivated by an application in cloud computing and online advertising. From a technical standpoint, we apply either worst-case analysis with limited information from the system or adaptive analysis with stochastic results learned after making an irrevocable decision. A central aspect of this work is dynamic benchmarks as opposed to static or offline ones. Static and offline viewpoints are too conservative and have limited interpretation in some dynamic settings. A dynamic criterion, such as the value of an optimal sequential policy, allows comparisons with the best that one could do in dynamic scenarios. Another aspect of this work is multi-objective criteria in dynamic settings, where two or more competing goals must be satisfied under an uncertain future. We tackle the challenges introduced by these new perspectives with fresh theoretical analyses, drawing inspiration from linear and nonlinear optimization and stochastic processes.Ph.D

    Three Risky Decades: A Time for Econophysics?

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    Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era
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