GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Institute for Operations Research and the Management Sciences (INFORMS)  (3)
  • Mathematics  (3)
Material
Publisher
  • Institute for Operations Research and the Management Sciences (INFORMS)  (3)
Person/Organisation
Language
Years
Subjects(RVK)
  • Mathematics  (3)
RVK
  • 1
    Online Resource
    Online Resource
    The Discrete Moment Problem with Nonconvex Shape Constraints
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2021
    In:  Operations Research Vol. 69, No. 1 ( 2021-01), p. 279-296
    Details
    In: Operations Research, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 69, No. 1 ( 2021-01), p. 279-296
    Abstract: The discrete moment problem is a foundational problem in distribution-free robust optimization, where the goal is to find a worst-case distribution that satisfies a given set of moments. This paper studies the discrete moment problems with additional shape constraints that guarantee the worst-case distribution is either log-concave (LC), has an increasing failure rate (IFR), or increasing generalized failure rate (IGFR). These classes of shape constraints have not previously been studied in the literature, in part due to their inherent nonconvexities. Nonetheless, these classes are useful in practice, with applications in revenue management, reliability, and inventory control. We characterize the structure of optimal extreme point distributions under these constraints. We show, for example, that an optimal extreme point solution to a moment problem with m moments and LC shape constraints is piecewise geometric with at most m pieces. This optimality structure allows us to design an exact algorithm for computing optimal solutions in a low-dimensional space of parameters. We also leverage this structure to study a robust newsvendor problem with shape constraints and compute optimal solutions.
    Type of Medium: Online Resource
    ISSN: 0030-364X , 1526-5463
    URL: Article
    DOI: 10.1287/opre.2020.1990
    RVK:
    SA 7410
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2021
    detail.hit.zdb_id: 2019440-7
    detail.hit.zdb_id: 123389-0
    SSG: 3,2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Choice-Based Recommender Systems: A Unified Approach to Achieving Relevancy and Diversity
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2014
    In:  Operations Research Vol. 62, No. 5 ( 2014-10), p. 973-993
    Details
    In: Operations Research, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 62, No. 5 ( 2014-10), p. 973-993
    Abstract: Recommender systems have been widely used by online stores to suggest items of interest to users. These systems often identify a subset of items from a much larger set that best matches the user's interest. A key concern with existing approaches is overspecialization, which results in returning items that are too similar to each other. Unlike existing solutions that rely on diversity metrics to reduce similarity among recommended items, we propose using choice probability to measure the overall quality of a recommendation list, which unifies the desire to achieve both relevancy and diversity in recommendation. We first define the recommendation problem from the discrete choice perspective. We then model the problem under the multilevel nested logit model, which is capable of handling similarities between alternatives along multiple dimensions. We formulate the problem as a nonlinear binary integer programming problem and develop an efficient dynamic programming algorithm that solves the problem to optimum in O(nKSR 2 ) time, where n is the number of levels and K is the maximum number of children nests a nest can have in the multilevel nested logit model, S is the total number of items in the item pool, and R is the number of items wanted in recommendation.
    Type of Medium: Online Resource
    ISSN: 0030-364X , 1526-5463
    URL: Article
    DOI: 10.1287/opre.2014.1292
    RVK:
    SA 7410
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2014
    detail.hit.zdb_id: 2019440-7
    detail.hit.zdb_id: 123389-0
    SSG: 3,2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2023
    In:  Operations Research
    Details
    In: Operations Research, Institute for Operations Research and the Management Sciences (INFORMS)
    Abstract: In solving simulation-based stochastic root-finding or optimization problems that involve rare events, such as in extreme quantile estimation, running crude Monte Carlo can be prohibitively inefficient. To address this issue, importance sampling can be employed to drive down the sampling error to a desirable level. However, selecting a good importance sampler requires knowledge of the solution to the problem at hand, which is the goal to begin with and thus forms a circular challenge. We investigate the use of adaptive importance sampling to untie this circularity. Our procedure sequentially updates the importance sampler to reach the optimal sampler and the optimal solution simultaneously, and can be embedded in both sample-average-approximation-type algorithms and stochastic-approximation-type algorithms. Our theoretical analysis establishes strong consistency and asymptotic normality of the resulting estimators. We also demonstrate, via a minimax perspective, the key role of using adaptivity in controlling asymptotic errors. Finally, we illustrate the effectiveness of our approach via numerical experiments. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72293562, 72121001, and 72171060], the National Science Foundation [Grants CAREER CMMI-1834710 and IIS-1849280] , and the Air Force Office of Scientific Research [Grant FA95502010211]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2023.2484 .
    Type of Medium: Online Resource
    ISSN: 0030-364X , 1526-5463
    URL: Article
    DOI: 10.1287/opre.2023.2484
    RVK:
    SA 7410
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2023
    detail.hit.zdb_id: 2019440-7
    detail.hit.zdb_id: 123389-0
    SSG: 3,2
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...