Search Results - "GOTOH, JUN"

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  1. 1

    DC formulations and algorithms for sparse optimization problems by Gotoh, Jun-ya, Takeda, Akiko, Tono, Katsuya

    Published in Mathematical programming (01-05-2018)
    “…We propose a DC (Difference of two Convex functions) formulation approach for sparse optimization problems having a cardinality or rank constraint. With the…”
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    Journal Article
  2. 2

    Newsvendor solutions via conditional value-at-risk minimization by Gotoh, Jun-ya, Takano, Yuichi

    Published in European journal of operational research (16-05-2007)
    “…In this paper, we consider the minimization of the conditional value-at-risk (CVaR), a most preferable risk measure in financial risk management, in the…”
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    Journal Article
  3. 3

    Exact penalty method for knot selection of B-spline regression by Yagishita, Shotaro, Gotoh, Jun-ya

    “…This paper presents a new approach to selecting knots at the same time as estimating the B-spline regression model. Such simultaneous selection of knots and…”
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  4. 4

    Robust empirical optimization is almost the same as mean–variance optimization by Gotoh, Jun-ya, Kim, Michael Jong, Lim, Andrew E.B.

    Published in Operations research letters (01-07-2018)
    “…We formulate a distributionally robust optimization problem where the deviation of the alternative distribution is controlled by a ϕ-divergence penalty in the…”
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  5. 5

    On the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression by Nakayama, Shummin, Gotoh, Jun-ya

    Published in Optimization letters (01-11-2021)
    “…This paper conducts a comparative study of proximal gradient methods (PGMs) and proximal DC algorithms (PDCAs) for sparse regression problems which can be cast…”
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  6. 6

    Support vector machines based on convex risk functions and general norms by Gotoh, Jun-ya, Uryasev, Stan

    Published in Annals of operations research (01-02-2017)
    “…This paper studies unified formulations of support vector machines (SVMs) for binary classification on the basis of convex analysis, especially, convex risk…”
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  7. 7

    Two pairs of families of polyhedral norms versus ...-norms: proximity and applications in optimization by Gotoh, Jun-ya, Uryasev, Stan

    Published in Mathematical programming (01-03-2016)
    “…(ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image).This paper studies four families of polyhedral norms parametrized by a single…”
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  8. 8

    Dynamic portfolio selection with linear control policies for coherent risk minimization by Takano, Yuichi, Gotoh, Jun-ya

    Published in Operations Research Perspectives (01-01-2023)
    “…This paper is concerned with a linear control policy for dynamic portfolio selection. We develop this policy by incorporating time-series behaviors of asset…”
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  9. 9

    Multi-period portfolio selection using kernel-based control policy with dimensionality reduction by Takano, Yuichi, Gotoh, Jun-ya

    Published in Expert systems with applications (15-06-2014)
    “…•Kernel method is employed for multi-period portfolio selection.•A dimensionality reduction technique is adopted to efficiently solve the problem.•Numerical…”
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  10. 10

    Continuous-Wave Laser Lateral Crystallization of A-Si Thin Films on Polyimide Using a Heatsink Layer Embedded in the Buffer SiO2 by Sasaki, Nobuo, Arif, Muhammad, Uraoka, Yukiharu, Gotoh, Jun, Sugimoto, Shigeto

    Published in Journal of electronic materials (01-06-2021)
    “…Continuous-wave laser crystallization of amorphous Si (a-Si) thin films on a polyimide (PI)-coated glass substrate is studied by using a single scan of a…”
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  11. 11

    Minimizing loss probability bounds for portfolio selection by Gotoh, Jun-ya, Takeda, Akiko

    Published in European journal of operational research (01-03-2012)
    “…► We derive nonparametric upper and lower bounds of portfolio loss probability. ► We propose to minimize a fraction consisting of VaR/CVaR and any norm of a…”
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  12. 12

    Unseeded Crystal Growth of (100)-Oriented Grain-Boundary-Free Si Thin-Film by a Single Scan of the CW-Laser Lateral Crystallization of a-Si on Insulator by Sasaki, Nobuo, Arif, Muhammad, Uraoka, Yukiharu, Gotoh, Jun, Sugimoto, Shigeto

    Published in Crystals (Basel) (01-05-2020)
    “…Laser crystallization of a-Si film on insulating substrate is a promising technology to fabricate three-dimensional integrations (3D ICs), flat panel displays…”
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  13. 13

    Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios by Takeda, Akiko, Niranjan, Mahesan, Gotoh, Jun-ya, Kawahara, Yoshinobu

    Published in Computational management science (01-02-2013)
    “…Index tracking is a passive investment strategy in which a fund (e.g., an ETF: exchange traded fund) manager purchases a set of assets to mimic a market index…”
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  14. 14

    Robust portfolio techniques for mitigating the fragility of CVaR minimization and generalization to coherent risk measures by Gotoh, Jun-Ya, Shinozaki, Keita, Takeda, Akiko

    Published in Quantitative finance (01-10-2013)
    “…The conditional value-at-risk (CVaR) has gained growing popularity in financial risk management due to the coherence property and tractability in its…”
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  15. 15

    Peer-To-Peer Lending: Classification in the Loan Application Process by Wei, Xinyuan, Gotoh, Jun-ya, Uryasev, Stan

    Published in Risks (Basel) (01-12-2018)
    “…This paper studies the peer-to-peer lending and loan application processing of LendingClub. We tried to reproduce the existing loan application processing…”
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  16. 16

    On the role of norm constraints in portfolio selection by Gotoh, Jun-ya, Takeda, Akiko

    Published in Computational management science (01-11-2011)
    “…Several optimization approaches for portfolio selection have been proposed in order to alleviate the estimation error in the optimal portfolio. Among them are…”
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    Interaction between financial risk measures and machine learning methods by Gotoh, Jun-ya, Takeda, Akiko, Yamamoto, Rei

    Published in Computational management science (01-10-2014)
    “…The purpose of this article is to review the similarity and difference between financial risk minimization and a class of machine learning methods known as…”
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