Yuichi Takano

Cutting-plane algorithm for sparse estimation of the Cox proportional-hazards model

  • Hiroki Saishu
  • Kota Kudo
  • Yuichi Takano
    • Categories (Mixed) Integer Nonlinear Programming, Branch and Cut Algorithms, Statistics Tags , , , , ,

    Survival analysis is a family of statistical methods for analyzing event occurrence times. In this paper, we address the mixed-integer optimization approach to sparse estimation of the Cox proportional-hazards model for survival analysis. Specifically, we propose a high-performance cutting-plane algorithm based on reformulation of bilevel optimization for sparse estimation. This algorithm solves the upper-level problem … Read more

    Linear Control Policies for Online Vehicle Relocation in Shared Mobility Systems

  • Yuichi Takano
  • Yasuhiro Yoshida
    • Categories Transportation

    In one-way station-based shared mobility systems, where system users share vehicles for making trips between vehicle stations, the accumulation of one-way trips inevitably causes vehicle imbalances between stations. To correct these imbalances, we focus on the effective use of linear control policies for calculating online vehicle relocations from a history of user trips. Our scenario-based … Read more

    Branch-and-bound Algorithm for Optimal Sparse Canonical Correlation Analysis

  • Ryuhei Miyashiro
  • Yuichi Takano
  • Ryuta Tamura
  • Akihisa Watanabe
    • Categories Statistics

    Canonical correlation analysis (CCA) is a family of multivariate statistical methods for extracting mutual information contained in multiple datasets. To improve the interpretability of CCA, here we focus on the mixed-integer optimization (MIO) approach to sparse estimation. This approach was first proposed for sparse linear regression in the 1970s, but it has recently received renewed … Read more

    Sparse Poisson regression via mixed-integer optimization

  • Kota Kudo
  • Yuichi Takano
  • Hiroki Saishu
    • Categories Integer Programming, Statistics Tags , , ,

    We present a mixed-integer optimization (MIO) approach to sparse Poisson regression. The MIO approach to sparse linear regression was first proposed in the 1970s, but has recently received renewed attention due to advances in optimization algorithms and computer hardware. In contrast to many sparse estimation algorithms, the MIO approach has the advantage of finding the … Read more

    Dynamic Portfolio Selection with Linear Control Policies for Coherent Risk Minimization

  • Jun-ya Gotoh
  • Yuichi Takano
    • Categories Finance and Economics, Robust Optimization Tags , , ,

    This paper is concerned with a linear control policy for dynamic portfolio selection. We develop this policy by incorporating time-series behaviors of asset returns on the basis of coherent risk minimization. Analyzing the dual form of our optimization model, we demonstrate that the investment performance of linear control policies is directly connected to the intertemporal … Read more

    Stochastic Discrete First-order Algorithm for Feature Subset Selection

  • Kota Kudo
  • Yuichi Takano
  • Ryo Nomura
    • Categories Integer Programming, Statistics Tags , , , ,

    This paper addresses the problem of selecting a significant subset of candidate features to use for multiple linear regression. Bertsimas et al. (2016) recently proposed the discrete first-order (DFO) algorithm to efficiently find near-optimal solutions to this problem. However, this algorithm is unable to escape from locally optimal solutions. To resolve this, we propose a … Read more

    Multi-objective optimization models for many-to-one matching problems

  • Natsumi Shimada
  • Yuichi Takano
  • Natsuki Yamazaki
    • Categories (Mixed) Integer Linear Programming, Finance and Economics Tags , , , ,

    This paper is concerned with many-to-one matching problems for assigning residents to hospitals according to their preferences. The stable matching model aims at finding a stable matching, and the assignment game model involves maximizing the total utility; however, these two objectives are incompatible in general. We also focus on a situation where there are predetermined … Read more

    Best Subset Selection via Cross-validation Criterion

  • Ryuhei Miyashiro
  • Yuichi Takano
    • Categories Integer Programming, Statistics Tags , , , ,

    This paper is concerned with the cross-validation criterion for best subset selection in a linear regression model. In contrast with the use of statistical criteria (e.g., Mallows’ $C_p$, AIC, BIC, and various information criteria), the cross-validation only requires the mild assumptions, namely, samples are identically distributed, and training and validation samples are independent. For this … Read more

    A Branch-and-Cut Algorithm for Solving Mixed-integer Semidefinite Optimization Problems

  • Ken Kobayashi
  • Yuichi Takano
    • Categories (Mixed) Integer Nonlinear Programming, Global Optimization, Semi-definite Programming Tags , , ,

    This paper is concerned with a cutting-plane algorithm for solving mixed-integer semidefinite optimization (MISDO) problems. In this algorithm, the positive semidefinite constraint is relaxed, and the resultant mixed-integer linear optimization problem is repeatedly solved with valid inequalities for the relaxed constraint. We prove convergence properties of the algorithm. Moreover, to speed up the computation, we … Read more

    Mixed Integer Quadratic Optimization Formulations for Eliminating Multicollinearity Based on Variance Inflation Factor

  • Ken Kobayashi
  • Tomomi Matsui
  • Ryuhei Miyashiro
  • Kazuhide Nakata
  • Yuichi Takano
  • Ryuta Tamura
    • Categories Applications - Science and Engineering, Integer Programming, Statistics Tags , , , , ,

    The variance inflation factor, VIF, is the most frequently used indicator for detecting multicollinearity in multiple linear regression models. This paper proposes two mixed integer quadratic optimization formulations for selecting the best subset of explanatory variables under upper-bound constraints on VIF of selected variables. Computational results illustrate the effectiveness of our optimization formulations based on … Read more