Yuichi Takano

Two-way Cutting-plane Algorithm for Best Subset Selection Considering Multicollinearity

  • Yuichi Takano
    • Categories Global Optimization, Integer Programming, Optimization in Data Science Tags , , , ,

    When linear dependence exists between some explanatory variables in a regression model, the estimates of regression coefficients become unstable, thereby making the interpretation of the estimation results unreliable. To eliminate such multicollinearity, we propose a high-performance method for selecting the best subset of explanatory variables for linear and logistic regression models. Specifically, we first derive … Read more

    Missing Value Imputation via Mathematical Optimization with Instance-and-Feature Neighborhoods

  • Yuichi Takano
    • Categories (Mixed) Integer Nonlinear Programming, Optimization in Data Science Tags , , , ,

    Datasets collected for analysis often contain a certain amount of incomplete instances, where some feature values are missing. Since many statistical analyses and machine learning algorithms depend on complete datasets, missing values need to be imputed in advance. Bertsimas et al. (2018) proposed a high-performance method that combines machine learning and mathematical optimization algorithms for … Read more

    Mean–variance portfolio optimization with shrinkage estimation for recommender systems

  • Yuichi Takano
    • Categories Integer Programming, Optimization in Data Science Tags , , , ,

    This paper is concerned with a mean-variance portfolio optimization model with cardinality constraint for generating high-quality lists of recommendations. It is usually difficult to accurately estimate the rating covariance matrix required for mean-variance portfolio optimization because of a shortage of observed user ratings. To improve the accuracy of covariance matrix estimation, we apply shrinkage estimation … Read more

    Prescriptive price optimization using optimal regression trees

  • Shunnosuke Ikeda
  • Naoki Nishimura
  • Noriyoshi Sukegawa
  • Yuichi Takano
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Marketing Tags , ,

    This paper focuses on prescriptive price optimization, which derives the optimal pricing strategy that maximizes future revenue or profit by using demand forecasting models for multiple products. Prescriptive price optimization requires accurate demand forecasting models because the accuracy of these models has a direct impact on pricing strategies aimed at increasing revenue or profit. However, … Read more

    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