Jun-ya Gotoh

发表

Interaction between financial risk measures and machine learning methods

Akiko Takeda, Rei Yamamoto, Jun-ya Gotoh, 2014, Comput. Manag. Sci..

Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension

Jun-ya Gotoh, Shotaro Yagishita, 2020 .

DC formulations and algorithms for sparse optimization problems

Akiko Takeda, Jun-ya Gotoh, Katsuya Tono, 2017, Mathematical Programming.

Global optimization method for solving the minimum maximal flow problem

Nguyen V. Thoai, Yoshitsugu Yamamoto, Jun-ya Gotoh, 2003, Optim. Methods Softw..

Conditional minimum volume ellipsoid with application to multiclass discrimination

Akiko Takeda, Jun-ya Gotoh, J. Gotoh, 2008, Comput. Optim. Appl..

Preface

Stan Uryasev, Jun-ya Gotoh, 2018, Ann. Oper. Res..

EDITORIAL INTRODUCTION : A SPECIAL ISSUE OF THE SCOPE (SEMINAR ON COMPUTATION AND OPTIMIZATION FOR NEW EXTENSIONS)( SCOPE (Seminar on Computation and OPtimization for new Extensions))

Katsuki Fujisawa, Jun-ya Gotoh, 2011 .

Multi-period portfolio selection using kernel-based control policy with dimensionality reduction

Jun-ya Gotoh, Yuichi Takano, Yuichi Takano, 2014, Expert Syst. Appl..

Numerical exploration of dynamic behavior of the ornstein-uhlenbeck process via ehrenfest process approximation

Ushio Sumita, Jun-ya Gotoh, Hui Jin, 2003 .

Maximization of the Ratio of Two Convex Quadratic Functions over a Polytope

Hiroshi Konno, Jun-ya Gotoh, H. Konno, 2001, Comput. Optim. Appl..

Worst-Case Sensitivity pdf

Andrew E. B. Lim, Michael Jong Kim, Jun-ya Gotoh, 2020, ArXiv.

Newsvendor solutions via conditional value-at-risk minimization

Jun-ya Gotoh, Yuichi Takano, Yuichi Takano, 2007, Eur. J. Oper. Res..

Two pairs of families of polyhedral norms versus $$\ell _p$$ℓp-norms: proximity and applications in optimization

Stan Uryasev, Jun-ya Gotoh, S. Uryasev, 2016, Math. Program..

Bounding Option Prices by Semidefinite Programming: A Cutting Plane Algorithm

Hiroshi Konno, Jun-ya Gotoh, H. Konno, 2002, Manag. Sci..

Bounding Contingent Claim Prices via Hedging Strategy with Coherent Risk Measures

Yoshitsugu Yamamoto, Weifeng Yao, Jun-ya Gotoh, 2011, J. Optim. Theory Appl..

Calibration of Distributionally Robust Empirical Optimization Models

Andrew E. B. Lim, Michael Jong Kim, Jun-ya Gotoh, 2017, Oper. Res..

Robust Empirical Optimization is Almost the Same As Mean-Variance Optimization

Andrew E. B. Lim, Michael Jong Kim, Jun-ya Gotoh, 2015, Oper. Res. Lett..

Minimal Ellipsoid Circumscribing a Polytope Defined by a System of Linear Inequalities

Hiroshi Konno, Jun-ya Gotoh, H. Konno, 2006, J. Glob. Optim..

A Cutting Plane Algorithm for Semi-Definite Programming Problems with Applications to Failure Discrimination and Cancer Diagnosis (Mathematical Science of Optimization)

Takeaki Uno, Jun-ya Gotoh, Hiroshi Konno, 2000 .

Support Vector Machines Based on Convex Risk Functionals and General Norms

Stan Uryasev, Jun-ya Gotoh, Jun-ya Gotoh, 2014 .

Peer-To-Peer Lending: Classification in the Loan Application Process

Stan Uryasev, Jun-ya Gotoh, Xinyuan Wei, 2018, Risks.

α-Conservative approximation for probabilistically constrained convex programs

Jun-ya Gotoh, Yuichi Takano, 2010, Comput. Optim. Appl..

On the role of norm constraints in portfolio selection

Akiko Takeda, Jun-ya Gotoh, J. Gotoh, 2011, Comput. Manag. Sci..

Minimizing loss probability bounds for portfolio selection

Akiko Takeda, Jun-ya Gotoh, J. Gotoh, 2012, Eur. J. Oper. Res..

Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios

Akiko Takeda, Yoshinobu Kawahara, Mahesan Niranjan, 2012, Comput. Manag. Sci..

On the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression

Jun-ya Gotoh, Shummin Nakayama, 2020 .

A data-driven approach to beating SAA out-of-sample

Andrew E. B. Lim, Jun-ya Gotoh, Michael Jong Kim, 2021, ArXiv.

Calibration of Distributionally Robust Empirical Optimization Models

Andrew E. B. Lim, Jun-ya Gotoh, Michael Jong Kim, 2021, Oper. Res..

On the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression

Jun-ya Gotoh, Shummin Nakayama, 2021, Optim. Lett..

Support Vector Classification with Positive Homogeneous Risk Functionals

Jun-ya Gotoh, Peter Tsyurmasto, J. Gotoh, 2013 .

Third Degree Stochastic Dominance and Mean-Risk Analysis

Hiroshi Konno, Jun-ya Gotoh, H. Konno, 2000 .

Convex optimization approaches to maximally predictable portfolio selection

Katsuki Fujisawa, Jun-ya Gotoh, K. Fujisawa, 2014 .

Failure Discrimination by Semi-Definite Programming

Hiroshi Konno, Stan Uryasev, Jun-ya Gotoh, 2002 .

Robust Empirical Optimization is Almost the Same As Mean-Variance Optimization

Jun-ya Gotoh, Michael Kim, Andrew Lim, 2015 .

Efficient DC Algorithm for Constrained Sparse Optimization

Akiko Takeda, Jun-ya Gotoh, Katsuya Tono, 2017, 1701.08498.

A linear classification model based on conditional geometric score

Akiko Takeda, Jun-ya Gotoh, J. Gotoh, 2004 .

Conditional Minimum Volume Ellipsoid with Applications to Subset Selection for MVE Estimator and Multiclass Discrimination

Akiko Takeda, Jun-ya Gotoh, Akiko Takeda, 2006 .