# Yuichi TakanoUniversity of Tsukuba · College of Policy and Planning Sciences

Yuichi Takano

Doctor of Engineering

## About

69

Publications

5,766

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1,032

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Introduction

I am an associate professor in the Institute of Systems and Information Engineering, University of Tsukuba, Japan.
My primary research interests are mathematical optimization and its application to financial engineering and machine learning.

**Skills and Expertise**

## Publications

Publications (69)

This paper is concerned with personalized pricing models aimed at maximizing the expected revenues or profits for a single item. While it is essential for personalized pricing to predict the purchase probabilities for each consumer, these predicted values are inherently subject to unavoidable errors that can negatively impact the realized revenues...

In a two-sided marketplace, network effects are crucial for competitiveness, and platforms need to retain users through advanced customer relationship management as much as possible. Maintaining numerous providers' stable and active presence on the platform is highly important to enhance the marketplace's scale and diversity. The strongest motivati...

In two-sided marketplaces such as online flea markets, recommender systems for providing consumers with personalized item rankings play a key role in promoting transactions between providers and consumers. Meanwhile, two-sided marketplaces face the problem of balancing consumer satisfaction and fairness among items to stimulate activity of item pro...

This paper is concerned with portfolio optimization models for creating high-quality lists of recommended items to balance the accuracy and diversity of recommendations. However, the statistics (i.e., expectation and covariance of ratings) required for mean--variance portfolio optimization are subject to inevitable estimation errors. To remedy this...

This paper is concerned with the container pre-marshalling problem, which involves relocating containers in the storage area so that they can be efficiently loaded onto ships without reshuffles. In reality, however, ship arrival times are affected by various external factors, which can cause the order of container retrieval to be different from the...

In order to provide high-quality recommendations for users, it is desirable to share and integrate multiple datasets held by different parties. However, when sharing such distributed datasets, we need to protect personal and confidential information contained in the datasets. To this end, we establish a framework for privacy-preserving recommender...

Currently, many e-commerce websites issue online/electronic coupons as an effective tool for promoting sales of various products and services. We focus on the problem of optimally allocating coupons to customers subject to a budget constraint on an e-commerce website. We apply a robust portfolio optimization model based on customer segmentation to...

We study the mixed-integer optimization (MIO) approach to feature subset selection in nonlinear kernel support vector machines (SVMs) for binary classification. To measure the performance of subset selection, we use the distance between two classes (DBTC) in a high-dimensional feature space based on the Gaussian kernel function. However, DBTC to be...

This paper examines the relationship between user pageview (PV) histories and their itemchoice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user–item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV...

Survival analysis is a family of statistical methods for analyzing event occurrence times. We adopt a mixed-integer optimization approach to estimation of sparse Cox proportional hazards (PH) models for survival analysis. Specifically, we propose a high-performance cutting-plane algorithm based on a reformulation of our sparse estimation problem in...

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 conn...

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...

We study the mixed-integer optimization (MIO) approach to feature subset selection in nonlinear kernel support vector machines (SVMs) for binary classification. First proposed for linear regression in the 1970s, this approach has recently moved into the spotlight with advances in optimization algorithms and computer hardware. The goal of this paper...

Many mothers have considerable anxiety about pregnancy, childbirth, and childcare. For such mothers, searching for information on the Internet is an effective means of dissolving their anxieties. We consider the problem of estimating, for each search word, a distribution of search dates with respect to children’s birth dates. Most of the empirical...

Today’s engineering contractors face increasing uncertainty with EPC (Engineering, Procurement, Construction) projects because of increased project complexity and scale. Due to these circumstances, the number of joint venture contracts has increased among EPC contractors to reduce risks and increase profits. In this chapter, a method to design a co...

This paper studies a distributionally robust portfolio optimization model with a cardinality constraint for limiting the number of invested assets. We formulate this model as a mixed-integer semidefinite optimization (MISDO) problem by means of the moment-based uncertainty set of probability distributions of asset returns. To exactly solve large-sc...

This paper studies mean-risk portfolio optimization models using the conditional value-at-risk (CVaR) as a risk measure. We also employ a cardinality constraint for limiting the number of invested assets. Solving such a cardinality-constrained mean-CVaR model is computationally challenging for two main reasons. First, this model is formulated as a...

The study focuses on the strategic decisions including on the location and capacity of stations and the fleet size for designing the one-way station-based carsharing systems. Under demand uncertainty, we introduce a two-stage risk-averse stochastic model to maximize the mean return and minimize the risk, where the conditional value-at-risk (CVaR) i...

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 ad...

This paper discusses the prediction of hierarchical time series, where each upper-level time series is calculated by summing appropriate lower-level time series. Forecasts for such hierarchical time series should be coherent, meaning that the forecast for an upper-level time series equals the sum of forecasts for corresponding lower-level time seri...

This paper discusses the prediction of hierarchical time series, where each upper-level time series is calculated by summing appropriate lower-level time series. Forecasts for such hierarchical time series should be coherent, meaning that the forecast for an upper-level time series equals the sum of forecasts for corresponding lower-level time seri...

This paper addresses the problem of selecting a significant subset of candidate features to use for multiple linear regression. Bertsimas et al. [5] 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 res...

This paper studies the mean-risk portfolio optimization problems with a constraint of the number of assets to be invested. We employ conditional value-at-risk (CVaR) as a risk measure. While several studies aim at efficiently solving mean-CVaR model, it has been computationally expensive to solve cardinality-constrained mean-CVaR model, and especia...

This paper is concerned with examining the relationship between users' page view (PV) history and their item-choice behavior on an e-commerce website. We focus particularly on the PV sequence, which represents a time series of the number of PVs for each user--item pair. We propose a shape-restricted optimization model to accurately estimate item-ch...

We consider a cutting-plane algorithm for solving mixed-integer semidefinite optimization (MISDO) problems. In this algorithm, the positive semidefinite (psd) constraint is relaxed, and the resultant mixed-integer linear optimization problem is solved repeatedly, imposing at each iteration a valid inequality for the psd constraint. We prove the con...

This paper is concerned with the cross-validation criterion for selecting the best subset of explanatory variables in a linear regression model. In contrast with the use of statistical criteria (e.g., Mallows’ \(C_p\), the Akaike information criterion, and the Bayesian information criterion), cross-validation requires only mild assumptions, namely,...

This paper is concerned with many-to-one matching problems for assigning resident physicians (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. These two objectives however are generally incompatible. We focus on a...

For practical applications of collaborative filtering, we need a user-item rating matrix that encodes user preferences for items. However, estimation of user preferences is inevitably affected by some degree of noise, which can markedly degrade the recommender performance. The primary aim of this research is to obtain a high-quality rating matrix b...

This paper is concerned with a mixed-integer optimization (MIO) approach to selecting a subset of relevant features from among many candidates. For ordinal classification, a sequential logit model and an ordered logit model are often employed. For feature subset selection in the sequential logit model, Sato et al.[22] recently proposed a mixed-inte...

Multicollinearity exists when some explanatory variables of a multiple linear regression model are highly correlated. High correlation among explanatory variables reduces the reliability of the analysis. To eliminate multicollinearity from a linear regression model, we consider how to select a subset of significant variables by means of the varianc...

We study a dynamic scheduling method for the state-dependent work based on the resource flow within a process of the work. Namely, we develop a multistage dynamic scheduling model consisting of N classes of activities and a three-layer control structure. Then, we devise a resource flow based order selection method and resource allocation method to...

Since project price is determined before the start of a project, project cost estimation is a critical work for the EPC (Engineering-Procurement-Construction) contractor in accepting profitable projects in competitive bidding situations. The contractor should devote significant time and resources to accurate cost estimation of project orders from c...

To win a project contract through competitive bidding, contractors submit a bid price that is determined by putting a markup on the estimated project cost. The success of the bid is therefore heavily dependent on the accuracy of that estimate, meaning that sufficient resources should be allocated to the estimation process. This paper develops a nov...

This paper proposes a method for eliminating multicollinearity from linear regression models. Specifically, we select the best subset of explanatory variables subject to the upper bound on the condition number of the correlation matrix of selected variables. We first develop a cutting plane algorithm that, to approximate the condition number constr...

In competitive bidding for project contracts, contractors estimate the cost of completing a project and then determine the bid price. Accordingly, the bid price is markedly affected by the inaccuracies in the estimated cost. To establish a profit-making strategy in competitive bidding, it is crucial for contractors to estimate project costs accurat...

This paper is concerned with the nonparametric item response theory (NIRT) for estimating item characteristic curves (ICCs) and latent abilities of examinees on educational and psychological tests. In contrast to parametric models, NIRT models can estimate various forms of ICCs under mild shape restrictions, such as the constraints of monotone homo...

This paper is concerned with a store-choice model for investigating consumers' store-choice behavior based on scanner panel data. Our store-choice model enables us to evaluate the effects of the consumer/product attributes not only on the consumer's store choice but also on his/her purchase quantity. Moreover, we adopt a mixed-integer optimization...

This paper analyzes customer product-choice behavior based on the recency and frequency of each customer's page views on e-commerce sites. Recently, we devised an optimization model for estimating product-choice probabilities that satisfy monotonicity, convexity, and concavity constraints with respect to recency and frequency. This shape-restricted...

The cost estimation process, carried out by the contractor before the start of a project, is a critical activity for the contractor in accepting profitable EPC projects in competitive bidding situations. Thus, the contractor should devote significant time and resources to the accurate cost estimation of project orders from clients. However, it is i...

This paper concerns a method of selecting a subset of features for a logistic regression model. Information criteria, such as the Akaike information criterion and Bayesian information criterion, are employed as a goodness-of-fit measure. The purpose of our work is to establish a computational framework for selecting a subset of features with an opt...

To determine the bidding prices in Engineering-Procurement-Construction (EPC) projects, where contract prices are fixed, the contractor needs to consider the accuracy of estimated project cost under the limited engineering Man-Hours (MH) for cost estimation to attain maximum profit from or- ders. In this paper, we develop an algorithm where, in ord...

This paper investigates the relationship between customers’ page views (PVs) and the probabilities of their product choices on e-commerce sites. For this purpose, we create a probability table consisting of product-choice probabilities for all recency and frequency combinations of each customers’ previous PVs. To reduce the estimation error when th...

This paper concerns a method of selecting a subset of features for a
sequential logit model. Tanaka and Nakagawa (2014) proposed a mixed integer
quadratic optimization formulation for solving the problem based on a quadratic
approximation of the logistic loss function. However, since there is a
significant gap between the logistic loss function and...

In this paper, we develop a heuristic bidding price decision algorithm in consideration of cost estimation accuracy under limited engineering Man-Hours (MH) in Engineering, Procurement, Construction (EPC) projects. It allocates engineering MH for cost estimation, which determines the cost estimation accuracy, to each order under the limited volume...

This study concerns a method of selecting the best subset of explanatory variables in a multiple linear regression model. Goodness-of-fit measures, for example, adjusted R2, AIC, and BIC, are generally used to evaluate a subset regression model. Although variable selection with regard to these measures is usually performed with a stepwise regressio...

This paper concerns a method of selecting the best subset of explanatory variables for a linear regression model. Employing Mallows’ CpCp as a goodness-of-fit measure, we formulate the subset selection problem as a mixed integer quadratic programming problem. Computational results demonstrate that our method provides the best subset of variables in...

This paper studies a nonlinear control policy for multi-period investment. The nonlinear strategy we implement is categorized as a kernel method, but solving large-scale instances of the resulting optimization problem in a direct manner is computationally intractable in the literature. In order to overcome this difficulty, we employ a dimensionalit...

Accurate cost estimation is essential for any Engineering–Procurement–Construction (EPC) contractor accepting profitable projects because the project price is determined prior to receiving the contract. Therefore the contractor needs to ensure engineering man-hours (MH) in order to estimate project costs accurately as well as carry out the accepted...

This paper studies the mean-risk portfolio optimization problem with nonconvex transaction costs. We employ the conditional value-at-risk (CVaR) as a risk measure. There are a number of studies that aim at efficiently solving large-scale CVaR minimization problems. None of these studies, however, take into account nonconvex transaction costs, which...

This paper develops a stochastic dynamic programming model for establishing an optimal sequential bidding strategy in a competitive bidding situation. In competitive bidding, a contractor usually sets the bid price of each contract by putting a markup on the estimated cost, and consequently, the bid price is affected by a cost estimation error. We...

We build a computational framework for determining an optimal dynamic asset allocation over multiple periods. To do this, we use a nonlinear control policy, which is a function of past returns of investable assets. By employing a kernel method, the problem of selecting the best control policy from among nonlinear functions can be formulated as a co...

In this paper, we study a multi-period portfolio optimization where conditional value-atrisk (CVaR) is controlled as well as expected return, and the so-called constant rebalancing strategy is employed under nonlinear transaction costs. In general, the optimization of this strategy itself is, however, difficult to attain a globally optimal solution...

Credit risk is the risk of loss stemming from borrower's default. We consider the credit risk minimization problem and propose an optimization method for minimizing the risk measured by Conditional Value-at-Risk (CVaR) criterion. Default of firms is modeled by the corporate valuation model and the factor analysis of time series data of TOPIX Sector...

We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion. In order to solve the POPs of high degree, we develop a cutting-plane algorithm based on semidefinite programming. Our algorithm can solve proble...

In this paper, we address an approximate solution of a probabilistically constrained convex program (PCCP), where a convex objective function is minimized over solutions satisfying, with a given probability, convex constraints that are parameterized by random variables. In order to approach to a solution, we set forth a conservative approximation p...

We prove that the earth mover's distance problem reduces to a problem with half the number of constraints regardless of the ground distance, and propose a further reduced formulation when the ground distance comes from a graph with a homogeneous neighborhood structure. We also propose to apply our formulation to the non-negative matrix factorizatio...

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 context of the well-known single-period newsvendor problem, which is originally formulated as the maximization of the expected profit or the minimization of the expected cost. We show that downside...

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 context of the well-known single-period news-vendor problem which is originally formulated as the maximization of the expected profit, or the minimization of the expected cost. We show that downsid...