A mixed integer linear programming formulation of the optimal mean/Value-at-Risk portfolio problem

In this paper, we consider an extension of the Markovitz model, in which the variance has been replaced with the Value-at-Risk. So a new portfolio optimization problem is formulated. We showed that the model leads to an NP-hard problem, but if the number of past observation T or the number of assets K are low, e.g. fixed to a constant, polynomial time algorithms exist. Furthermore, we showed that the problem can be formulated as an integer programming instance. When K and T are large and α^VaR is small—as common in financial practice—the computational results show that the problem can be solved in a reasonable amount of time.     

A mixed integer linear programming formulation of the maximum betweenness problem

This paper considers the maximum betweenness problem. A new mixed integer linear programming (MILP) formulation is presented and validity of this formulation is given. Experimental results are performed on randomly generated instances from the literature. The results of CPLEX solver, based on the proposed MILP formulation, are compared with results obtained by total enumeration technique. The results show that CPLEX optimally solves instances of up to 30 elements and 60 triples in a short period of time.     

Cutting plane algorithms for the inverse mixed integer linear programming problem

We present cutting plane algorithms for the inverse mixed integer linear programming problem (InvMILP), which is to minimally perturb the objective function of a mixed integer linear program in order to make a given feasible solution optimal.     

Marginal values in mixed integer linear programming

For a given optimization problem, P, considered as a function of the data, its marginal values are defined as the directional partial derivatives of the value of P with respect to perturbations in that data. For linear programs, formulas for the marginal values were given by Mills, [10], and further developed by the current author [16]. In this paper, the marginal value formulas are extended to the case of mixed integer linear programming (MIP). As in ordinary linear programming, discontinuities in the value can occur, and the analysis here identifies them. This latter aspect extends previous work on continuity by the current author, [18], Geoffrion and Nauss, [5], Nauss, [11], and Radke, [12], and work on the value function of Blair and Jeroslow, [2]. Application is made to model formulation and to post-optimal analysis.Supported in part by the Air Force Office of Scientific Research, Grant # AFSOR-0271 to Rutgers University.     

A mixed integer programming model for multistage mean–variance post-tax optimization

In this paper, we introduce a mixed integer stochastic programming approach to mean–variance post-tax portfolio management. This approach takes into account of risk in a multistage setting and allows general withdrawals from original capital. The uncertainty on asset returns is specified as a scenario tree. The risk across scenarios is addressed using the probabilistic approach of classical stochastic programming. The tax rules are used with stochastic linear and mixed integer quadratic programming models to compute an overall tax and return-risk efficient multistage portfolio. The incorporation of the risk term in the model provides robustness and leads to diversification over wrappers and assets within each wrapper. General withdrawals and risk aversion have an impact on the distribution of assets among wrappers. Computational results are presented using a study with different scenario trees in order to show the performance of these models.     

A note on “A new local and global optimization method for mixed integer quadratic programming problems” by G.Q. Li et al.

In this comment, we preset a minor mistake in typing which is made in “A new local and global optimization method for mixed integer quadratic programming problems” by G.Q. Li et al.     

整数线性规划投资组合模型的直接搜索算法求解

利用松弛最优邻近解临域整数点搜索法作过滤条件,建立求解整数规划的新方法——直接搜索算法,利用直接搜索算法并借助Matlab软件求解整数线性规划投资组合模型.数值结果表明了模型的建立与提出方法的有效性.     

A branch-and-bound algorithm for 0–1 parametric mixed integer programming

A branch-and-bound algorithm to solve 0–1 parametric mixed integer linear programming problems has been developed. The present algorithm is an extension of the branch-and-bound algorithm for parametric analysis on pure integer programming. The characteristic of the present method is that optimal solutions for all values of the parameter can be obtained.     

A note on “A mixed integer programming model for advanced planning and scheduling (APS)”

In a recent paper, Chen and Ji [Chen, K., Ji, P., 2007. A mixed integer programming model for advanced planning and scheduling (APS). European Journal of Operational Research 181, 515–522] develop a mixed integer programming model for advanced planning and scheduling problem that considers capacity constraints and precedence relations between the operations. The orders require processing of several operations on eligible machines. The model presented in the above paper works for the case where each operation can be processed on only one machine. However, machine eligibility means that only a subset of machines are capable of processing a job and this subset may include more than one machine. We provide a general model for advanced planning and scheduling problems with machine eligibility. Our model can be used for problems where there are alternative machines that an operation can be assigned to.     

A mixed integer linear programming model for optimal sovereign debt issuance

Governments borrow funds to finance the excess of cash payments or interest payments over receipts, usually by issuing fixed income debt and index-linked debt. The goal of this work is to propose a stochastic optimization-based approach to determine the composition of the portfolio issued over a series of government auctions for the fixed income debt, to minimize the cost of servicing debt while controlling risk and maintaining market liquidity. We show that this debt issuance problem can be modeled as a mixed integer linear programming problem with a receding horizon. The stochastic model for the interest rates is calibrated using a Kalman filter and the future interest rates are represented using a recombining trinomial lattice for the purpose of scenario-based optimization. The use of a latent factor interest rate model and a recombining lattice provides us with a realistic, yet very tractable scenario generator and allows us to do a multi-stage stochastic optimization involving integer variables on an ordinary desktop in a matter of seconds. This, in turn, facilitates frequent re-calibration of the interest rate model and re-optimization of the issuance throughout the budgetary year allows us to respond to the changes in the interest rate environment. We successfully demonstrate the utility of our approach by out-of-sample back-testing on the UK debt issuance data.     

On a pair of nonlinear mixed integer programming problems

We consider maximin and minimax nonlinear mixed integer programming problems which are nonsymmetric in duality sense. Under weaker (pseudo-convex/pseudo-concave) assumptions, we show that the supremum infimum of the maximin problem is greater than or equal to the infimum supremum of the minimax problem. As a particular case, this result reduces to the weak duality theorem for minimax and symmetric dual nonlinear mixed integer programming problems. Further, this is used to generalize available results on minimax and symmetric duality in nonlinear mixed integer programming.     

A new mixed integer programming formulation for facility layout design using flexible bays

This paper presents a mixed-integer programming formulation to find optimal solutions for the block layout problem with unequal departmental areas arranged in flexible bays. The nonlinear department area constraints are modeled in a continuous plane without using any surrogate constraints. The formulation is extensively tested on problems from the literature.     

Complete mixed integer linear programming formulations for modularity density based clustering

Modularity density maximization is a clustering method that improves some issues of the commonly used modularity maximization approach. Recently, some Mixed-Integer Linear Programming (MILP) reformulations have been proposed in the literature for the modularity density maximization problem, but they require as input the solution of a set of auxiliary binary Non-Linear Programs (NLPs). These can become computationally challenging when the size of the instances grows. In this paper we propose and compare some explicit MILP reformulations of these auxiliary binary NLPs, so that the modularity density maximization problem can be completely expressed as MILP. The resolution time is reduced by a factor up to two order of magnitude with respect to the one obtained with the binary NLPs.     

A mixed integer programming model for multiple stage adaptive testing

The last decade has seen paper-and-pencil (P&P) tests being replaced by computerized adaptive tests (CATs) within many testing programs. A CAT may yield several advantages relative to a conventional P&P test. A CAT can determine the questions or test items to administer, allowing each test form to be tailored to a test taker’s skill level. Subsequent items can be chosen to match the capability of the test taker. By adapting to a test taker’s ability, a CAT can acquire more information about a test taker while administering fewer items. A Multiple Stage Adaptive test (MST) provides a means to implement a CAT that allows review before the administration. The MST format is a hybrid between the conventional P&P and CAT formats. This paper presents mixed integer programming models for MST assembly problems. Computational results with commercial optimization software will be given and advantages of the models evaluated.     

On the greedy solution in integer linear programming

A version of the greedy method not using any knapsack relaxation of the integer programming problem is considered in this paper. It is based on a natural partial ordering of the vectors. Our aim is to determine a large class of problems where the greedy solution is always optimal. The results generalize some theorems of an early paper of Magazine, Nemhauser and Trotter and at the same time show a connection between two different notions of combinatorics: the greedy method and the Hilbert basis.

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Zusammenfassung In dieser Arbeit wird eine Version des Greedy-Algorithmus zur Lösung ganzzahliger linearer Optimierungsprobleme benutzt, die kein Rucksackproblem als Relaxation verwendet. Das Verfahren basiert auf der natürlichen partiellen Ordnung von Vektoren. Ziel der Arbeit ist es, eine möglichst große Problemklasse zu beschreiben, für die die Greedy-Lösung optimal ist. Die Ergebnisse verallgemeinern Sätze einer früheren Arbeit von Magazine, Nemhauser und Trotter und zeigen gleichzeitig einen Bezug zwischen zwei verschiedenen Gebieten der Kombinatorik auf: des Greedy-Verfahrens und von Hubert-Basen.

     

An integer linear programming approach for a class of bilinear integer programs

We propose an Integer Linear Programming (ILP) approach for solving integer programs with bilinear objectives and linear constraints. Our approach is based on finding upper and lower bounds for the integer ensembles in the bilinear objective function, and using the bounds to obtain a tight ILP reformulation of the original problem, which can then be solved efficiently. Numerical experiments suggest that the proposed approach outperforms a latest iterative ILP approach, with notable reductions in the average solution time.     

A two-stage approach for bi-objective integer linear programming

We present a new exact approach for solving bi-objective integer linear programs. The new approach employs two of the existing exact algorithms in the literature, including the balanced box and the $?$-constraint methods, in two stages. A computationally study shows that the new approach has three desirable characteristics. (1) It solves less single-objective integer linear programs. (2) Its solution time is significantly smaller. (3) It is competitive with the two-stage algorithm proposed by Leitner et al. (2016).     

Globally optimal clusterwise regression by mixed logical-quadratic programming

Exact global optimization of the clusterwise regression problem is challenging and there are currently no published feasible methods for performing this clustering optimally, even though it has been over thirty years since its original proposal. This work explores global optimization of the clusterwise regression problem using mathematical programming and related issues. A mixed logical-quadratic programming formulation with implication of constraints is presented and contrasted against a quadratic formulation based on the traditional big-M, which cannot guarantee optimality because the regression line coefficients, and thus errors, may be arbitrarily large. Clusterwise regression optimization times and solution optimality for two clusters are empirically tested on twenty real datasets and three series of synthetic datasets ranging from twenty to one hundred observations and from two to ten independent variables. Additionally, a few small real datasets are clustered into three lines.     

Multicriteria dynamic programming with an application to the integer case

Fundamental dynamic programming recursive equations are extended to the multicriteria framework. In particular, a more detailed procedure for a general recursive solution scheme for the multicriteria discrete mathematical programming problem is developed. Definitions of lower and upper bounds are offered for the multicriteria case and are incorporated into the recursive equations to aid problem solution by eliminating inefficient subpolicies. Computational results are reported for a set of 0–1 integer linear programming problems.This research was supported in part by CONACYT (Consejo Nacional de Ciencia y Technologia), Mexico City, Mexico.