Linear Complementarity Research Articles

Pricing of American Carbon Emission Derivatives and Numerical Method under the Mixed Fractional Brownian Motion

This paper studies the pricing of American carbon emission derivatives and its numerical method under the mixed fractional Brownian motion. To capture the long memory properties such as self-similarity and long-range dependence in the price process, we proposed a model based on a fractional Black–Scholes, which is more in line with the actual characteristics of the option market. We have outlined a power penalty approach using parabolic variation inequality and linear complementarity (LCP) which arises from mixed fractional Brownian motion. In addition, we introduced a nonuniform grid-based modification of the fitted finite volume method for numerical solution. Numerically, we show the impact of Hurst exponent on the pricing and analyze the convergence rates of the proposed penalty method. In conclusion, since mfBm is a well-developed mathematical model of strongly correlated stochastic processes, this model will be an efficient model for pricing carbon financial derivative.

Complementarity Model of a Photovoltaic Power Electronic System With Model Predictive Control

The modeling and control problem for a grid-connected photovoltaic (PV) power electronic system, which includes a dc/dc boost converter, an inverter and a filter are considered. A linear complementarity (LC) dynamic model of the PV system allows the design of a model predictive controller (MPC). Dynamic models of the subsystems are obtained and merged in order to represent the whole PV system in a compact and comprehensive LC model, which is valid for all operating modes of power converters and PV cells involved in the energy conversion process. A finite-control-set MPC problem is formulated as a mixed-integer quadratic program subject to the dynamic LC model and pulse width modulators. The minimization of an objective function aimed at tracking dc voltage and grid current references provides directly the commands for the switches of the boost converter and inverter. Numerical results show the effectiveness of the proposed strategy for maximum power point tracking and synchronization to the grid under dynamic scenarios characterized by variations of the solar irradiance.

A majorization–minimization algorithm for split feasibility problems

The classical multi-set split feasibility problem seeks a point in the intersection of finitely many closed convex domain constraints, whose image under a linear mapping also lies in the intersection of finitely many closed convex range constraints. Split feasibility generalizes important inverse problems including convex feasibility, linear complementarity, and regression with constraint sets. When a feasible point does not exist, solution methods that proceed by minimizing a proximity function can be used to obtain optimal approximate solutions to the problem. We present an extension of the proximity function approach that generalizes the linear split feasibility problem to allow for non-linear mappings. Our algorithm is based on the principle of majorization–minimization, is amenable to quasi-Newton acceleration, and comes complete with convergence guarantees under mild assumptions. Furthermore, we show that the Euclidean norm appearing in the proximity function of the non-linear split feasibility problem can be replaced by arbitrary Bregman divergences. We explore several examples illustrating the merits of non-linear formulations over the linear case, with a focus on optimization for intensity-modulated radiation therapy.

The Problem of Projecting the Origin of Euclidean Space onto the Convex Polyhedron

This paper is aimed at presenting a systematic survey of the existing now different formulations for the problem of projection of the origin of the Euclidean space onto the convex polyhedron (PPOCP). In the present paper, there are investigated the reduction of the projection program to the problems of quadratic programming, maximin, linear complementarity, and nonnegative least squares. Such reduction justifies the opportunity of utilizing a much more broad spectrum of powerful tools of mathematical programming for solving PPOCP. The paper's goal is to draw the attention of a wide range of research at the different formulations of the projection problem, which remain largely unknown due to the fact that the papers (addressing the subject of concern) are published even though on the adjacent, but other topics, or only in the conference proceedings.

Solutions of parametric complementarity problems monotone with respect to parameters

In many applied problems (such as, e.g., elasto-hydrodynamic lubrication problem, some economic equilibrium problems, etc.), one of the important question is if certain complementarity problem's solution is monotone with respect to parameters. Our paper investigates this question and provides several sufficient conditions that guarantee such a monotonicity of the solutions to linear and nonlinear complementarity problems with parameters. In the majority of cases, it is required that the principal mapping of the complementarity problem be monotone by decision variables and, vice versa, antitone with respect to parameters.

A New Heuristic for the Convex Quadratic Programming Problem

This paper presents a new heuristic to linearise the convex quadratic programming problem. The usual Karush-Kuhn-Tucker conditions are used but in this case a linear objective function is also formulated from the set of linear equations and complementarity slackness conditions. An unboundedness challenge arises in the proposed formulation and this challenge is alleviated by construction of an additional constraint. The formulated linear programming problem can be solved efficiently by the available simplex or interior point algorithms. There is no restricted base entry in this new formulation. Some computational experiments were carried out and results are provided.

Interior hybrid proximal extragradient methods for the linear monotone complementarity problem

We present new infeasible path-following methods for linear monotone complementarity problems based on Auslender, Teboulle and Ben-Tiba’s log-quadratic barrier functions. The central paths associated with these barriers are always well defined and, for those problems which have a solution, convergent to a pair of complementary solutions. Starting points in these paths are easy to compute. The theoretical iteration-complexity of these new path-following methods is derived and improved by a strategy which uses relaxed hybrid proximal-extragradient steps to control the quadratic term. Encouraging preliminary numerical experiments are presented.

A majorized penalty approach to inverse linear second order cone programming problems

This paper focuses on a type of inverse linear second order cone programming (LSOCP) problems which require us to adjust the parameters in both the objective function and the constraint set of a given LSOCP problem as little as possible so that a known feasible solution becomes optimal one. This inverse problem can be formulated as a linear second order cone complementarity constrained optimization problem and is difficult to solve due to the presence of second order cone complementarity constraint. To solve this difficult problem, we first partially penalize the inverse problem and then propose the majorization approach to the penalized problem by solving a sequence of convex optimization problems with quadratic objective function and simple second order cone constraints. Numerical results demonstrate the efficiency of our approach to inverse LSOCP problems.

Finite Maturity Optimal Stopping of Levy Processes with Running Cost, Stopping Cost and Terminal Gain

Motivated by problems in mathematical finance and insurance, this paper discusses optimal stopping problem in general setting. It considers discounted running cost and stopping cost in addition to terminal gain in the objective function, subject to be optimized over finite-time period. The underlying source of uncertainty is modeled by Levy processes. We derive early exercise premium representation for the value function based on a partial integro-differential free-boundary problem associated with the optimal stopping problem. The representation gives rise to a nonlinear integral equation for the optimal stopping boundary. The integral equation generalizes that of found in Kim (1990), Myneni (1992), Carr et al. (1990), Jacka (1991), Pham (1997), and Peskir (2005). The boundary can be characterized as a unique solution of the integral equation within the class of continuous decreasing function of time to maturity. We show that the continuity of the boundary holds when the stopping cost function is either time-independent or decreasing in time. Uniqueness of such solution holds when the running cost and stopping cost functions satisfy a differential inequality. By reformulating the free-boundary problem as a linear complementarity, the problem is solved iteratively by adapting the implicit-explicit method of Cont and Voltchkova (2005) and the Brennan-Schwartz (1977) algorithm that was implemented in Jaillet et al. (1990) and Almendral (2005) for the pricing of American put option. We give an example in optimal capital structure. We also verify numerically the recent results in Kyprianou and Surya (2007) that the smooth pasting condition may not hold for general Levy processes.

On solving Linear Complementarity Problems by DC programming and DCA

In this paper, we consider four optimization models for solving the Linear Complementarity (LCP) Problems. They are all formulated as DC (Difference of Convex functions) programs for which the unified DC programming and DCA (DC Algorithms) are applied. The resulting DCA are simple: they consist of solving either successive linear programs, or successive convex quadratic programs, or simply the projection of points on \(\mathbb{R}_{+}^{2n}\). Numerical experiments on several test problems illustrate the efficiency of the proposed approaches in terms of the quality of the obtained solutions, the speed of convergence, and so on. Moreover, the comparative results with Lemke algorithm, a well known method for the LCP, show that DCA outperforms the Lemke method.

General primal-dual penalty/barrier path-following Newton methods for nonlinear programming

In the present article rather general penalty/barrier-methods are considered, that define a local continuously differentiable primal-dual path. The class of penalty/barrier terms includes most of the usual techniques like logarithmic barriers, SUMT, quadratic loss functions as well as exponential penalties, and the optimization problem which may contain inequality as well as equality constraints. The convergence of the corresponding general primal-dual path-following method is shown for local minima that satisfy strong second-order sufficiency conditions with linear independence constraint qualification (LICQ) and strict complementarity. A basic tool in the analysis of these methods is to estimate the radius of convergence of Newton's method depending on the penalty/barrier-parameter. Without using self-concordance properties convergence bounds are derived by direct estimations of the solutions of the Newton equations. Parameter selection rules are proposed which guarantee the local convergence of the considered penalty/barrier-techniques with only a finite number of Newton steps at each parameter level. Numerical examples illustrate the practical behavior of the proposed class of methods.

An M-Matrix Based Efficient Algorithm for Model Predictive Control of Input Constrained Linear Systems

This paper discusses an on-line approach to Model Predictive Control (MPC) of input constrained linear systems. First, it is shown that an example of MPC with one-side input constraints is reduced into the Linear Complementarity (LC) Problem with M-matrix, which can be more efficiently solved by the existing special algorithm than the other general algorithms such as the Lemke method. However, in the case of both-side input constraints, which is more practical from the control engineering points of view, it cannot be reduced into the LC problem with M-matrix. Thus, next, a new algorithm for the case of both-side input constraints is proposed in this paper.

入力拘束システムのモデル予測制御に対する<i>M</i>-行列を用いた高速解法

Abstract This paper discusses an on-line approach to Model Predictive Control (MPC) of input constrained linear systems. First, it is shown that an example of MPC with one-side input constraints is reduced into the Linear Complementarity (LC) Problem with M -matrix, which can be more efficiently solved by the existing special algorithm than the other general algorithms such as the Lemke method. However, in the case of both-side input constraints, which is more practical from the control engineering points of view, it cannot be reduced into the LC problem with M -matrix. Thus, next, a new algorithm for the case of both-side input constraints is proposed in this paper.

Hybrid system analysis and control via mixed integer optimization

The paper discusses a framework for modeling, analyzing and controlling systems whose behavior is governed by interdependent physical laws, logic rules, and operating constraints, denoted as Mixed Logical Dynamical (MLD) systems. They are described by linear dynamic equations subject to linear inequalities involving real and integer variables. MLD models are equivalent to various other system descriptions like Piece Wise Affine (PWA) systems and Linear Complementarity (LC) systems. They have the advantage, however, that all problems of system analysis (like controllability, observability, stability and verification) and all problems of synthesis (like controller design and filter design) can be readily expressed as mixed integer linear or quadratic programs, for which many commercial software packages exist.In this paper we first recall how to derive MLD models and then illustrate their use in predictive control. Subsequently we define “verification” and show how verification algorithms can be used to solve a variety of practical problems like checking the correctness of an emergency shutdown procedure implemented on a PLC, or assessing the performance of a constrained MPC controller. The eventual practical success of these methods will depend on progress in the development of the various optimization packages so that problems of realistic size can be tackled.

Equivalence of hybrid dynamical models

This paper establishes equivalences among five classes of hybrid systems: mixed logical dynamical (MLD) systems, linear complementarity (LC) systems, extended linear complementarity (ELC) systems, piecewise affine (PWA) systems, and max-min-plus-scaling (MMPS) systems. Some of the equivalences are established under (rather mild) additional assumptions. These results are of paramount importance for transferring theoretical properties and tools from one class to another, with the consequence that for the study of a particular hybrid system that belongs to any of these classes, one can choose the most convenient hybrid modeling framework.

Option pricing and linear complementarity

Option pricing and linear complementarity

A Newton Acceleration of the Weiszfeld Algorithm for Minimizing the Sum of Euclidean Distances

The Weiszfeld algorithm for continuous location problems can be considered as an iteratively reweighted least squares method. It generally exhibits linear convergence. In this paper, a Newton algorithm with similar simplicity is proposed to solve a continuous multifacility location problem with the Euclidean distance measure. Similar to the Weiszfeld algorithm, the main computation can be solving a weighted least squares problem at each iteration. A Cholesky factorization of a symmetric positive definite band matrix, typically with a small band width (e.g., a band width of two for a Euclidean location problem on a plane) is performed. This new algorithm can be regarded as a Newton acceleration to the Weiszfeld algorithm with fast global and local convergence. The simplicity and efficiency of the proposed algorithm makes it particularly suitable for large-scale Euclidean location problems and parallel implementation. Computational experience suggests that the proposed algorithm often performs well in the absence of the linear independence or strict complementarity assumption. In addition, the proposed algorithm is proven to be globally convergent under similar assumptions for the Weiszfeld algorithm. Although local convergence analysis is still under investigation, computation results suggest that it is typically superlinearly convergent.

On the application of the ABS algorithm to linear programming and linear complementarity

In this paper, we discuss the application of the ABS algorithm to the simplex method, the dual simplex method, the linear complementary problem. We consider the ABS formulation of the stopping criterion, the search direction, the minimal rule to determine the vectors entering and leaving the basis matrix and the updating of the Abaffian matrix after a basis vector exchange. The Lemke algorithm for the LCP problem is reformulated in terms of the ABS procedure

Basic lemmas in polynomial-time infeasible-interiorpoint methods for linear programs

The primal-dual infeasible-interior-point algorithm is known as one of the most efficient computational methods for linear programs. Recently, a polynomial-time computational complexity bound was established for special variants of the algorithm. However, they impose severe restrictions on initial points and require a common step length in the primal and dual spaces. This paper presents some basic lemmas that bring great flexibility and improvement into such restrictions on initial points and step lengths, and discusses their extensions to linear and nonlinear monotone complementarity problems.

Analysis of Bifurcation Due to Loss of Linear Independence and Strict Complementarity for Penalty Methods for Solving Constrained Optimization Problems, II

In this article we investigate the structure and persistence of critical point solutions of the nonlinear programming problem obtained from the quadratic penalty function, the logarithmic-barrier function, and the multiplier method. The analysis focuses on singularities arising from the loss of the linear independence constraint qualification and the loss of strict complementarity. The programming problem is first formulated as a system of equations using the Fritz John first order necessary conditions and a nonstandard normalization of the multipliers. The singularities of this system are then classified and solutions are investigated at each type of singularity of codimension zero and one in terms of the bifurcation behavior and persistence of minima of the critical point curves.