Zsolt Csizmadia

New criss-cross type algorithms for linear complementarity problems with sufficient matrices

We generalize new criss-cross type algorithms for linear complementarity problems (LCPs) given with sufficient matrices. Most LCP solvers require a priori information about the input matrix. The sufficiency of a matrix is hard to be checked (no polynomial time method is known). Our algorithm is similar to Zhangs linear programming and Akkeles¸, Balogh and Illéss criss-cross type algorithm for LCP-QP problems. We modify our basic algorithm in such a way that it can start with any matrix M, without having any information about the properties of the matrix (sufficiency, bisymmetry, positive definiteness, etc.) in advance. Even in this case, our algorithm terminates with one of the following cases in a finite number of steps: it solves the LCP problem, it solves its dual problem or it gives a certificate that the input matrix is not sufficient, thus cycling can occur. Although our algorithm is more general than that of Akkeles¸, Balogh and Illéss, the finiteness proof has been simplified. Furthermore, the finiteness proof of our algorithm gives a new, constructive proof to Fukuda and Terlakys LCP duality theorem as well.

A stochastic programming and simulation based analysis of the structure of production on the arable land

This paper is devoted to the analysis of the effectiveness of the use of arable land. This is an issue, which is important for national-level decision makers. The particular calculations are carried out for Hungary, but similar analysis can be made for each country having several parts with different geographical conditions.In general the structure of the use of arable land has been developed in an evolutionary manner in each country. This paper is devoted to the evaluation of the effectiveness of this structure. Some main crops must be included in the analysis such that the land used for their production is a high percentage in the total arable land of the country. From agricultural point of view the question to be answered is whether or not the same level of supply is achievable with high probability on a smaller area. As the agriculture is affected by stochastic factors via the weather, no supply can be guaranteed up to 100 per cent. Thus each production structure provides the required supply only with a certain probability. One inequality corresponding to each crop must be satisfied at the same time with a prescribed probability. The main theoretical difficulty here is that the inequalities are not independent from one another from stochastic point of view as the yields of the crops are highly correlated. The problem is modeled by a chance constrained stochastic programming model such that the stochastic variables are on the left-hand side of the inequalities, while the right-hand sides are constants. Kataoka was the first in 1963 who solved a similar problem with a single inequality in the probabilistic constraint. The mathematical analysis of the present problem is using the results of Kataoka. This problem is solved numerically via discretization.Numerical results for the optimal structure of the production are presented for the case of Hungary. It is shown that a much higher probability, i.e. a more safe supply, can be achieved on a smaller area than what is provided by the current practice.

The s-monotone index selection rules for pivot algorithms of linear programming

In this paper we introduce the concept of s-monotone index selection rule for linear programming problems. We show that several known anti-cycling pivot rules like the minimal index, Last-In–First-Out and the most-often-selected-variable pivot rules are s-monotone index selection rules. Furthermore, we show a possible way to define new s-monotone pivot rules. We prove that several known algorithms like the primal (dual) simplex, MBU-simplex algorithms and criss-cross algorithm with s-monotone pivot rules are finite methods.

Anstreicher-Terlaky type monotonic simplex algorithms for linear feasibility problems

Based on the pivot selection rule [Anstreicher, K.M. and Terlaky, T., 1994, A monotonic build-up simplex algorithm for linear programming. Operations Research, 42, 556–561.] we define a new monotonic build-up (MBU) simplex algorithm for linear feasibility problems. An mK upper bound for the iteration bound of our algorithm is given under a weak non-degeneracy assumption, where K is determined by the input data of the problem and m is the number of constraints. The constant K cannot be bounded in general by a polynomial of the bit length of the input data since it is related to the determinants (of the pivot tableau) with the highest absolute value. An interesting local property of degeneracy led us to construct a new recursive procedure to handle strongly degenerate problems as well. Analogous complexity bounds for the linear programming versions of MBU and the first phase of the simplex method can be proved under our weak non-degeneracy assumption.

The s-monotone index selection rule for criss-cross algorithms of linear complementarity problems

Abstract In this paper we introduce the s-monotone index selection rules for the well-known criss-cross method for solving the linear complementarity problem (LCP). Most LCP solution methods require a priori information about the properties of the input matrix. One of the most general matrix properties often required for finiteness of the pivot algorithms (or polynomial complexity of interior point algorithms) is sufficiency. However, there is no known polynomial time method for checking the sufficiency of a matrix (classification of column sufficiency of a matrix is co-NP-complete). Following the ideas of Fukuda, Namiki and Tamura, using Existentially Polynomial (EP)-type theorems, a simple extension of the crisscross algorithm is introduced for LCPs with general matrices. Computational results obtained using the extended version of the criss-cross algorithm for bi-matrix games and for the Arrow-Debreu market equilibrium problem with different market size is presented.