Symbolic decision procedure for termination of linear programs

Tiwari proved that the termination of a class of linear programs is decidable in Tiwari (Proceedings of CAV’04. Lecture notes in computer science, vol 3114, pp 70–82, 2004). The decision procedure proposed therein depends on the computation of Jordan forms. Thus, people may draw a wrong conclusion from this procedure, if they simply apply floating-point computation to compute Jordan forms. In this paper, we first use an example to explain this problem, and then present a symbolic implementation of the decision procedure. Thus, the rounding error problem is therefore avoided. Moreover, we also show that the symbolic decision procedure is as efficient as the numerical one given in Tiwari (Proceedings of CAV’04. Lecture notes in computer science, vol 3114, pp 70–82, 2004). The complexity of former is max{O(n ⁶), O(n ^m+3)}, while that of the latter is O(n ^m+3), where n is the number of variables of the program and m is the number of its Boolean conditions. In addition, for the case when the characteristic polynomial of the assignment matrix is irreducible, we design a more efficient symbolic algorithm whose complexity is max(O(n ⁶), O(mn ³)).     

Polynomial algorithms for linear programming over the algebraic numbers

We derive a bound on the computational complexity of linear programs whose coefficients are real algebraic numbers. Key to this result is a notion of problem size that is analogous in function to the binary size of a rational-number problem. We also view the coefficients of a linear program as members of a finite algebraic extension of the rational numbers. The degree of this extension is an upper bound on the degree of any algebraic number that can occur during the course of the algorithm, and in this sense can be viewed as a supplementary measure of problem dimension. Working under an arithmetic model of computation, and making use of a tool for obtaining upper and lower bounds on polynomial functions of algebraic numbers, we derive an algorithm based on the ellipsoid method that runs in time bounded by a polynomial in the dimension, degree, and size of the linear program. Similar results hold under a rational number model of computation, given a suitable binary encoding of the problem input.This research was founded by the National Science Foundation under Grant DMS88-10192.     

The alternation hierarchy for sublogarithmic space is infinite

The alternation hierarchy for Turing machines with a space bound between loglog and log is infinite. That applies to all common concepts, especially a) to two-way machines with weak space-bounds, b) to two-way machines with strong space-bounds, and c) to one-way machines with weak space-bounds. In all of these cases the _k -and II _k -classes are not comparable fork>-2. Furthermore the _k -classes are not closed under intersection and the II _k -classes are not closed under union. Thus these classes are not closed under complementation. The hierarchy results also apply to classes determined by an alternation depth which is a function depending on the input rather than on a constant.     

A new version of the Improved Primal Simplex for degenerate linear programs

The Improved Primal Simplex (IPS) algorithm [Elhallaoui I, Metrane A, Desaulniers G, Soumis F. An Improved Primal Simplex algorithm for degenerate linear programs. SIAM Journal of Optimization, submitted for publication] is a dynamic constraint reduction method particularly effective on degenerate linear programs. It is able to achieve a reduction in CPU time of over a factor of three on some problems compared to the commercial implementation of the simplex method CPLEX. We present a number of further improvements and effective parameter choices for IPS. On certain types of degenerate problems, our improvements yield CPU times lower than those of CPLEX by a factor of 12.     

Novel linear programming approach for building a piecewise nonlinear binary classifier with a priori accuracy

This paper describes a novel approach to build a piecewise (non)linear surface that separates individuals from two classes with an a priori classification accuracy. In particular, total classification with a good generalization level can be obtained, provided no individual belongs to both classes. The method is iterative: at each iteration a new piece of the surface is found via the solution of a Linear Programming model. Theoretically, the larger the number of iterations, the better the classification accuracy in the training set; numerically, we also found that the generalization ability does not deteriorate on the cases tested. Nonetheless, we have included a procedure that computes a lower bound to the number of errors that will be generated in any given validation set. If needed, an early stopping criterion is provided. We also showed that each piece of the discriminating surface is equivalent to a neuron of a feed forward neural network (FFNN); so as a byproduct we are providing a novel training scheme for FFNNs that avoids the minimization of non convex functions which, in general, present many local minima.We compare this algorithm with a new linear SVM that needs no pre tuning and has an excellent performance on standard and synthetic data. Highly encouraging numerical results are reported on synthetic examples, on the Japanese Bank dataset, and on medium and small datasets from the Irvine repository of machine learning databases.     

On implementing a computation

To clarify the notion of computation and its role in cognitive science, we need an account of implementation, the nexus between abstract computations and physical systems. I provide such an account, based on the idea that a physical system implements a computation if the causal structure of the system mirrors the formal structure of the computation. The account is developed for the class of combinatorial-state automata, but is sufficiently general to cover all other discrete computational formalisms. The implementation relation is non-vacuous, so that criticisms by Searle and others fail. This account of computation can be extended to justify the foundational role of computation in artificial intelligence and cognitive science.     

On an optimal linear pattern classification procedure

The problem of Optimal Linear Discrimination for 3-class pattern recognition is attacked. The Linear Discrimination Rule, under those circumstances, is devised according to the following optimality criterion: maximize the probability of correct classification into any one of the three classes, while keeping the other two such probabilities fixed.An explicit solution of the problem is given, and a search technique is implemented greatly reducing the problem complexity.     

Determining the optimal decision delay parameter for a linear equalizer

1 Introduction Equalization techniques play an ever-increasing role in combating distortion and interference in mod- ern communication links[1,2]. It is well-known that the choice of equalizer decision delay parameter crit- ically determines achievable bit error rate (BER) performance[3,4]. We present a simple and e?ective method for determining an optimal decision delay pa- rameter that results in the best bit error rate perfor- mance for a linear equalizer. The proposed technique computes …     

求解整数线性规划问题的定界阻止算法的改进*

在现有求解整数线性规划问题的定界阻止算法的基础上提出了一种改进。该算法通过目标函数超平面截线性规划松弛问题的有效约束锥而形成一个单纯形;然后,引入一串平行片来切割该单纯形产生更低维的凸多面体;最后,在片上的这些凸多面体上执行阻止搜寻程序。由于单纯形和片上凸多面体的极顶点可以直接通过公式计算,且变量在片上凸多面体上的取值区间更窄,改进的定界阻止算法既方便又高效,这得到了一些经典算例和随机产生的算例的验证。     

Approximate linear programming for networks: Average cost bounds

This paper uses approximate linear programming (ALP) to compute average cost bounds for queueing network control problems. Like most approximate dynamic programming (ADP) methods, ALP approximates the differential cost by a linear form. New types of approximating functions are identified that offer more accuracy than previous ALP studies or other performance bound methods. The structure of the infinite constraint set is exploited to reduce it to a more manageable set. When needed, constraint sampling and truncation methods are also developed. Numerical experiments show that the LPs using quadratic approximating functions can be easily solved on examples with up to 17 buffers. Using additional functions reduced the error to 1–5% at the cost of larger LPs. These ALPs were solved for systems with up to 6–11 buffers, depending on the functions used. The method computes bounds much faster than value iteration. It also gives some insights into policies. The ALPs do not scale to very large problems, but they offer more accurate bounds than other methods and the simplicity of just solving an LP.     

Leveraging technological knowledge transfer by using fuzzy linear programming technique for multiattribute group decision making with fuzzy decision variables

Despite the importance of knowledge transfer for firms involved in foreign direct investment activities, this area has not received appropriate attention from the perspectives of both the knowledge transferor (i.e., MNC parent) and the knowledge recipient. To fill in the gap in the current literature we propose a model to understand the links between criteria complicating the transfer of knowledge and preferences that the company has to focus. This model is based on both the existing literature as well as views of company representatives and provides a useful methodology for identifying decision making problems on the transfer of knowledge. In this paper, we investigate the fuzzy linear programming technique (FLP) to analyze these links and for multiple attribute group decision making (MAGDM) problems with preference information on criteria. To reflect the decision maker’s subjective preference information and to determine the weight vector of attributes, the technique for order preference by similarity to ideal solution (TOPSIS) developed by Hwang and Yoon (1995) and the linear programming technique for multidimensional analysis of preference (LINMAP) developed by Sirinivasan and Shocker (Psychometrica 38:337–369, 1973) are used.     

On bilevel multi-follower decision making: General framework and solutions

Within the framework of any bilevel decision problem, a leader’s decision is influenced by the reaction of his or her follower. When multiple followers who may have had a share in decision variables, objectives and constraints are involved in a bilevel decision problem, the leader’s decision will be affected, not only by the reactions of these followers, but also by the relationships among these followers. This paper firstly identifies nine different kinds of relationships (S₁ to S₉) amongst followers by establishing a general framework for bilevel multi-follower decision problems. For each of the nine a corresponding bilevel multi-follower decision model is then developed. Also, this paper particularly proposes related theories focusing on an uncooperative decision problem (i.e., S₁ model), as this model is the most basic one for bilevel multi-follower decision problems over the nine kinds of relationships. Moreover, this paper extends the Kuhn-Tucker approach for driving an optimal solution from the uncooperative decision model. Finally, a real case study of a road network problem illustrates the application of the uncooperative bilevel decision model and the proposed extended Kuhn-Tucker approach.     

求解整数线性规划的一种高效隐数搜寻

提出了一种求解整数线性规划的新的隐数算法。首先,该算法引入了一组线性变换,将线性松弛问题的最优非基变量变换到一组新变量,使新变量有更小的取值范围。然后,在目标函数超平面上对非基变量和新变量进行隐数计算,从而大大提高了隐数搜寻的效率。     

A linear programming algorithm for invariant polyhedral sets of discrete-time linear systems

In this paper we formulate necessary and sufficient conditions for an arbitrary polyhedral set to be a positively invariant set of a linear discrete-time system. Polyhedral cones and linear subspaces are included in the analysis. A linear programming algorithm is presented that enables practical application of the results stated in this paper.     

On combined phase 1-phase 2 projective methods for linear programming

We compare the projective methods for linear programming due to de Ghellinck and Vial, Anstreicher, Todd, and Fraley. These algorithms have the feature that they approach feasibility and optimality simultaneously, rather than requiring an initial feasible point. We compare the directions used in these methods and the lower-bound updates employed. In many cases the directions coincide and two of the lower-bound updates give the same result. It appears that Todd's direction and Fraley's lower-bound update have slight advantages, and this is borne out in limited computational testing.This research was partially supported by NSF Grant DMS-8904406 and by ONR Contract N00004-87-K0212. The computations were carried out in the Cornell Computational Optimization Laboratory with support from NSF Grant DMS-8706133.     

A polynomial newton method for linear programming

An algorithm is presented for solving a set of linear equations on the nonnegative orthant. This problem can be made equivalent to the maximization of a simple concave function subject to a similar set of linear equations and bounds on the variables. A Newton method can then be used which enforces a uniform lower bound which increases geometrically with the number of iterations. The basic steps are a projection operation and a simple line search. It is shown that this procedure either proves in at mostO(n ² m ² L) operations that there is no solution or, else, computes an exact solution in at mostO(n ³ m ² L) operations.The linear programming problem is treated as a parametrized feasibility problem and solved in at mostO(n ³ m ² L) operations.     

A monotonic projective algorithm for fractional linear programming

We demonstrate that Karmarkar's projective algorithm is fundamentally an algorithm for fractional linear programming on the simplex. Convergence for the latter problem is established assuming only an initial lower bound on the optimal objective value. We also show that the algorithm can be easily modified so as to assure monotonicity of the true objective values, while retaining all global convergence properties. Finally, we show how the monotonic algorithm can be used to obtain an initial lower bound when none is otherwise available.