Generalized green operator of Noetherian boundary-value problem for matrix differential equation

We find necessary and sufficient conditions for solvability and the construction of the generalized Green operator for Noetherian linear boundary-value problem for a linear matrix differential equation. We propose an operator, which leads a linear matrix algebraic equation to the traditional linear algebraic system with a rectangular matrix. We use pseudoinverseMoore–Penrose matrices and orthogonal projections for solving a linear algebraic system.     

Maximality of linear continuous logic

The linear compactness theorem is a variant of the compactness theorem holding for linear formulas. We show that the linear fragment of continuous logic is maximal with respect to the linear compactness theorem and the linear elementary chain property. We also characterize linear formulas as those preserved by the ultramean construction.     

Solvability of Linear Matrix Boundary-Value Problem

We find solvability conditions and give a construction of generalized Green operator for a linear matrix boundary-value problem. We suggest an operator which reduces a linear matrix equation to a standard linearNoetherian boundary-value problem. To solve a linearmatrix systemwe use an operatorwhich reduces a linear matrix equation to a linear algebraic equation with rectangular matrix.     

Some families of -cyclic codes

We introduce and solve several problems on -cyclic codes.We study the link between -linear cyclic codes and -cyclic codes (not necessarily linear) obtained by using two binary linear cyclic codes. We use these results to present a family of -self-dual linear cyclic codes.     

Linear programming approaches for multicategory support vector machines

We consider linear programming approaches for support vector machines (SVM). The linear programming problems are introduced as an approximation of the quadratic programming problems commonly used in SVM. When we consider the kernel based nonlinear discriminators, the approximation can be viewed as kernel principle component analysis which generates an important subspace from the feature space characterized the kernel function. We show that any data points nonlinearly, and implicitly, projected into the feature space by kernel functions can be approximately expressed as points lying a low dimensional Euclidean space explicitly, which enables us to develop linear programming formulations for nonlinear discriminators. We also introduce linear programming formulations for multicategory classification problems. We show that the same maximal margin principle exploited in SVM can be involved into the linear programming formulations. Moreover, considering the low dimensional feature subspace extraction, we can generate nonlinear multicategory discriminators by solving linear programming problems.Numerical experiments on real world datasets are presented. We show that the fairly low dimensional feature subspace can achieve a reasonable accuracy, and that the linear programming formulations calculate discriminators efficiently. We also discuss a sampling strategy which might be crucial for huge datasets.     

Cosine of angle and center of mass of an operator

We consider the notion of real center of mass and total center of mass of a bounded linear operator relative to another bounded linear operator and explore their relation with cosine and total cosine of a bounded linear operator acting on a complex Hilbert space. We give another proof of the Min-max equality and then generalize it using the notion of orthogonality of bounded linear operators. We also illustrate with examples an alternative method of calculating the antieigenvalues and total antieigenvalues for finite dimensional operators.     

Control synthesis for a three-dimensional nilpotent system

We consider the control problem for a system described by ordinary differential equations with linear controls. We present sufficient conditions for finding an exact solution of the control problem for a three-dimensional nilpotent system with a two-dimensional linear control in the form of programmed controls and feedback controls. We consider two examples of the computation of controls with the use of linear vector fields on the plane.     

Exact relaxations for parametric robust linear optimization problems

We first show that the closedness of the characteristic cone of the constraint system of a parametric robust linear optimization problem is a necessary and sufficient condition for each robust linear program with the finite optimal value to admit exact semidefinite linear programming relaxations. We then provide the weakest regularity condition that guarantees exact second-order cone programming relaxations for parametric robust linear programs.     

An exact penalty on bilevel programs with linear vector optimization lower level

We are interested in a class of linear bilevel programs where the upper level is a linear scalar optimization problem and the lower level is a linear multi-objective optimization problem. We approach this problem via an exact penalty method. Then, we propose an algorithm illustrated by numerical examples.     

Complexity of Linear Problems with a Fixed Output Basis

We use an information-based complexity approach to study the complexity of approximation of linear operators. We assume that the values of a linear operator may be elements of an infinite dimensional space G. It seems reasonable to look for an approximation as a linear combination of some elements g_i from the space G and compute only the coefficients c_i of this linear combination. We study the case when the elements g_i are fixed and compare it with the case where the g_i can be chosen arbitrarily. We show examples of linear problems where a significant output data compression is possible by the use of a nonlinear algorithm, and this nonlinear algorithm is much better than all linear algorithms. We also provide an example of a linear problem for which one piece of information is enough whereas an optimal (minimal cost) algorithm must use information of much higher cardinality.     

Saddle representations of positively homogeneous functions by linear functions

We say that a positively homogeneous function admits a saddle representation by linear functions iff it admits both an inf-sup-representation and a sup-inf-representation with the same two-index family of linear functions. In the paper we show that each continuous positively homogeneous function can be associated with a two-index family of linear functions which provides its saddle representation. We also establish characteristic properties of those two-index families of linear functions which provides saddle representations of functions belonging to the subspace of Lipschitz continuous positively homogeneous functions as well as the subspaces of difference sublinear and piecewise linear functions.     

Aglianò-Montagna type decomposition of linear pseudo hoops and its applications

We decompose every linear pseudo hoop as an Aglianò-Montagna type of ordinal sum of linear Wajsberg pseudo hoops which are either negative cones of linear ?-groups or intervals in linear unital ?-groups with strong unit. We apply the decomposition to present a new proof that every linear pseudo BL-algebra and consequently every representable pseudo BL-algebra is good. Moreover, we show that every maximal filter and every value of a linear pseudo hoop is normal, and every σ-complete linear pseudo hoop is commutative.     

Optimal Point Placement for Mesh Smoothing

We study the problem of moving a vertex in an unstructured mesh of triangular, quadrilateral, or tetrahedral elements to optimize the shapes of adjacent elements. We show that many such problems can be solved in linear time using generalized linear programming. We also give efficient algorithms for some mesh smooth ing problems that do not fit into the generalized linear programming paradigm.     

On possibility of obtaining linear accuracy evaluation of approximate solutions to inverse problems

We consider a concept of linear a priori estimate of the accuracy for approximate solutions to inverse problems with perturbed data. We establish that if the linear estimate is valid for a method of solving the inverse problem, then the inverse problem is well-posed according to Tikhonov. We also find conditions, which ensure the converse for the method of solving the inverse problem independent on the error levels of data. This method is well-known method of quasi-solutions by V. K. Ivanov. It provides for well-posed (according to Tikhonov) inverse problems the existence of linear estimates. If the error levels of data are known, a method of solving well-posed according to Tikhonov inverse problems is proposed. This method called the residual method on the correctness set (RMCS) ensures linear estimates for approximate solutions. We give an algorithm for finding linear estimates in the RMCS.     

A compromise solution for the multiobjective stochastic linear programming under partial uncertainty

This paper solves the multiobjective stochastic linear program with partially known probability. We address the case where the probability distribution is defined by crisp inequalities. We propose a chance constrained approach and a compromise programming approach to transform the multiobjective stochastic linear program with linear partial information on probability distribution into its equivalent uniobjective problem. The resulting program is then solved using the modified L-shaped method. We illustrate our results by an example.     

Multistep Methods for Conservative Problems

We discuss the use of linear multistep methods for the solution of conservative (in particular Hamiltonian) problems. Despite the lack of good results concerning their behaviour, linear multistep methods have been satisfactorily used by researchers in applicative fields. We try to elucidate the requirements that a linear multistep method should fulfill in order to be suitable for the integration of such problems, relating our analysis to their use as boundary value methods. We collect a number of results obtained for the linear autonomous case and also consider some numerical tests on the Kepler problem.Work supported by GNCS and MIUR.     

Solving Bilevel Linear Programs Using Multiple Objective Linear Programming

We present an algorithm for solving bilevel linear programs that uses simplex pivots on an expanded tableau. The algorithm uses the relationship between multiple objective linear programs and bilevel linear programs along with results for minimizing a linear objective over the efficient set for a multiple objective problem. Results in multiple objective programming needed are presented. We report computational experience demonstrating that this approach is more effective than a standard branch-and-bound algorithm when the number of leader variables is small.     

Periodic schedules for linear precedence constraints

We consider the computation of periodic cyclic schedules for linear precedence constraints graphs: a linear precedence constraint is defined between two tasks and induces an infinite set of usual precedence constraints between their executions such that the difference of iterations is a linear function. The objective function is the minimization of the maximal period of a task.We recall first that this problem may be modelled using linear programming. A polynomial algorithm is then developed to solve it for a particular class of linear precedence graphs called unitary graphs. We also show that a periodic schedule may not exist for unitary graphs. In the general case, a decomposition of the linear precedence graph into unitary components is computed and we assume that a periodic schedule exists for each of these components. Lower bounds on the periods are exhibited and we show that an optimal periodic schedule may not achieve them. The notion of quasi-periodic schedule is then introduced and we prove that this new class of schedules always reaches these bounds.