Sanciones pecuniarias en la contratación estatal

En el presente proyecto se realizaron cuatro capítulos que corresponden a unos objetivos específicos; en el primer capítulo, se abordó un estudio analítico del origen, definición, características y naturaleza de las multas contractuales y de las cláusulas penales en el derecho privado, el objetivo fijado era establecer un marco teórico que fundamente los capítulos siguientes. Posteriormente, en el segundo capítulo, se realizó una identificación y evaluación del tratamiento que la legislación ha dado a las multas contractuales y cláusulas penales. En el tercer capítulo, se analizó un caso práctico donde se evidencia la dificultad en la aplicación de estas figuras por la Administración Pública, resaltada por la escasa e imprecisa legislación al respecto. Finalmente, en el último capítulo, se enumeraron y desarrollaron la problemática e interrogantes detectados a lo largo del proyecto, con la idea de establecer soluciones a los vacíos o incongruencias encontradas.

Estudio sobre la operatividad y eficacia de las multas en el marco de la contratación estatal

El trabajo de grado se estructuró en tres capítulos. El primero constituye un marco teórico sobre el cual se fundamentan los siguientes, pues se realiza un análisis de la cláusula penal en el derecho privado, incluidas las prácticas comerciales internacionales. El segundo capítulo aborda el tema de las multas en la contratación estatal, para lo cual se estudió la normatividad y jurisprudencia pertinente; en esa parte del escrito se incluyó el apartado correspondiente al derecho comparado. Finalmente, en el tercer capítulo, se formula una propuesta que permite hacer de esta medida coercitiva provisional una herramienta idónea para lograr el correcto y oportuno cumplimiento del contrato.

A STUDY OF PENALTY FORMULATIONS USED IN THE NUMERICAL APPROXIMATION OF A RADIALLY SYMMETRIC ELASTICITY PROBLEM

We consider a class of two-dimensional problems in classical linear elasticity for which material overlapping occurs in the absence of singularities. Of course, material overlapping is not physically realistic, and one possible way to prevent it uses a constrained minimization theory. In this theory, a minimization problem consists of minimizing the total potential energy of a linear elastic body subject to the constraint that the deformation field must be locally invertible. Here, we use an interior and an exterior penalty formulation of the minimization problem together with both a standard finite element method and classical nonlinear programming techniques to compute the minimizers. We compare both formulations by solving a plane problem numerically in the context of the constrained minimization theory. The problem has a closed-form solution, which is used to validate the numerical results. This solution is regular everywhere, including the boundary. In particular, we show numerical results which indicate that, for a fixed finite element mesh, the sequences of numerical solutions obtained with both the interior and the exterior penalty formulations converge to the same limit function as the penalization is enforced. This limit function yields an approximate deformation field to the plane problem that is locally invertible at all points in the domain. As the mesh is refined, this field converges to the exact solution of the plane problem.

Subject/Object Asymmetry in the Comprehension of English Relative Clauses by Japanese Learners of English

While a number of studies have shown that object-extracted relative clauses are more difficult to understand than subject-extracted counterparts for second language (L2) English learners (e.g., Izumi, 2003), less is known about why this is the case and how they process these complex sentences. This exploratory study examines the potential applicability of Gibson's (1998, 2000) Syntactic Prediction Locality Theory (SPLT), a theory proposed to predict first language (L1) processing difficulty, to L2 processing and considers whether the theory might also account for the processing difficulties of subject- and object-extracted relative clauses encountered by L2 learners. Results of a self-paced reading time experiment from 15 Japanese learners of English are mainly consistent with the reading time profile predicted by the SPLT and thus suggest that the L1 processing theory might also be able to account for L2 processing difficulty.

Syntactic and pragmatic constraints on processing relative clauses

Thc oen itninteureasc ttioo nb eo fa pcreangtmraal tiiscs uaen di ns ymnotadcetlisc ocfo nsestnrtaeinnctse processing. It is well established that object relatives (1) are harder to process than subject relatives (2). Passivization, other things being equal, increases sentence complexity. However, one of the functions of the passive construction is to promote an NP into the role of subject so that it can be more easily bound to the head NP in a higher clause. Thus, (3) is predicted to be marginally preferred over (1). Passiviazation in this instance may be seen as a way of avoiding the object relative construction. 1. The pipe that the traveller smoked annoyed the passengers. 2. The traveller that smoked the pipe annoyed the passengers. 3.The pipe that was smoked by the traveller annoyed the 4.The traveller that the pipe was smoked by annoyed the 5.The traveller that the lady was assaulted by annoyed the In (4) we have relativization of an NP which has been demoted by passivization to the status of a by-phrase. Such relative clauses may only be obtained under quite restrictive pragmatic conditions. Many languages do not permit relativization of a constituent as low as a by-phrase on the NP accessibility hierarchy (Comrie, 1984). The factors which determine the acceptability of demoted NP relatives like (4-5) reflect the ease with which the NP promoted to subject position can be taken as a discourse topic. We explored the acceptability of sentences such as (1-5) using pair-wise judgements of samddifferent meaning, accompanied by ratings of easeof understanding. Results are discussed with reference to Gibsons DLT model of linguistic complexity and sentence processing (Gibson, 2000)

Learning the dynamics of embedded clauses

Recent work by Siegelmann has shown that the computational power of recurrent neural networks matches that of Turing Machines. One important implication is that complex language classes (infinite languages with embedded clauses) can be represented in neural networks. Proofs are based on a fractal encoding of states to simulate the memory and operations of stacks. In the present work, it is shown that similar stack-like dynamics can be learned in recurrent neural networks from simple sequence prediction tasks. Two main types of network solutions are found and described qualitatively as dynamical systems: damped oscillation and entangled spiraling around fixed points. The potential and limitations of each solution type are established in terms of generalization on two different context-free languages. Both solution types constitute novel stack implementations - generally in line with Siegelmann's theoretical work - which supply insights into how embedded structures of languages can be handled in analog hardware.

Solving MPCC problem with the hyperbolic penalty function

The main goal of this work is to solve mathematical program with complementarity constraints (MPCC) using nonlinear programming techniques (NLP). An hyperbolic penalty function is used to solve MPCC problems by including the complementarity constraints in the penalty term. This penalty function [1] is twice continuously differentiable and combines features of both exterior and interior penalty methods. A set of AMPL problems from MacMPEC [2] are tested and a comparative study is performed.

Solving a signalized traffic intersection problem with an hyperbolic penalty function

Mathematical Program with Complementarity Constraints (MPCC) finds many applications in fields such as engineering design, economic equilibrium and mathematical programming theory itself. A queueing system model resulting from a single signalized intersection regulated by pre-timed control in traffic network is considered. The model is formulated as an MPCC problem. A MATLAB implementation based on an hyperbolic penalty function is used to solve this practical problem, computing the total average waiting time of the vehicles in all queues and the green split allocation. The problem was codified in AMPL.

Classification of some penalty methods

Optimization problems arise in science, engineering, economy, etc. and we need to find the best solutions for each reality. The methods used to solve these problems depend on several factors, including the amount and type of accessible information, the available algorithms for solving them, and, obviously, the intrinsic characteristics of the problem. There are many kinds of optimization problems and, consequently, many kinds of methods to solve them. When the involved functions are nonlinear and their derivatives are not known or are very difficult to calculate, these methods are more rare. These kinds of functions are frequently called black box functions. To solve such problems without constraints (unconstrained optimization), we can use direct search methods. These methods do not require any derivatives or approximations of them. But when the problem has constraints (nonlinear programming problems) and, additionally, the constraint functions are black box functions, it is much more difficult to find the most appropriate method. Penalty methods can then be used. They transform the original problem into a sequence of other problems, derived from the initial, all without constraints. Then this sequence of problems (without constraints) can be solved using the methods available for unconstrained optimization. In this chapter, we present a classification of some of the existing penalty methods and describe some of their assumptions and limitations. These methods allow the solving of optimization problems with continuous, discrete, and mixing constraints, without requiring continuity, differentiability, or convexity. Thus, penalty methods can be used as the first step in the resolution of constrained problems, by means of methods that typically are used by unconstrained problems. We also discuss a new class of penalty methods for nonlinear optimization, which adjust the penalty parameter dynamically.

Penalty fuzzy function for derivative-free optimization

Penalty and Barrier methods are normally used to solve Nonlinear Optimization Problems constrained problems. The problems appear in areas such as engineering and are often characterised by the fact that involved functions (objective and constraints) are non-smooth and/or their derivatives are not know. This means that optimization methods based on derivatives cannot net used. A Java based API was implemented, including only derivative-free optimizationmethods, to solve both constrained and unconstrained problems, which includes Penalty and Barriers methods. In this work a new penalty function, based on Fuzzy Logic, is presented. This function imposes a progressive penalization to solutions that violate the constraints. This means that the function imposes a low penalization when the violation of the constraints is low and a heavy penalisation when the violation is high. The value of the penalization is not known in beforehand, it is the outcome of a fuzzy inference engine. Numerical results comparing the proposed function with two of the classic penalty/barrier functions are presented. Regarding the presented results one can conclude that the prosed penalty function besides being very robust also exhibits a very good performance.

Direct-search penalty/barrier methods

In Nonlinear Optimization Penalty and Barrier Methods are normally used to solve Constrained Problems. There are several Penalty/Barrier Methods and they are used in several areas from Engineering to Economy, through Biology, Chemistry, Physics among others. In these areas it often appears Optimization Problems in which the involved functions (objective and constraints) are non-smooth and/or their derivatives are not know. In this work some Penalty/Barrier functions are tested and compared, using in the internal process, Derivative-free, namely Direct Search, methods. This work is a part of a bigger project involving the development of an Application Programming Interface, that implements several Optimization Methods, to be used in applications that need to solve constrained and/or unconstrained Nonlinear Optimization Problems. Besides the use of it in applied mathematics research it is also to be used in engineering software packages.

Adaptive Penalty and Barrier function based on Fuzzy Logic

Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twenty-eight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.