Constraint reasoning for differential models

The basic motivation of this work was the integration of biophysical models within the interval constraints framework for decision support. Comparing the major features of biophysical models with the expressive power of the existing interval constraints framework, it was clear that the most important inadequacy was related with the representation of differential equations. System dynamics is often modelled through differential equations but there was no way of expressing a differential equation as a constraint and integrate it within the constraints framework. Consequently, the goal of this work is focussed on the integration of ordinary differential equations within the interval constraints framework, which for this purpose is extended with the new formalism of Constraint Satisfaction Differential Problems. Such framework allows the specification of ordinary differential equations, together with related information, by means of constraints, and provides efficient propagation techniques for pruning the domains of their variables. This enabled the integration of all such information in a single constraint whose variables may subsequently be used in other constraints of the model. The specific method used for pruning its variable domains can then be combined with the pruning methods associated with the other constraints in an overall propagation algorithm for reducing the bounds of all model variables. The application of the constraint propagation algorithm for pruning the variable domains, that is, the enforcement of local-consistency, turned out to be insufficient to support decision in practical problems that include differential equations. The domain pruning achieved is not, in general, sufficient to allow safe decisions and the main reason derives from the non-linearity of the differential equations. Consequently, a complementary goal of this work proposes a new strong consistency criterion, Global Hull-consistency, particularly suited to decision support with differential models, by presenting an adequate trade-of between domain pruning and computational effort. Several alternative algorithms are proposed for enforcing Global Hull-consistency and, due to their complexity, an effort was made to provide implementations able to supply any-time pruning results. Since the consistency criterion is dependent on the existence of canonical solutions, it is proposed a local search approach that can be integrated with constraint propagation in continuous domains and, in particular, with the enforcing algorithms for anticipating the finding of canonical solutions. The last goal of this work is the validation of the approach as an important contribution for the integration of biophysical models within decision support. Consequently, a prototype application that integrated all the proposed extensions to the interval constraints framework is developed and used for solving problems in different biophysical domains.

Modern techniques for constraint solving the CASPER experience

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Constraint-based modeling of minimum set covering: application to species differentation

Work presented in the context of the European Master in Computational Logics, as partial requisit for the graduation as Master in Computational Logics

Constraint-based verification of imperative programs

work presented in the context of the European Master’s program in Computational Logic, as the partial requirement for obtaining Master of Science degree in Computational Logic

Probabilistic constraint reasoning

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Informática, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Constraint-based modelling of mixed microbial populations: Application to polyhydroxyalkanoates production

Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica

Constraint reasoning with local search for continuous optimization

Optimization is a very important field for getting the best possible value for the optimization function. Continuous optimization is optimization over real intervals. There are many global and local search techniques. Global search techniques try to get the global optima of the optimization problem. However, local search techniques are used more since they try to find a local minimal solution within an area of the search space. In Continuous Constraint Satisfaction Problems (CCSP)s, constraints are viewed as relations between variables, and the computations are supported by interval analysis. The continuous constraint programming framework provides branch-and-prune algorithms for covering sets of solutions for the constraints with sets of interval boxes which are the Cartesian product of intervals. These algorithms begin with an initial crude cover of the feasible space (the Cartesian product of the initial variable domains) which is recursively refined by interleaving pruning and branching steps until a stopping criterion is satisfied. In this work, we try to find a convenient way to use the advantages in CCSP branchand- prune with local search of global optimization applied locally over each pruned branch of the CCSP. We apply local search techniques of continuous optimization over the pruned boxes outputted by the CCSP techniques. We mainly use steepest descent technique with different characteristics such as penalty calculation and step length. We implement two main different local search algorithms. We use “Procure”, which is a constraint reasoning and global optimization framework, to implement our techniques, then we produce and introduce our results over a set of benchmarks.

Probabilistic constraint reasoning with Monte Carlo integration to robot localization

This work studies the combination of safe and probabilistic reasoning through the hybridization of Monte Carlo integration techniques with continuous constraint programming. In continuous constraint programming there are variables ranging over continuous domains (represented as intervals) together with constraints over them (relations between variables) and the goal is to find values for those variables that satisfy all the constraints (consistent scenarios). Constraint programming “branch-and-prune” algorithms produce safe enclosures of all consistent scenarios. Special proposed algorithms for probabilistic constraint reasoning compute the probability of sets of consistent scenarios which imply the calculation of an integral over these sets (quadrature). In this work we propose to extend the “branch-and-prune” algorithms with Monte Carlo integration techniques to compute such probabilities. This approach can be useful in robotics for localization problems. Traditional approaches are based on probabilistic techniques that search the most likely scenario, which may not satisfy the model constraints. We show how to apply our approach in order to cope with this problem and provide functionality in real time.

In silico constraint-based strain optimization methods: the quest for optimal cell factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

Kinematic constraint equations

This chapter presents a general methodology for the formulation of the kinematic constraint equations at position, velocity and acceleration levels. Also a brief characterization of the different type of constraints is offered, namely the holonomic and nonholonomic constraints. The kinematic constraints described here are formulated using generalized coordinates. The chapter ends with a general approach to deal with the kinematic analysis of multibody systems.

Origen y evolución del magma granítico en el Batolito de Achala: Fundamentos petrológicos, geoquímicos y estructurales para la preconcentración uranífera en el área de Cañada del Puerto. Origin and evolution of the granitic magma in the Achala Batholith: petrological, geochemical and structural constraint for the uranium preconcentration in the Cañada del Puerto area.

El batolito de Achala es uno de los macizos graníticos más grandes de las Sierras Pampeanas, el cual se localiza en las Sierras Grandes de Córdoba. Si bien el batolito de Achala ha sido objeto de diversos estudios geológicos, principalmente debido a sus yacimientos de uranio, el mismo todavía no posee un inequívoco modelo petrogéntico. Tampoco existe, en la actualidad, un inequívoco modelo que explique la preconcentración de uranio en las rocas graníticas portadores de este elemento. Este Proyecto tiene como objetivo general realizar estudios petrológicos y geoquímicos en la región conocida como CAÑADA del PUERTO, un lugar estratégicamente definido debido a la abundancia de granitos equigranulares de grano fino y/o grano medio biotíticos, emplazados durante el desarrollo de cizallas magmáticas tardías, y que constituirían las rocas fuentes de uranio. El objetivo específico requiere estudios detallados de las diferentes facies del batolito de Achala en el área seleccionada, incluyendo investigaciones petrológicas, geoquímicas de roca total, geoquímica de isótopos radiactivos y química mineral, con el fin de definir un MODELO PETROGENÉTICO que permita explicar: (a) el origen del magma padre y el subsiguiente proceso de cristalización de las diferentes facies graníticas aflorantes en el área de estudio, (b) identificar el proceso principal que condujo a la PRECONCENTRACIÓN uranífera de los magmas graníticos canalizados en las cizallas magmáticas tardías. Ambos objetivos se complementan y no son compartimentos estancos, ya que el logro combinado de estos objetivos permitirá comprender de mejor manera el proceso geoquímico que gobernó la distribución y concentración del U. De esta manera, se intentará definir un MODELO de PRECONCENTRACIÓN URANÍFERA EXTRAPOLABLE a otras áreas graníticas enriquecidas en uranio, constituyendo una poderosa herramienta de investigación aplicada a la exploración uranífera. En particular, el conocimiento de los recursos uraníferos es parte de una estrategia nacional con vistas a triplicar antes del 2025 la disponibilidad energética actual, en cuyo caso, el uranio constituye la materia prima de las centrales nucleares que se están planificando y en construcción. Por otro lado, la Argentina adhirió al Protocolo de Kioto y, junto a los países adherentes, deben disminuir de manera progresiva el uso de combustibles fósiles (que producen gases de efecto invernadero), reemplazándola por otras fuentes de energía, entre ellas, la ENERGÍA NUCLEAR. Este Proyecto, si bien NO es un Proyecto de exploración y/o prospección minera, es totalmente consistente con la política energética nacional promocionada desde el Ministerio de Planificación Federal, Inversión Pública y Servicios (v. sitio WEB CNEA), que ha invertido, desde 2006, importantes sumas de dinero, en el marco del Programa de Reactivación de la Actividad Nuclear.Los estudios referidos serán conducidos por los Drs. Dahlquist (CONICET-UNC) y Zarco (CNEA) quienes integrarán sus experiencias desarrolladas en el campo de las Ciencias Básicas con aquel logrado en el campo de las Ciencias Aplicadas, respectivamente. Se pretende, por tanto, aplicar conocimientos académicos-científicos a un problema de geología con potencial significado económico-energético, vinculando las instituciones referidas, esto es, CONICET-UNC y CNEA, con el fin de contribuir a la actividad socioeconómica de la provincia de Córdoba en particular y de Argentina en general.Finalmente, convencidos de que el progreso de la Ciencia y el Desarrollo Tecnológico está íntimamente vinculada con la sólida Formación de Recursos Humanos se pretende que este Proyecto contribuya SIGNIFICATIVAMENTE a las investigaciones de Doctorado que iniciará la Geóloga Carina Bello, actual Becaria de la CNEA.

Convergence and morphological constraint in frogs : variation in postcranial morphology ; a contribution in celebration of the distinguished scholarship of Robert F. Inger on the occasion of his sixty-fifth birthday / Sharon B. Emerson.

n.s. no.43(1988)

Double implementation in a market for indivisible goods with a price constraint

I consider the problem of assigning agents to objects where each agent must pay the price of the object he gets and prices must sum to a given number. The objective is to select an assignment-price pair that is envy-free with respect to the true preferences. I prove that the proposed mechanism will implement both in Nash and strong Nash the set of envy-free allocations. The distinguishing feature of the mechanism is that it treats the announced preferences as the true ones and selects an envy-free allocation with respect to the announced preferences.

Multilayer perceptron with local constraint as an emerging method in spatial data analysis

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

Misallocation of capital in a model of endogenous financial intermediation and insurance

In this paper we analyze productivity and welfare losses from capital misallocation in a general equilibrium model of occupational choice and endogenous financial intermediation. We study the effects of borrowing and lending, insurance, and risk sharing on the optimal allocation of resources. We find that financial markets together with general equilibrium effects have large impact on entrepreneurs' entry and firm-size decisions. Efficiency gains are increasing in the quality of financial markets, particularly in their ability to alleviate a financing constraint by providing insurance against idiosyncratic risk.