New and improved results for packing identical unitary radius circles within triangles, rectangles and strips

The focus of study in this paper is the class of packing problems. More specifically, it deals with the placement of a set of N circular items of unitary radius inside an object with the aim of minimizing its dimensions. Differently shaped containers are considered, namely circles, squares, rectangles, strips and triangles. By means of the resolution of non-linear equations systems through the Newton-Raphson method, the herein presented algorithm succeeds in improving the accuracy of previous results attained by continuous optimization approaches up to numerical machine precision. The computer implementation and the data sets are available at http://www.ime.usp.br/similar to egbirgin/packing/. (C) 2009 Elsevier Ltd, All rights reserved.

Approximating a class of combinatorial problems with rational objective function

In the late seventies, Megiddo proposed a way to use an algorithm for the problem of minimizing a linear function a(0) + a(1)x(1) + ... + a(n)x(n) subject to certain constraints to solve the problem of minimizing a rational function of the form (a(0) + a(1)x(1) + ... + a(n)x(n))/(b(0) + b(1)x(1) + ... + b(n)x(n)) subject to the same set of constraints, assuming that the denominator is always positive. Using a rather strong assumption, Hashizume et al. extended Megiddo`s result to include approximation algorithms. Their assumption essentially asks for the existence of good approximation algorithms for optimization problems with possibly negative coefficients in the (linear) objective function, which is rather unusual for most combinatorial problems. In this paper, we present an alternative extension of Megiddo`s result for approximations that avoids this issue and applies to a large class of optimization problems. Specifically, we show that, if there is an alpha-approximation for the problem of minimizing a nonnegative linear function subject to constraints satisfying a certain increasing property then there is an alpha-approximation (1 1/alpha-approximation) for the problem of minimizing (maximizing) a nonnegative rational function subject to the same constraints. Our framework applies to covering problems and network design problems, among others.

Formalization of an architectural model for exception handling coordination based on CA action concepts

Architectures based on Coordinated Atomic action (CA action) concepts have been used to build concurrent fault-tolerant systems. This conceptual model combines concurrent exception handling with action nesting to provide a general mechanism for both enclosing interactions among system components and coordinating forward error recovery measures. This article presents an architectural model to guide the formal specification of concurrent fault-tolerant systems. This architecture provides built-in Communicating Sequential Processes (CSPs) and predefined channels to coordinate exception handling of the user-defined components. Hence some safety properties concerning action scoping and concurrent exception handling can be proved by using the FDR (Failure Divergence Refinement) verification tool. As a result, a formal and general architecture supporting software fault tolerance is ready to be used and proved as users define components with normal and exceptional behaviors. (C) 2010 Elsevier B.V. All rights reserved.

Efficient solutions to factored MDPs with imprecise transition probabilities

When modeling real-world decision-theoretic planning problems in the Markov Decision Process (MDP) framework, it is often impossible to obtain a completely accurate estimate of transition probabilities. For example, natural uncertainty arises in the transition specification due to elicitation of MOP transition models from an expert or estimation from data, or non-stationary transition distributions arising from insufficient state knowledge. In the interest of obtaining the most robust policy under transition uncertainty, the Markov Decision Process with Imprecise Transition Probabilities (MDP-IPs) has been introduced to model such scenarios. Unfortunately, while various solution algorithms exist for MDP-IPs, they often require external calls to optimization routines and thus can be extremely time-consuming in practice. To address this deficiency, we introduce the factored MDP-IP and propose efficient dynamic programming methods to exploit its structure. Noting that the key computational bottleneck in the solution of factored MDP-IPs is the need to repeatedly solve nonlinear constrained optimization problems, we show how to target approximation techniques to drastically reduce the computational overhead of the nonlinear solver while producing bounded, approximately optimal solutions. Our results show up to two orders of magnitude speedup in comparison to traditional ""flat"" dynamic programming approaches and up to an order of magnitude speedup over the extension of factored MDP approximate value iteration techniques to MDP-IPs while producing the lowest error of any approximation algorithm evaluated. (C) 2011 Elsevier B.V. All rights reserved.

Algorithms for Maximum Independent Set in Convex Bipartite Graphs

A bipartite graph G = (V, W, E) is convex if there exists an ordering of the vertices of W such that, for each v. V, the neighbors of v are consecutive in W. We describe both a sequential and a BSP/CGM algorithm to find a maximum independent set in a convex bipartite graph. The sequential algorithm improves over the running time of the previously known algorithm and the BSP/CGM algorithm is a parallel version of the sequential one. The complexity of the algorithms does not depend on |W|.

Minimizing the object dimensions in circle and sphere packing problems

Given a fixed set of identical or different-sized circular items, the problem we deal with consists on finding the smallest object within which the items can be packed. Circular, triangular, squared, rectangular and also strip objects are considered. Moreover, 2D and 3D problems are treated. Twice-differentiable models for all these problems are presented. A strategy to reduce the complexity of evaluating the models is employed and, as a consequence, instances with a large number of items can be considered. Numerical experiments show the flexibility and reliability of the new unified approach. (C) 2007 Elsevier Ltd. All rights reserved.

Newton`s iterates can converge to non-stationary points

In this note we discuss the convergence of Newton`s method for minimization. We present examples in which the Newton iterates satisfy the Wolfe conditions and the Hessian is positive definite at each step and yet the iterates converge to a non-stationary point. These examples answer a question posed by Fletcher in his 1987 book Practical methods of optimization.

Improving ultimate convergence of an augmented Lagrangian method

Optimization methods that employ the classical Powell-Hestenes-Rockafellar augmented Lagrangian are useful tools for solving nonlinear programming problems. Their reputation decreased in the last 10 years due to the comparative success of interior-point Newtonian algorithms, which are asymptotically faster. In this research, a combination of both approaches is evaluated. The idea is to produce a competitive method, being more robust and efficient than its `pure` counterparts for critical problems. Moreover, an additional hybrid algorithm is defined, in which the interior-point method is replaced by the Newtonian resolution of a Karush-Kuhn-Tucker (KKT) system identified by the augmented Lagrangian algorithm. The software used in this work is freely available through the Tango Project web page:http://www.ime.usp.br/similar to egbirgin/tango/.

Augmented Lagrangian methods under the constant positive linear dependence constraint qualification

Two Augmented Lagrangian algorithms for solving KKT systems are introduced. The algorithms differ in the way in which penalty parameters are updated. Possibly infeasible accumulation points are characterized. It is proved that feasible limit points that satisfy the Constant Positive Linear Dependence constraint qualification are KKT solutions. Boundedness of the penalty parameters is proved under suitable assumptions. Numerical experiments are presented.

Handling Equality in Monodic Temporal Resolution

First-order temporal logic is a concise and powerful notation, with many potential applications in both Computer Science and Artificial Intelligence. While the full logic is highly complex, recent work on monodic first-order temporal logics has identified important enumerable and even decidable fragments including the guarded fragment with equality. In this paper, we specialise the monodic resolution method to the guarded monodic fragment with equality and first-order temporal logic over expanding domains. We introduce novel resolution calculi that can be applied to formulae in the normal form associated with the clausal resolution method, and state correctness and completeness results.

The Requirements of Using Provenance in e-Science Experiments

In e-Science experiments, it is vital to record the experimental process for later use such as in interpreting results, verifying that the correct process took place or tracing where data came from. The process that led to some data is called the provenance of that data, and a provenance architecture is the software architecture for a system that will provide the necessary functionality to record, store and use process documentation. However, there has been little principled analysis of what is actually required of a provenance architecture, so it is impossible to determine the functionality they would ideally support. In this paper, we present use cases for a provenance architecture from current experiments in biology, chemistry, physics and computer science, and analyse the use cases to determine the technical requirements of a generic, technology and application-independent architecture. We propose an architecture that meets these requirements and evaluate a preliminary implementation by attempting to realise two of the use cases.

Maude Object-Oriented Action Tool

MAIDL, André Murbach; CARVILHE, Claudio; MUSICANTE, Martin A. Maude Object-Oriented Action Tool. Electronic Notes in Theoretical Computer Science. [S.l:s.n], 2008.

POPT: uma abordagem de ensino de programação orientada a problema e testes

There is a growing interest of the Computer Science education community for including testing concepts on introductory programming courses. Aiming at contributing to this issue, we introduce POPT, a Problem-Oriented Programming and Testing approach for Introductory Programming Courses. POPT main goal is to improve the traditional method of teaching introductory programming that concentrates mainly on implementation and neglects testing. POPT extends POP (Problem Oriented Programing) methodology proposed on the PhD Thesis of Andrea Mendonça (UFCG). In both methodologies POPT and POP, students skills in dealing with ill-defined problems must be developed since the first programming courses. In POPT however, students are stimulated to clarify ill-defined problem specifications, guided by de definition of test cases (in a table-like manner). This paper presents POPT, and TestBoot a tool developed to support the methodology. In order to evaluate the approach a case study and a controlled experiment (which adopted the Latin Square design) were performed. In an Introductory Programming course of Computer Science and Software Engineering Graduation Programs at the Federal University of Rio Grande do Norte, Brazil. The study results have shown that, when compared to a Blind Testing approach, POPT stimulates the implementation of programs of better external quality the first program version submitted by POPT students passed in twice the number of test cases (professor-defined ones) when compared to non-POPT students. Moreover, POPT students submitted fewer program versions and spent more time to submit the first version to the automatic evaluation system, which lead us to think that POPT students are stimulated to think better about the solution they are implementing. The controlled experiment confirmed the influence of the proposed methodology on the quality of the code developed by POPT students

Industrial Robotics Applied to Education

This work describes a ludic proposal for programming learning of industrial robots to be developed by groups of engineering students. Two projects are presented: Tic-tac-toe Opponent Robot and Environmentalist Robot. The first project use competitive search techniques of the Artificial Intelligence, computational vision, electronic and pneumatic concepts for ability decision making for a robotic agent on the tic-tae-toe game. The second project consists of a game that contains a questions and answers database about environmental themes. An algorithm selects the group of questions to be answered by the player, analyses the answers and sends the result to a industrial robot through serial port. According with the player performance, the robot makes congratulation movements and giving a gift to the winner player. Otherwise, the robot makes movements, disapproving the player performance.