On Some Optimization Problems on Dynamic Networks

The basic assumption of re-optimization consists in the need of eiciently managing huge quantities of data in order to reduce the waste of resources, both in terms of space and time. Re-optimization refers to a series of computational strategies through which new problem instances are tackled analyzing similar, previously solved, problems, reusing existing useful information stored in memory from past computations. Its natural collocation is in the context of dynamic problems, with these latter accounting for a large share of the themes of interest in the multifaceted scenario of combinatorial optimization, with notable regard to recent applications. Dynamic frameworks are topic of research in classical and new problems spanning from routing, scheduling, shortest paths, graph drawing and many others. Concerning our speciic theme of investigation, we focused on the dynamical characteristics of two problems deined on networks: re-optimization of shortest paths and incremental graph drawing. For the former, we proposed a novel exact algorithm based on an auction approach, while for the latter, we introduced a new constrained formulation, Constrained Incremental Graph Drawing, and several meta-heuristics based prevalently on Tabu Search and GRASP frameworks. Moreover, a parallel branch of our research focused on the design of new GRASP algorithms as eicient solution strategies to address further optimization problems. Speciically, in this research thread, will be presented several GRASP approaches devised to tackle intractable problems such as: the Maximum-Cut Clique, p-Center, and Minimum Cost Satisiability

Incremental Grid-like Layout Using Soft and Hard Constraints

We explore various techniques to incorporate grid-like layout conventions into a force-directed, constraint-based graph layout framework. In doing so we are able to provide high-quality layout---with predominantly axis-aligned edges---that is more flexible than previous grid-like layout methods and which can capture layout conventions in notations such as SBGN (Systems Biology Graphical Notation). Furthermore, the layout is easily able to respect user-defined constraints and adapt to interaction in online systems and diagram editors such as Dunnart.Comment: Accepted to Graph Drawing 201

Logic learning and optimized drawing: two hard combinatorial problems

Nowadays, information extraction from large datasets is a recurring operation in countless fields of applications. The purpose leading this thesis is to ideally follow the data flow along its journey, describing some hard combinatorial problems that arise from two key processes, one consecutive to the other: information extraction and representation. The approaches here considered will focus mainly on metaheuristic algorithms, to address the need for fast and effective optimization methods. The problems studied include data extraction instances, as Supervised Learning in Logic Domains and the Max Cut-Clique Problem, as well as two different Graph Drawing Problems. Moreover, stemming from these main topics, other additional themes will be discussed, namely two different approaches to handle Information Variability in Combinatorial Optimization Problems (COPs), and Topology Optimization of lightweight concrete structures

Drawing Graphs within Restricted Area

We study the problem of selecting a maximum-weight subgraph of a given graph such that the subgraph can be drawn within a prescribed drawing area subject to given non-uniform vertex sizes. We develop and analyze heuristics both for the general (undirected) case and for the use case of (directed) calculation graphs which are used to analyze the typical mistakes that high school students make when transforming mathematical expressions in the process of calculating, for example, sums of fractions

A practical approach to goal modelling for time-constrained projects

Goal modelling is a well known rigorous method for analysing problem rationale and developing requirements. Under the pressures typical of time-constrained projects its benefits are not accessible. This is because of the effort and time needed to create the graph and because reading the results can be difficult owing to the effects of crosscutting concerns. Here we introduce an adaptation of KAOS to meet the needs of rapid turn around and clarity. The main aim is to help the stakeholders gain an insight into the larger issues that might be overlooked if they make a premature start into implementation. The method emphasises the use of obstacles, accepts under-refined goals and has new methods for managing crosscutting concerns and strategic decision making. It is expected to be of value to agile as well as traditional processes

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Unveiling The Tree: A Convex Framework for Sparse Problems

This paper presents a general framework for generating greedy algorithms for solving convex constraint satisfaction problems for sparse solutions by mapping the satisfaction problem into one of graph traversal on a rooted tree of unknown topology. For every pre-walk of the tree an initial set of generally dense feasible solutions is processed in such a way that the sparsity of each solution increases with each generation unveiled. The specific computation performed at any particular child node is shown to correspond to an embedding of a polytope into the polytope received from that nodes parent. Several issues related to pre-walk order selection, computational complexity and tractability, and the use of heuristic and/or side information is discussed. An example of a single-path, depth-first algorithm on a tree with randomized vertex reduction and a run-time path selection algorithm is presented in the context of sparse lowpass filter design

On Class Diagrams, Crossings and Metrics

As a standardized software engineering diagram, the UML class diagram provides various information on the static structure of views on software while design, implementation and maintenance phase. This talk gives an overview on drawing UML class diagrams in hierarchical fashion. Therefore, common elements of class diagrams are introduced and aesthetic rules for drawing UML class diagrams are given. These rules are based on four disciplines involved in the reading process of diagrams. After a brief introduction to our drawing algorithm, an extensive extension of the well-known Sugiyama algorithm, two details are highlighted: A new crossing reduction algorithm is presented and compared to existing ones and issues on measuring the quality of a layout are discussed