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Social marketing is an effective approach in promoting physical activity among adults when a substantial number of benchmarks are used and when managers understand the audience, make the desired behavior tangible, and promote the desired behavior persuasively.

The paper presents a review of the basic concepts of the Logical Analysis of Data (LAD), along with a series of discrete optimization models associated to the implementation of various components of its general methodology, as well as an outline of applications of LAD to medical problems. The combinatorial optimization models described in the paper represent variations on the general theme of set covering, including some with nonlinear objective functions. The medical applications described include the development of diagnostic and prognostic systems in cancer research and pulmonology, risk assessment among cardiac patients, and the design of biomaterials.

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Introduction The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors.

Logical analysis of data (LAD) is a special data analysis methodology which combines ideas and concepts from optimization, combinatorics, and Boolean functions. The central concept in LAD is that of patterns, or rules, which were found to play a critical role in classification, ranked regression, clustering, detection of subclasses, feature selection and other problems. The research area of LAD was defined and initiated by Peter L. Hammer, who was the catalyst of the LAD oriented research for decades, and whose consistent vision and efforts helped the methodology to move from theory to data analysis applications, to achieve maturity and to be successful in many medical, industrial and economics case studies. This overview presents some of the basic aspects of LAD, from the definition of the main concepts to the efficient algorithms for pattern generation, and from the complexity

a b s t r a c tThe oil field development is a hard and critical task that defines the main procedures to be performed during the oil field productive life. Given the complexity of this planning phase, methods to support decision making have been developed to assist in the proper application of high investments. This paper aims to report a 0-1 Linear Programming Model which minimizes the development costs of a given oil field as a whole. The model seeks to define: the number, location and capacities of production platforms; number and positions of wells; points where manifolds must be installed; interconnection between platforms, manifolds and wells; and which sections of each well should be vertical or horizontal. The model was named Multicapacitated Platforms and Wells Location Problem (MPWLP). Two different scenarios were tested and the results were consistent with reality, computationally feasible and presented innovations compared to models found in literature.

We introduce an optimization approach for the construction of large margin rule‐based classifiers. We base our description on the Logical Analysis of Data (LAD) methodology, but the same approach can be applied to different rule‐based classification algorithms. The novelty in our algorithm relies on the fact that it unifies the distinct tasks of pattern (rule) generation and creation of a so‐called discriminant function for classification. Moreover, the algorithm has a single parameter, thus, significantly reducing the necessity for parameter calibration when learning a new data set. We investigate how accurate the LAD classification models built with our algorithm are and how they compare to other machine learning models.