This paper deals with generalized pattern search (GPS) algorithms for linearly constrained optimization. At each iteration, the GPS algorithm generates a set of directions that conforms to the geometry of any nearby linear constraints. This set is then used to construct trial points to be evaluated during the iteration. In previous work, Lewis and Torczon developed a scheme for computing the co...

Abstract. We present two new classes of pattern search algorithms for unconstrained minimization: the rank ordered and the positive basis pattern search methods. These algorithms can nearly halve the worst case cost of an iteration compared to the classical pattern search algorithms. The rank ordered pattern search methods are based on a heuristic for approximating the direction of steepest des...

We propose a new method of extracting texts related to a given keyword from Web pages collected by a search engine. By combining structural pattern matching and text classification, texts related to a given keyword such as reputations of a given restaurant can be extracted automatically from Web pages in unfixed sites, which is impossible by conventional wrappers. According to our cross validat...

This paper presents a pattern search algorithm and its hybridization with a random descent search for solving bound constrained minimax problems. The herein proposed heuristic pattern search method combines the Hooke and Jeeves (HJ) pattern and exploratory moves with a randomly generated approximate descent direction. Two versions of the heuristic algorithm have been applied to several benchmar...

Nearest neighbour search is one of the most simple and used technique in Pattern Recognition. In this paper we are interested on tree based algorithms that only make use of the metric properties of the space. One of the most known and refereed method in this class was proposed by Fukunaga and Narendra in the 70’s. This algorithm uses a tree that is traversed on search time and uses some elimina...

Consumer information search has been a focus of research nowadays, especially in the context of online business environments. One of the research questions is to determine how much information to search (i.e., when to stop searching), since extensive literature on behavior science has revealed that consumers often search either “too little” or “too much”, even with the help of existing interact...

With ever-increasing number of patterns in the literature and online repositories, it can be hard for non-experts to know about new patterns and select patterns appropriate to their needs. We argue that a systematic way for searching patterns is required and we present the Patterns 2.0 service, a composite software service for facilitating pattern search and selection. The service combines seve...

Pattern Search Algorithms for Mixed Variable General Constrained Optimization Problems

The N-tuple method [4] is a statistical pattern recognition method, which decomposes a given pattern into several sets of n points, termed “N tuples”. The input connection mapping of the N-tuple classifier determines the sampling and defines the locations of the pattern matrix. Realizing the fact that the classification performance of the N-tuple classifier is highly dependant on the actual sub...

Rare itemsets are an important sort of patterns that have a wide range of practical applications. Although mining rare patterns poses specific algorithmic problems, it is yet insufficiently studied. In a previous work, we proposed a levelwise approach for rare itemset mining. Here, we examine the benefits of depth-first methods for that task as such methods are known to outperform the levelwise...