Heuristic Option Research Articles

Improved computation of polynomial roots over number fields when using complex embeddings

We explore a fairly generic method to compute roots of polynomials over number fields through complex embeddings. Our main contribution is to show how to use a structure of a relative extension to decode in a subfield. Additionally we describe several heuristic options to improve practical efficiency. We provide experimental data from our implementation and compare our methods to the state of the art algorithm implemented in Pari/Gp.

Theory and Algorithms for Shapelet-Based Multiple-Instance Learning.

We propose a new formulation of multiple-instance learning (MIL), in which a unit of data consists of a set of instances called a bag. The goal is to find a good classifier of bags based on the similarity with a "shapelet" (or pattern), where the similarity of a bag with a shapelet is the maximum similarity of instances in the bag. In previous work, some of the training instances have been chosen as shapelets with no theoretical justification. In our formulation, we use all possible, and thus infinitely many, shapelets, resulting in a richer class of classifiers. We show that the formulation is tractable, that is, it can be reduced through linear programming boosting (LPBoost) to difference of convex (DC) programs of finite (actually polynomial) size. Our theoretical result also gives justification to the heuristics of some previous work. The time complexity of the proposed algorithm highly depends on the size of the set of all instances in the training sample. To apply to the data containing a large number of instances, we also propose a heuristic option of the algorithm without the loss of the theoretical guarantee. Our empirical study demonstrates that our algorithm uniformly works for shapelet learning tasks on time-series classification and various MIL tasks with comparable accuracy to the existing methods. Moreover, we show that the proposed heuristics allow us to achieve the result in reasonable computational time.

Reliability Evaluation for a Stochastic Flow Network Based on Upper and Lower Boundary Vectors

For stochastic flow network (SFN), given all the lower (or upper) boundary points, the classic problem is to calculate the probability that the capacity vectors are greater than or equal to the lower boundary points (less than or equal to the upper boundary points). However, in some practical cases, SFN reliability would be evaluated between the lower and upper boundary points at the same time. The evaluation of SFN reliability with upper and lower boundary points at the same time is the focus of this paper. Because of intricate relationships among upper and lower boundary points, a decomposition approach is developed to obtain several simplified subsets. SFN reliability is calculated according to these subsets by means of the inclusion-exclusion principle. Two heuristic options are then established in order to calculate SFN reliability in an efficient direction based on the lower and upper boundary points.

Parasitism – Predation algorithm (PPA): A novel approach for feature selection

Maximizing the classification accuracy and minimizing the number of selected features are the two main incompatible objectives for using feature selection to overcome the curse of dimensionality. “Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy.” This work presents a new meta-heuristic optimization approach, called Parasitism-Predation Algorithm (PPA), which mimics the interaction between the predator (cats), the parasite (cuckoos) and the host (crows) in the crow–cuckoo–cat system model to overcome the problems of low convergence and the curse of dimensionality of large data. The proposed hybrid framework combines the relative advantages of cat swarm optimization (CSO), cuckoo search (CS) and crow search algorithm (CSA) to attain a combinatorial set of features to boost up the classification accuracy. Nesting, parasitism, and predation phases are supposed to help exploration ability and balance in the context of solving classification problems. In addition, Levy flight distribution is applied to help better diversity of conventional CSA and improve ability of exploration. Meanwhile, an effective fitness function is utilized to enable the proposed PPA-based feature selector using K-Nearest Neighbors algorithm (KNN) to attain a combinatorial set of features. The proposed PPA and four standard heuristic search algorithms are looked at to gauge how efficient the proposed option is. Additionally, eighteen classification datasets are deployed to gauges its efficacy. The results highlight that the algorithm proposed is both effective and competitive in terms of performance of classification and dimensionality reduction as opposed to other heuristic options.

Helmholtz’s Vortex Motion: An Embodied View of Mathematics in the Heuristics of Fluid Mechanics

Some viewpoints on the foundations of mathematics and its philosophy are more connected to scientific practice and its heuristics, mainly with the construction of physical theories and the search for the best explanations of physical phenomena by means of abduction or the solution of problems by the analytical method. Some researchers have introduced the importance of human cultural activities into the cognitive aspects of the mental processes of scientists, proposing an embodied approach in the bridge between mathematics and reality. Fluid mechanics is an interesting area in this sense due to its position if the network of mathematics. By means of an historical example on vortex motion by Helmholtz, we show that the intuitive idea of eddy (or vortex) contains cognitive properties of a mental schema and that it gives many heuristic options (via cooperation with other heuristic instruments like extreme thinking, thought experiment and analogy) for an embodied mathematical explanation about vortex dynamics.

Una visión filogenética de <i>Frankliniella</i> (Tbysanoptera: Tbripidae)

The pbylogeny of the species groups of Franldiniella and the position of Exophtalmothirps and Bolbothrips are discussed with reference to recent literature and analysis with the cladistic software PAUP 3.0 in its heuristic option, the matrix has 24 taxa and 25 cbaracteristics for each; 1) the intonsa group is nOl a well defined taxon and may include groups of species, but there are no good morphological traits fot its recognition, 2) the pulchella and cephaUca groups are consistent monophyletic groups of species, 3) the minuta group is rnonophyletic with a few roorphological constraints, 4) Exophtalmothrips and Bolbothíips seem to he segregated, and notsynonymous of Franlcliniella.

Steps towards a high-frequency financial decision support system to pricing options on currency futures with neural networks

In this paper, we present steps towards a model-driven financial decision support system (FDSS) to pricing options on currency futures, which can be embedded in a high-frequency trading process. Due to the difficulty of option valuation, we provide an alternative heuristic option pricing approach with neural networks. We show that the use of neural networks is not only suitable in generating accurate trading signals, but also in generating automated fast run-time trading signals for the decision taker. To achieve this, we conduct an experiment with an empirical tick data set of EUR/USD options on currency futures of four weeks. An essential advantage of our approach is the simultaneous pricing across different strike prices and parsimonious use of input variables. Nevertheless, we also have to take particular limitations into account, which give us useful hints for further research and steps.

Possible Collision Avoidance with Off-line Route Selection

The paper describes the traffic flow problems in telecommunication networks based on the Internet protocol. The main aim of telecommunication network operator today is to offer an SLA (Service Level Agreement) contract to end users, with provided QoS (Quality of Service) for different classes of services. In order to achieve this, it is necessary to establish the routes between marginal network nodes meeting the network traffic requirements and optimizing the network performances free of simultaneous flows conflicts. In DiffServ/MPLS (Multi-Protocol Label Switching) networks traffic flows traverse the network simultaneously and there may come to collision of concurrent flows. They are distributed among LSPs (Labeled Switching Paths) related to service classes. In LSP creation the IGP (Interior Gateway Protocol) uses simple on-line routing algorithms based on the shortest path methodology. In highly loaded networks this becomes an insufficient technique. In this suggested approach LSP need not necessarily be the shortest path solution. It can be pre-computed much earlier, possibly during the SLA negotiation process. In that sense an effective algorithm for collision control is developed. It may find a longer but lightly loaded path, taking care of the collision possibility. It could be a very good solution for collision avoidance and for better load-balancing purpose where links are running close to capacity. The algorithm can be significantly improved through heuristic approach. Heuristic options are compared in test-examples and their application for collision control is explained. KEYWORDS: Telecommunication networks, collision avoidance, multi-constraint route selection, self-organizing systems, MPLS, QoS

Load Control for Overloaded MPLS/DiffServ Networks during SLA Negotiation

In end-to-end QoS provisioning some bandwidth portions on the link may be reserved for certain traffic classes (and for particular set of users) so the congestion problem of concurrent flows (traversing the network simultaneously) can appear. It means that in overloaded and poorly connected MPLS/DS networks the CR (Constraint-based Routing) becomes insufficient technique. If traffic engineering is supported with ap-propriate traffic load control the congestion possibility can be predicted before the utilization of guaranteed service. In that sense the initial (proactive) routing can be pre-computed much earlier, possible during SLA (Service Level Agreement) negotiation. In the paper a load simulation technique for load balancing control purpose is proposed. It could be a very good solution for congestion avoidance and for better load-balancing purpose where links are running close to capacity. To be acceptable for real application such complicated load control technique needs very effective algorithm. Proposed algorithm was tested on the network with maximum M core routers on the path and detail results are given for N=3 service classes. Further improve-ment through heuristic approach is made and results are discussed. Some heuristic options show significant complexity savings that is appropriate for load control in huge networks.

A Heuristic Approach to Satellite Link Capacity Planning Applied in Mobile Networks

The efficient heuristic algorithm for sizing of N satellite capacitytypes (on LES side of/ink) in mobile networks during exploitationis being developed, minimizing the total expansioncost. Using the network flow approach and the extreme flowthe01y many expansion solutions (sub-problems) are eliminatedfrom further calculation because they consist at least ofone flow that cannot be a part of optimal expansion sequence.At first, the heuristic algorithm is compared with algorithmbased on exact approach. In all numerical test-examples thebest possible result is achieved. After that we developed andtested some algorithm options, using various limitations for capacitystate values. It is obvious that all heuristic options areve1y effective and for some cases they are capable to find thebest possible result but with significant savings. It means thatour heuristic approach can be successfully applied to short-term or medium-term satellite network planning with finitenumber of discrete time periods. Only if we apply adequate heuristicsolution can we ensure both significant improvement ofQoS (Quality of Setvice) and minimal capacity expansion cost.

Heuristic option pricing with neural networks and the neuro-computer synapse 3

Today’s option and warrant pricing is based on models developed by Black, Scholes and Merton in 1973 and Cox, Ross and Rubinstein in 1979. The price movement of the underlying asset is modeled by continuous-time or discrete-time stochastic processes. Unfortunately these models are based on severely unrealistic assumptions. Permanently an unsatisfactory and quite artificial adaption to the true market conditions is necessary (future volatility of the underlying price). Here, an alternative heuristic approach with a highly accurate neural network approximation is presented. Market prices of options and warrants and the values of the influence variables form the usually very large output/ input data set. Thousands of multi-layer perceptrons with various topologies and with different weight initializations are trained with a fast sequential quadratic programming (SQP) method. The best networks are combined to an expert council network to synthesize market prices accurately. All options and warrants can be compared to single out overpriced and underpriced ones for each trading day. For each option and warrant overpriced and underpriced trading days can be used to ascertain a better buy and sell timing. Furthermore the neural model gains deep insight into the market price sen-sitivities (option Greeks), e.g., ▵, Г, Θ and Ω. As an illustrative example we inves-tigate BASF stock call warrants. Time series from the beginning of 1996 to mid 1997 of 74 BASF call warrant prices at the Frankfurter Wertpapierborse (Frankfurt Stock Exchange) form the data basis. Finally a possible speed up of the training with the neuro-computer SYNAPSE 3 is briefly discussed

TIMING STRATEGIES IN THE CALL OPTION MARKET*

THIS STUDY investigates the problem faced by the risk averse call option-holder of when to exercise the option contract. An optimal exercising strategy model is developed using the recursive maximization techniques of dynamic programming. The model involves six parameters-the length and exercise price of the option contract, the mean and standard deviation of the lognormal distribution of stock price changes, and the utility function and alternative investment opportunity rate of the investor. A simulation of the model is performed, using randomly generated stock prices, to test the effect of alternative parameter values on the return and utility of return of the optimal exercising strategy. The results indicate that the return and utility are highly sensitive to all of the above mentioned parameters except the investment opportunity rate. These results imply that, if the model is to be used in practice, then methods must be devised for accurately measuring the unknown values of some of the parameters of the model. Otherwise, heuristic option timing strategies must be devised. Using a random sample of option contract offerings, the return and utility of return of the model are compared with the return and utility of several alternative timing strategies, namely (a) exercising the option on the expiration date (buy-and-hold policy), (b) exercising the option at the most profitable price (perfect information), and (c) exercising the option whenever an exercise signal is indicated by a trading rule known as the filter technique. For the sample of options studied, the average utility of the model is not significantly different from that of the buy-and-hold rule and the filter technique. Subject to further testing of the dynamic programming model, these results suggest that the option-holder can do as well using one of the simpler timing strategies such as the buy-and-hold policy in timing the execution of option contracts. Also, given these results, the model builder may want to concentrate his efforts in the future on the development of heuristic timing rules rather than on optimization techniques. Although the result is not statistically significant, the buyand-hold rule yields a higher average return than the dynamic programming model and the filter technique. This finding is shown to be consistent with the random walk theory of price changes. Another finding is that all of the strategies tested yield returns well below the average return that could be obtained with perfect information. This suggests that there are sizeable gains to be made if improved timing strategies can be devised. Suggestions for further research include the incorporation of the returns from other assets (portfolio effects) and the wealth position of the investor in the utility function, finding techniques for accurately measuring the parameters of the model, possibly blending optimization techniques and heuristic rules in the development of timing strategies, and solving the problem of finding an optimal option selection strategy given an optimal exercising strategy.

Role of automatic, conscious, and unconscious thought processes in complex decision making

A complex decision is any decision which includes choosing among options with numerous describing attributes. Certain decisions are fast, often guided with automatic processes of thought, while other decisions are made much slower with careful examination of all the factors. These processes can have a significant impact on the quality of decision making. The aim of this research was to investigate the effect of automatic, conscious and unconscious thought processes in the context of decision making. Participants were psychology students aged between 19 to 28 years. First experiment investigated the role of three different thought processes on choosing a subjectively best option, as well as TTB heuristic option. The second experiment investigated metacognitive aspects of decision making, precisely, to determine the differences in feeling of rightness (FOR) as well as the tendency to change the decision, depending on the activated thought processes. Different thought processes determined the choice of the subjectively best option. In the conscious thought condition, participants chose the subjectively best option more often than in the automatic or unconscious thought condition. However, there was no difference between conditions in choosing the TTB heuristic option. The feeling of rightness was significantly higher in conscious thought condition than in automatic or unconscious thought condition, but the two latter conditions did not differ in the judgment of feeling of rightness nor did they differ in the tendency to change the decision.