Algorithms And Data Structures

A branch-and-bound algorithm for discrete multi-factor by Niu Shu-fen, Wang Guo-xin, Sun Xiao-ling PDF

By Niu Shu-fen, Wang Guo-xin, Sun Xiao-ling

During this paper, a brand new branch-and-bound set of rules in accordance with the Lagrangian twin rest and non-stop leisure is proposed for discrete multi-factor portfolio choice version with roundlot limit in monetary optimization. This discrete portfolio version is of integer quadratic programming difficulties. The separable constitution of the version is investigated by utilizing Lagrangian rest and twin seek. Computational effects convey that the set of rules is able to fixing real-world portfolio issues of information from US inventory marketplace and randomly generated try out issues of as much as one hundred twenty securities.

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Since each node may have different maximum net inputs, given some global temperature, the probability distribution varies from node to node. As a result, the search is performed with varying levels of noise across the nodes. This is a source of inefficiency, since it is likely that some nodes are operating with too much noise. In summary, the outlined paradigm, although reasonable in practice, can lead to anomalies in energy computation. First, it is difficult to predict the energy of a solution.

This suggests that, in the worst case, GAs should not have to search more than the cube root of the search 26 space in order to find a solution and, in general, should do much better. One of the unexpected benefits of the experimental results presented here is substantial empirical evidence of just such speedups on SAT problems. Clearly, on the TP and FP problems the GA is performing better than the theoretical lower bound. This section on GAs has outlined current GA research and possible future directions.

AVEˆ2 AVEˆ3 20 40 60 80 # Variables = log(Search Space) Figure 3: Performance of GAs using AVE p Figure 4 summarizes the performance of the GAs on the two families of SAT problems using AVE 2 in the payoff function. As noted earlier, the log-log curves appear to be sub-linear. To get a better feeling for the form of these curves, both linear and quadratic curve fits were attempted. For both of the families of SAT problems, a quadratic form produces a better fit and, by using the coefficients of the quadratic form, the observed speedup can be calculated.

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A branch-and-bound algorithm for discrete multi-factor portfolio optimization model by Niu Shu-fen, Wang Guo-xin, Sun Xiao-ling

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