Informatica

Portfolio optimization is to find the stock portfolio minimizing the risk for a required return or maximizing the return for a given risk level. The seminal work in this field is the m ean-variance model formulated as a quadratic programming problem. Since it is not computationally practical to solve the original model directly, a number of alternative models have been proposed.

In this study, the performance of the modified subgradient algorithm (MSG) to solve the 0–1 quadratic knapsack problem (QKP) was examined. The MSG was proposed by Gasimov for solving dual problems constructed with respect to sharp Augmented Lagrangian function. The MSG has some important proven properties. For example, it is convergent, and it guarantees zero duality gap for the problems such that its objective and constraint functions are all Lipschtz. Additionally, the MSG has been successfully used for solving non-convex continuous and some combinatorial problems with equality constraints since it was first proposed. In this study, the MSG was used to solve the QKP which has an inequality constraint. The first step in solving the problem was converting zero-one nonlinear QKP problem into continuous nonlinear problem by adding only one constraint and not adding any new variables. Second, in order to solve the continuous QKP, dual problem with "zero duality gap" was constructed by using the sharp Augmented Lagrangian function. Finally, the MSG was used to solve the dual problem, by considering the equality constraint in the computation of the norm. To compare the performance of the MSG with some other methods, some test instances from the relevant literature were solved both by using the MSG and by using three different MINLP solvers of GAMS software. The results obtained were presented and discussed.

The paper deals with the use of dynamic programming for word endpoint detection in isolated word recognition. Endpoint detection is based on likelihood maximization. Expectation maximization approach is used to deal with the problem of unknown parameters. Speech signal and background noise energy is used as features for making decision. Performance of the proposed approach was evaluated using isolated Lithuanian words speech corpus.

This paper deals with maximum likelihood and least square segmentation of autoregressive random sequences with abruptly changing parameters. Conditional distribution of the observations has been derived. Objective function was modified to the form suitable to apply dynamic programming method for its optimization. Expressions of Bellman functions for this case were obtained. Performance of presented approach is illustrated with simulation examples and segmentation of speech signals examples.

The dynamic programming method for estimation of many change-points in univariate autoregressive (AR) sequences with known AR parameters between change-points is investigated. A problem how to use this method for long autoregressive sequences is solved and a constructive solution is given. A simulation experiment illustrates the advantages of the solution obtained.