| 1. |
- Mischenko, Kateryna, et al.
(author)
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Assessing a multiple QTL search using the variance component model
- 2010
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In: Computational biology and chemistry (Print). - : Elseiver. - 1476-9271 .- 1476-928X. ; 34:1, s. 34-41
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Journal article (peer-reviewed)abstract
- Development of variance component algorithms in genetics has previously mainly focused on animal breeding models or problems in human genetics with a simple data structure. We study alternative methods for constrained likelihood maximization in quantitative trait loci (QTL) analysis for large complex pedigrees. We apply a forward selection scheme to include several QTL and interaction effects, as well as polygenic effects, with up to five variance components in the model. We show that the implemented active set and primal-dual schemes result in accurate solutions and that they are robust. In terms of computational speed, a comparison of two approaches for approximating the Hessian of the log-likelihood shows that the method using an average information matrix is the method of choice for the five-dimensional problem. The active set method, with the average information method for Hessian computation, exhibits the fastest convergence with an average of 20 iterations per tested position, where the change in variance components <0.0001 was used as convergence criterion.
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| 2. |
- Mishchenko, Kateryna, et al.
(author)
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Adapted Downhill Simplex Method for Pricing Convertible Bonds
- 2007
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In: Theory of Stochastic Processes. - 0321-3900. ; 13:4, s. 130-147
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Journal article (peer-reviewed)abstract
- The paper is devoted to modeling optimal exercise strategies of thebehavior of investors and issuers working with convertible bonds.This implies solution of the problems of stock price modeling, payoffcomputation and minimax optimization.Stock prices (underlying asset) were modeled under the assumptionof the geometric Brownian motion of their values. The Monte Carlomethod was used for calculating the real payoff which is the objectivefunction. The minimax optimization problem was solved using thederivative-free Downhill Simplex method.The performed numerical experiments allowed to formulate recommendationsfor the choice of appropriate size of the initial simplex inthe Downhill Simplex Method, the number of generated trajectoriesof underlying asset, the size of the problem and initial trajectories ofthe behavior of investors and issuers.
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| 3. |
- Ljungberg, Kajsa, et al.
(author)
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Efficient Algorithms for Multi-Dimensional Global Optimization in Genetic Mapping of Complex Traits
- 2010
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In: Advances and Applications in Bioinformatics and Chemistry. - 1178-6949. ; 3:1, s. 75-88
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Journal article (peer-reviewed)abstract
- We present a two-phase strategy for optimizing a multidimensional, nonconvex function arising during genetic mapping of quantitative traits. Such traits are believed to be affected by multiple so called quantitative trait loci (QTL), and searching for d QTL results in a d-dimensional optimization problem with a large number of local optima. We combine the global algorithm DIRECT with a number of local optimization methods that accelerate the final convergence, and adapt the algorithms to problem-specific features. We also improve the evaluation of the QTL mapping objective function to enable exploitation of the smoothness properties of the optimization landscape. Our best two-phase method is demonstrated to be accurate in at least six dimensions and up to ten times faster than currently used QTL mapping algorithms.
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| 4. |
- Ljungberg, Kajsa, et al.
(author)
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Efficient algorithms for multi-dimensional global optimization in genetic mapping of complex traits
- 2005
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Reports (other academic/artistic)abstract
- We present a two-phase strategy for optimizing a multi-dimensional, non-convex function arising during genetic mapping of quantitative traits. Such traits are believed to be affected by multiple so called QTL, and searching for d QTL results in a d-dimensional optimization problem with a large number of local optima. We combine the global algorithm DIRECT of Jones et al. with a number of local optimization methods that accelerate the final convergence, and adapt the algorithms to problem-specific features. We also improve the evaluation of the QTL mapping objective function to enable exploitation of the smoothness properties of the optimization landscape. Our best two-phase method is demonstrated to be accurate in at least six dimensions and up to ten times faster than currently used QTL mapping algorithms.
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| 6. |
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| 7. |
- Mishchenko, Kateryna, et al.
(author)
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Hyperparameters Optimization for Federated Learning System : Speech Emotion Recognition Case Study
- 2023
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In: 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC). - : IEEE. ; , s. 80-86
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Conference paper (peer-reviewed)abstract
- Context: Federated Learning (FL) has emerged as a promising, massively distributed way to train a joint deep model across numerous edge devices, ensuring user data privacy by retaining it on the device. In FL, Hyperparameters (HP) significantly affect the training overhead regarding computation and transmission time, computation and transmission load, as well as model accuracy. This paper presents a novel approach where Hyperparameters Optimization (HPO) is used to optimize the performance of the FL model for Speech Emotion Recognition (SER) application. To solve this problem, both Single-Objective Optimization (SOO) and Multi-Objective Optimization (MOO) models are developed and evaluated. The optimization model includes two objectives: accuracy and total execution time. Numerical results show that optimal Hyperparameters (HP) settings allow for improving both the accuracy of the model and its computation time. The proposed method assists FL system designers in finding optimal parameters setup, allowing them to carry out model design and development efficiently depending on their goals.
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| 8. |
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| 9. |
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| 10. |
- Mishchenko, Kateryna, et al.
(author)
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Newton-type Methods for REML Estimation in Genetic Analysis of Quantitative Traits
- 2008
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In: Journal of Computational Methods in Sciences and Engineering. - 1472-7978. ; 8:1, s. 53-67
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Journal article (peer-reviewed)abstract
- Robust and efficient optimization methods for variance component estimation using Restricted Maximum Likelihood (REML) models for geneticmapping of quantitative traits are considered. We show that the standard Newton-AI scheme may fail when the optimum is located at one of the constraint boundaries, and we introduce different approaches to remedy this by taking the constraints into account. We approximate the Hessian of the objective function using the average information matrix and also by using an inverse BFGS formula. The robustness and efficiency is evaluated for problems derived from two experimental data from the same animal populations.
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