Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of Multi-Objective Genetic Algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated Genetic Algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary Algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN's initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective.

Darbo pradžioje supažindinsiu su gamybinių tvarkaraščių sudarymo uždaviniu. Jo tikslas rasti tvarkaraštį tam tikrai gamybinei situacijai. Uždavinys turi pilnai nusakyti kas ir kur turi įvykti ir apibrėžti visus apribojimus, o gautas sprendinys turi tenkinti šiuos reikalavimus ir vienareikšmiškai nusakyti operacijų vykdymą. Ši problema aktuali gamyklose, personalo valdyme, krovinių gabenime, oro uostuose, traukinių stotyse ir daugelyje kitų. Kadangi matematinis gamybinių tvarkaraščių sudarymo apibūdinimas sudaromas atsižvelgiant į realaus pasaulio problemas, egzistuoja daug šio uždavinio variantų. Dėl to teko pasirinkti kurį konkretų uždavinį nagrinėti. Pirmajame skyriuje supažindinu su šiuo konkrečiu uždaviniu, pateikiu apibrėžimus, s��vokas ir egzistuojančias problemas sprendžiant gamybinių tvarkaraščių uždavinį. Galiausiai parodysiu, kad tai yra sunkiai sprendžiamas ir dėl to vienas iš aktualių kombinatorinio optimizavimo uždavinių. Tuomet plačiau apibūdinu genetinį ir skruzdžių kolonijos optimizavimo algoritmus. Šie algoritmai ir naudojami sprendžiant mano konkret�� gamybinių tvarkaraščių sudarymo uždavinį. Aš apibūdinu visus parametrus ir koeficientus. Vėliau aš pristatau sukurtą programinę įrangą, skirtą rasti spręsti gamybinių tvarkaraščių sudarymo uždavinį ir vaizdžiai pateikti gautus sprendinius. Taip pat grafikų lentelių ir kitų priemonių pagalba pateikiu atliktų eksperimentų rezultatus. Tuomet apžvelgiu gautus rezultatus ir aptariu pastebėtas tendencijas ir... [toliau žr. visą tekstą] / At the begining of this work introduction to the scheduling problem and its basics is given. The goal of a scheduling problem is to make a schedule for a certain production situation. In the problem it is stated what must take place, and the solution describes exactly what should happen at what time. These problems occur in factories, personnel planning, transportation, airfields, railroad stations etcetera. Since mathematical descriptions of scheduling problems are often distilled from practical situations there are many variants of scheduling problems. A selection had to be made which problem is going to be a target of the study. The job shop scheduling problem was chosen. In the first chapter there is definitions of the problems we are trying to solve, introduction of important concepts (properties, bounds, definitions) from the field of scheduling. The last section takes a small detour into theoretical computer science in order to make precise that scheduling problems are hard to solve. In the second chapter introduction of genetic and ant colony optomization algorithms and its basius is given. It is used to solve scheduling problem, which was mentioned before. Introduction of all genetic and ant colony optimization algorithm operators and settings are given here. Then follows the introduction to software witch was made to solve and visualize solutions of scheduling problem. A great number of plots and figures are used in the experimental chapter to explain what... [to full text]

Darbo tikslas – sukurti bendrą prieigos tinklo modeliavimo metodiką bei jos programinę realizaciją, atitinkančią šiuos reikalavimus: • n užduotų prieigos tinklo parametrų reikšmių optimalus nustatymas pagal m užduotų prieigos tinklo kokybės apribojimų, kai n ≥ 1, o m ≥ 0; • optimalus stočių koordinačių nustatymas mobiliojo telefono ryšio tinklui; • optimalus stočių koordinačių nustatymas laidinio telefono ryšio tinklui; Darbo pradžioje apžvelgiamos telekomunikacijų sektoriaus užduotys, kurios gali būti sprendžiamos kombinatorinio optimizavimo metodais. Taipogi pristatomi ir suklasifikuojami galimi šių užduočių sprendimo metodai. Tiriamojoje darbo dalyje pristatomas daugiaparametrinis prieigos tinklo optimizavimo algoritmas integruotas su stočių išdėstymo algoritmais. Stočių išdėstymui pateikiami du meta-euristiniai algoritmai: • Skruzdžių kolonijos algoritmas, papildytas lokalios paieškos procedūra; • Genetinis algoritmas, papildytas lokalios paieškos procedūra. Minėtų algoritmų realizacijos skirstomos pagal šias prieigos tinklo ryšio topologijas: • Mobiliojo telefono ryšio tinklui; • Fiksuoto telefono ryšio tinklui. Esminiai darbe pasiekti rezultatai: • Sukurta universali metodika, leidžianti kurti realius prieigos tinklo modelius; • Sukurta šios metodikos programinė realizacija. Darbe nagrinėjamų uždavinių ir algoritmų pagrindu buvo paskelbti ir pristatyti šie straipsniai: • „Prieigos tinklo parametrų optimalaus... [toliau žr. visą tekstą] / The objective of this work is creation of telecommunication network approach algorithm and its realization. The created algorithm must fulfill following requirements: • optimal values evaluation of n given network approach parameters with m given network approach quality constrains, where n ≥ 1, o m ≥ 0; • optimal solution for transmitters placement problem in mobile phone network; • optimal solution for transmitters placement problem in fixed phone network; In the beginning of this paper we present a set of telecommunication segment problems which can be solved using combinatorial optimization methods. Also we present a set of combinatorial optimization methods which can be used for solving these problems. Finally we present a graphical classification of analyzed problems and connect it with algorithms which are capable for solving it. In the research part of this paper we present a multi parametric network approach optimization algorithm united with algorithms for placing transmitters. Next we present two Meta heuristics based optimization algorithms: • Ant Colony Optimization algorithm with local search procedure; • Genetic algorithm with local search procedure. The realization of these two algorithms depends on the topology of the network approach being analyzed. In this paper we analyze two most common types of network approaches: • Mobile phone network approach; • Fixed phone network approach. The two main achievements of... [to full text]

This dissertation studies methods to achieve reliable communication over unreliable channels. Iterative decoding algorithms for low-density parity-check (LDPC) codes and generalized LDPC (GLDPC) codes are analyzed. A new class of error-correcting codes to enhance the reliability of the communication for high-speed systems, such as optical communication systems, is proposed. The class of spatially-coupled GLDPC codes is studied, and a new iterative hard- decision decoding (HDD) algorithm for GLDPC codes is introduced. The main result is that the minimal redundancy allowed by Shannon’s Channel Coding Theorem can be achieved by using the new iterative HDD algorithm with spatially-coupled GLDPC codes. A variety of low-density parity-check (LDPC) ensembles have now been observed to approach capacity with iterative decoding. However, all of them use soft (i.e., non-binary) messages and a posteriori probability (APP) decoding of their component codes. To the best of our knowledge, this is the first system that can approach the channel capacity using iterative HDD. The optimality of a codeword returned by the weighted min-sum (WMS) algorithm, an iterative decoding algorithm which is widely used in practice, is studied as well. The attenuated max-product (AttMP) decoding and weighted min-sum (WMS) decoding for LDPC codes are analyzed. Applying the max-product (and belief- propagation) algorithms to loopy graphs are now quite popular for best assignment problems. This is largely due to their low computational complexity and impressive performance in practice. Still, there is no general understanding of the conditions required for convergence and/or the optimality of converged solutions. This work presents an analysis of both AttMP decoding and WMS decoding for LDPC codes which guarantees convergence to a fixed point when a weight factor, β, is sufficiently small. It also shows that, if the fixed point satisfies some consistency conditions, then it must be both a linear-programming (LP) and maximum-likelihood (ML) decoding solution.

Approximate string matching consists in identifying strings as similar even ifthere is a number of mismatch between them. This technique is one of thesolutions to reduce the exact matching strictness in data comparison. In manycases it is useful to identify stream variation (e.g. audio) or word declension (e.g.prefix, suffix, plural). Approximate string matching can be used to score terms in InformationRetrieval (IR) systems. The benefit is to return results even if query terms doesnot exactly match indexed terms. However, as approximate string matchingalgorithms only consider characters (nor context neither meaning), there is noguarantee that additional matches are relevant matches. This paper presents the effects of some approximate string matchingalgorithms on search results in IR systems. An experimental research design hasbeen conducting to evaluate such effects from two perspectives. First, resultrelevance is analysed with precision and recall. Second, performance is measuredthanks to the execution time required to compute matches. Six approximate string matching algorithms are studied. Levenshtein andDamerau-Levenshtein computes edit distance between two terms. Soundex andMetaphone index terms based on their pronunciation. Jaccard similarity calculatesthe overlap coefficient between two strings. Tests are performed through IR scenarios regarding to different context,information need and search query designed to query on a technicaldocumentation related to software development (man pages from Ubuntu). Apurposive sample is selected to assess document relevance to IR scenarios andcompute IR metrics (precision, recall, F-Measure). Experiments reveal that all tested approximate matching methods increaserecall on average, but, except Metaphone, they also decrease precision. Soundexand Jaccard Similarity are not advised because they fail on too many IR scenarios.Highest recall is obtained by edit distance algorithms that are also the most timeconsuming. Because Levenshtein-Damerau has no significant improvementcompared to Levenshtein but costs much more time, the last one is recommendedfor use with a specialised documentation. Finally some other related recommendations are given to practitioners toimplement IR systems on technical documentation.

Due to the growing mismatch between processor performance and memory latency, many dynamic mechanisms which are “invisible” to the user have been proposed: for example, trace caches and automatic pre-fetch units. However, these dynamic mechanisms have become inadequate due to implicit memory accesses that have become so expensive. On the other hand, compiler-visible mechanisms like SWAR (SIMD Within A Register) and LARs (Line Associative Registers) are potentially more effective at improving data access performance. This thesis investigates applying the same ideas to improve instruction access. ILAR (Instruction LARs) store instructions in wide registers. Instruction blocks are explicitly loaded into ILAR, using block compression to enhance memory bandwidth. The control flow of the program then refers to instructions directly by their position within an ILAR, rather than by lengthy memory addresses. Because instructions are accessed directly from within registers, there is no implicit instruction fetch from memory. This thesis proposes an instruction set architecture for ILAR, investigates a mechanism to load ILAR using the best available block compression algorithm and also develop hardware descriptions for both ILAR and a conventional memory cache model so that performance comparisons could be made on the instruction fetch stage.

Computational learning theory is a branch of theoretical computer science that re-imagines the role of an algorithm from an agent of computation to an agent of learning. The operations of computers become those of the human mind; an important step towards illuminating the limitations of artificial intelligence. The central difference between a learning algorithm and a traditional algorithm is that the learner has access to an oracle who, in constant time, can answer queries about that to be learned. Normally an algorithm would have to discover such information on its own accord. This subtle change in how we model problem solving results in changes in the computational complexity of some classic problems; allowing us to re-examine them in a new light. Specifically two known result are examined: one positive, one negative. It is know that one can efficiently learn Deterministic Finite Automatons with queries, not so of Non-Deterministic Finite Automatons. We generalize these Automatons into Labeled Transition Systems and attempt to learn them using a stronger query.

The Steiner Tree problem has appeared in the Karp's list of the first 21 NP-hard problems and is well known as one of the most fundamental problems in Network Design area. We study the Node-Weighted version of the Prize Collecting Steiner Tree problem. In this problem, we are given a simple graph with a cost and penalty value associated with each node. Our goal is to find a subtree T of the graph minimizing the cost of the nodes in T plus penalty of the nodes not in T. By a reduction from set cover problem it can be easily shown that the problem cannot be approximated in polynomial time within factor of (1-o(1))ln n unless NP has quasi-polynomial time algorithms, where n is the number of vertices of the graph. Moss and Rabani claimed an O(log n)-approximation algorithm for the problem using a Primal-Dual approach in their STOC'01 paper \cite{moss2001}. We show that their algorithm is incorrect by providing a counter example in which there is an O(n) gap between the dual solution constructed by their algorithm and the optimal solution. Further, evidence is given that their algorithm probably does not have a simple fix. We propose a new algorithm which is more involved and introduces novel ideas in primal dual approach for network design problems. Also, our algorithm is a Lagrangian Multiplier Preserving algorithm and we show how this property can be utilized to design an O(log n)-approximation algorithm for the Node-Weighted Quota Steiner Tree problem using the Lagrangian Relaxation method. We also show an application of the Node Weighted Quota Steiner Tree problem in designing algorithm with better approximation factor for Technology Diffusion problem, a problem proposed by Goldberg and Liu in \cite{goldberg2012} (SODA 2013). In Technology Diffusion, we are given a graph G and a threshold θ(v) associated with each vertex v and we are seeking a set of initial nodes called the seed set. Technology Diffusion is a dynamic process defined over time in which each vertex is either active or inactive. The vertices in the seed set are initially activated and each other vertex v gets activated whenever there are at least θ(v) active nodes connected to v through other active nodes. The Technology Diffusion problem asks to find the minimum seed set activating all nodes. Goldberg and Liu gave an O(rllog n)-approximation algorithm for the problem where r and l are the diameter of G and the number of distinct threshold values, respectively. We improve the approximation factor to O(min{r,l}log n) by establishing a close connection between the problem and the Node Weighted Quota Steiner Tree problem.

Changing in variance or volatility with time can be modeled as deterministic by using autoregressive conditional heteroscedastic (ARCH) type models, or as stochastic by using stochastic volatility (SV) models. This study compares these two kinds of models which are estimated on Turkish / USA exchange rate data. First, a GARCH(1,1) model is fitted to the data by using the package E-views and then a Bayesian estimation procedure is used for estimating an appropriate SV model with the help of Ox code. In order to compare these models, the LR test statistic calculated for non-nested hypotheses is obtained.

This thesis analyzes the process of deblurring of degraded images generated by space-variant nonlinear image systems with Gaussian observation noise. The restoration of blurred images is performed by using two methods / a modified version of the Optimum Decoding Based Smoothing Algorithm and the Bootstrap Filter Algorithm which is a version of Particle Filtering methods. A computer software called MATLAB is used for performing the simulations of image estimation. The results of some simulations for various observation and image models are presented.