The purpose of this thesis is to develop an optimization model that chooses the optimal and price efficient combination of underlying assets for a equally weighted basket option. To obtain a price efficient combination of underlying assets a function that calculates the basket option price is needed, for further use in an optimization model. The closed-form basket option pricing is a great challenge, due to the lack of a distribution describing the augmented stochastic price process. Many types of approaches to price an basket option has been made. In this thesis, an analytical approximation of the basket option price has been used, where the analytical approximation aims to develop a method to describe the augmented price process. The approximation is done by moment matching, i.e. matching the first two moments of the real distribution of the basket option with an lognormal distribution. The obtained price function is adjusted and used as the objective function in the optimization model. Furthermore, since the goal is to obtain en equally weighted basket option, the appropriate class of optimization models to use are binary optimization problems. This kind of optimization model is in general hard to solve - especially for increasing dimensions. Three different continuous relaxations of the binary problem has been applied in order to obtain continuous problems, that are easier to solve. The results shows that the purpose of this thesis is fulfilled when formulating and solving the optimization problem - both as an binary and continuous nonlinear optimization model. Moreover, the results from a Monte Carlo simulation for correlated stochastic processes shows that the moment matching technique with a lognormal distribution is a good approximation for pricing a basket option. / Syftet med detta examensarbete är att utveckla ett optimeringsverktyg som väljer den optimala och priseffektiva kombinationen av underliggande tillgångar för en likaviktad aktiekorg. För att kunna hitta en priseffektiv kombination av underliggande tillgångar behöver man finna en passande funktion som bestämmer priset på en likaviktad aktiekorg. Prissättningen av dessa typer av optioner är en stor utmaning. Detta är på grund av bristen av en sannolikhetsfördelning som kan beskriva den utökade och korrelerade stokastiska prisprocess som uppstår för en aktiekorg. Många typer av prissättningar har undersökts och tillämpats. I detta arbete har en analytisk approximation använts för att kunna beskriva den underliggande pris processen approximativt. Uppskattningen görs genom att matcha de tvåförsta momenten av den verkliga fördelningen med motsvarande moment för en lognormal fördelning. Den erhållna prisfunktionen justeras och används som målfunktionen i optimeringsmodellen. Binära ickelinjära optimeringsproblem är i allmänhet svåra att lösa - särskilt för ökande dimensioner av variabler. Tre olika kontinuerliga omformuleringar av det binära optimeringsproblemet har gjorts för att erhålla kontinuerliga problem som är lättare att lösa. Resultaten visar att en optimal och priseffektiv kombination av underliggande aktier är möjlig att hitta genom att formulera ett optimeringsproblem - både som en binär och kontinuerlig ickelinjär optimeringsmodell. Dessutom visar resultaten från en Monte Carlo-simulering, i detta fall för korrelerade stokastiska processer, att moment matching metoden utförd med en lognormal fördelning är en god approximation för prissättningen av aktiekorgar.

Neste trabalho propomos uma abordagem computacional especificamente talhada para a solução de problemas de programação matemática discreta-mista com restrições de equilíbrio (MPEC). Para isso, inicialmente, transformamos o MPEC discreto-misto em uma sequência de MPECs contínuos. Na formulação dos MPECs contínuos, inserimos restrições de igualdade e de desigualdades artificias, as quais nos permitem considerar as variáveis discretas como contínuas. Cada MPEC contínuo é transformado em um problema de programação não-linear (PNL) padrão. Isso é feito por meio da reformulação das restrições de complementaridade originais do MPEC contínuo em um conjunto equivalente de restrições usuais de desigualdade. As restrições de igualdade originais do PNL são tratadas por meio da função lagrangiana clássica, as restrições de igualdade artificiais associada às variáveis discretas do PNL são tratadas por meio de uma técnica variante do método de penalidades clássico e as restrições de desigualdade artificias e originais do problema são tratadas por meio do método de reescalamento não-linear integrado proposto neste trabalho. Cada PNL é resolvido por meio de uma abordagem primal-dual do método de reescalamento não-linear integrado (PDRNLI) com atualização dinâmica dos parâmetros e com a estratégia de convergência global proposta. O método PDRNLI é aplicado ao problema de fluxo de potência ótimo reativo com restrições de atuação de dispositivo de controle de tensão associado aos sistemas elétricos IEEE-14, IEEE-30 e IEEE-118 barras. Os resultados numéricos comprovam a eficiência do método PDRNLI proposto para a solução do problema. / In this work we propose a computational approach specifically tailored for solving mixed-discrete mathematical problems with equilibrium constraints (MPEC). For such a purpose, we initially transform the mixed-discrete MPEC problem into a sequence of continuous MPEC problems. In the formulation of the continuous MPECs, we insert artificial equality and inequality constraints, which allow us handling discrete variables as continuous ones. Each continuous MPEC is transformed into a standard nonlinear programming problem (NLP). This is performed by reformulating the original complementarity constraints of the continuous MPEC problems into an equivalent system of standard inequality constraints. The original equality constraints of the NLP problem are handled by means of the classical lagrangian function, while the artificial equality constraints associated with the discrete variables are handled by means of a variant of the classic penalty method. The original and artificial inequality constraints are handled by means of the integrated nonlinear rescaling method proposed in this work. Each NLP is solved by means of a primal-dual version of the integrated nonlinear rescaling approach (PDINLR), with dynamic updating of parameters together with proposed a global convergence strategy. The PDINLR method is applied to the reactive optimal power flow problem with additional constraints associated with the actuation of voltage control devices for the associated with IEEE-14, 30 and 118 bus electrical systems. Numerical results assure the efficiency of the method PDINLR proposed for solving the problem.

<p>U disertaciji je razmatran problem stohastičkog programiranja s ograničenjima tipa jednakosti, odnosno problem minimizacije s ograničenjima koja su u obliku matematičkog očekivanja. Za re&scaron;avanje posmatranog problema kreirana su dva iterativna postupka u kojima se u svakoj iteraciji računa s uzoračkim očekivanjem kao aproksimacijom matematičkog očekivanja. Oba postupka koriste prednosti postupaka s promenljivom veličinom uzorka zasnovanih na adaptivnom ažuriranju veličine uzorka. To znači da se veličina uzorka određuje na osnovu informacija u tekućoj iteraciji. Konkretno, tekuće informacije o preciznosti aproksimacije očekivanja i tačnosti aproksimacije re&scaron;enja problema defini&scaron;u veličinu uzorka za narednu iteraciju. Oba iterativna postupka su zasnovana na linijskom pretraživanju, a kako je u pitanju problem s ograničenjima, i na kvadratnom kaznenom postupku prilagođenom stohastičkom okruženju. Postupci su zasnovani na istim idejama, ali s različitim pristupom.<br />Po prvom pristupu postupak je kreiran za re&scaron;avanje SAA reformulacije problema stohastičkog programiranja, dakle za re&scaron;avanje aproksimacije originalnog problema. To znači da je uzorak definisan pre iterativnog postupka, pa je analiza konvergencije algoritma deterministička. Pokazano je da se, pod standardnim pretpostavkama, navedenim algoritmom dobija podniz iteracija čija je tačka nagomilavanja KKT tačka SAA reformulacije.<br />Po drugom pristupu je formiran algoritam za re&scaron;avanje samog problema<br />stohastičkog programiranja, te je analiza konvergencije stohastička. Predstavljenim algoritmom se generi&scaron;e podniz iteracija čija je tačka nagomilavanja, pod standardnim pretpostavkama za stohastičku optimizaciju, skoro sigurno<br />KKT tačka originalnog problema.<br />Predloženi algoritmi su implementirani na istim test problemima. Rezultati numeričkog testiranja prikazuju njihovu efikasnost u re&scaron;avanju posmatranih problema u poređenju s postupcima u kojima je ažuriranje veličine uzorka<br />zasnovano na unapred definisanoj &scaron;emi. Za meru efikasnosti je upotrebljen<br />broj izračunavanja funkcija. Dakle, na osnovu rezultata dobijenih na skupu<br />testiranih problema može se zaključiti da se adaptivnim ažuriranjem veličine<br />uzorka može u&scaron;tedeti u broju evaluacija funkcija kada su u pitanju i problemi s<br />ograničenjima.<br />Kako je posmatrani problem deterministički, a formulisani postupci su stohastički, prva tri poglavlja disertacije sadrže osnovne pojmove determinističke<br />i stohastiˇcke optimizacije, ali i kratak pregled definicija i teorema iz drugih<br />oblasti potrebnih za lak&scaron;e praćenje analize originalnih rezultata. Nastavak disertacije čini prikaz formiranih algoritama, analiza njihove konvergencije i numerička implementacija.<br />&nbsp;</p> / <p>Stochastic programming problem with equality constraints is considered within thesis. More precisely, the problem is minimization problem with constraints in the form of mathematical expectation. We proposed two iterative methods for solving considered problem. Both procedures, in each iteration, use a sample average function instead of the mathematical expectation function, and employ the advantages of the variable sample size method based on adaptive updating the sample size. That means, the sample size is determined at every iteration using information from the current iteration. Concretely, the current precision of the approximation of expectation and the quality of the approximation of solution determine the sample size for the next iteration. Both iterative procedures are based on the line search technique as well as on the quadratic penalty method adapted to stochastic environment, since the considered problem has constraints. Procedures relies on same ideas, but the approach is different.<br />By first approach, the algorithm is created for solving an SAA reformulation of the stochastic programming problem, i.e., for solving the approximation of the original problem. That means the sample size is determined before the iterative procedure, so the convergence analyses is deterministic. We show that, under the standard assumptions, the proposed algorithm generates a subsequence which accumulation point is the KKT point of the SAA problem. Algorithm formed by the second approach is for solving the stochastic programming problem, and therefore the convergence analyses is stochastic. It generates a subsequence with&nbsp; accumulation point that is almost surely the KKT point of the original problem, under the standard assumptions for stochastic optimization.for sample size. The number of function evaluations is used as measure of efficiency. Results of the set of tested problems suggest that it is possible to make smaller number of function evaluations by adaptive sample size scheduling in the case of constrained problems, too.<br />Since the considered problem is deterministic, but the formed procedures are stochastic, the first three chapters of thesis contain basic notations of deterministic and stochastic optimization, as well as a short sight of definitions and theorems from another fields necessary for easier tracking the original results analysis. The rest of thesis consists of the presented algorithms, their convergence analysis and numerical implementation.</p>

Neste trabalho propomos uma abordagem computacional especificamente talhada para a solução de problemas de programação matemática discreta-mista com restrições de equilíbrio (MPEC). Para isso, inicialmente, transformamos o MPEC discreto-misto em uma sequência de MPECs contínuos. Na formulação dos MPECs contínuos, inserimos restrições de igualdade e de desigualdades artificias, as quais nos permitem considerar as variáveis discretas como contínuas. Cada MPEC contínuo é transformado em um problema de programação não-linear (PNL) padrão. Isso é feito por meio da reformulação das restrições de complementaridade originais do MPEC contínuo em um conjunto equivalente de restrições usuais de desigualdade. As restrições de igualdade originais do PNL são tratadas por meio da função lagrangiana clássica, as restrições de igualdade artificiais associada às variáveis discretas do PNL são tratadas por meio de uma técnica variante do método de penalidades clássico e as restrições de desigualdade artificias e originais do problema são tratadas por meio do método de reescalamento não-linear integrado proposto neste trabalho. Cada PNL é resolvido por meio de uma abordagem primal-dual do método de reescalamento não-linear integrado (PDRNLI) com atualização dinâmica dos parâmetros e com a estratégia de convergência global proposta. O método PDRNLI é aplicado ao problema de fluxo de potência ótimo reativo com restrições de atuação de dispositivo de controle de tensão associado aos sistemas elétricos IEEE-14, IEEE-30 e IEEE-118 barras. Os resultados numéricos comprovam a eficiência do método PDRNLI proposto para a solução do problema. / In this work we propose a computational approach specifically tailored for solving mixed-discrete mathematical problems with equilibrium constraints (MPEC). For such a purpose, we initially transform the mixed-discrete MPEC problem into a sequence of continuous MPEC problems. In the formulation of the continuous MPECs, we insert artificial equality and inequality constraints, which allow us handling discrete variables as continuous ones. Each continuous MPEC is transformed into a standard nonlinear programming problem (NLP). This is performed by reformulating the original complementarity constraints of the continuous MPEC problems into an equivalent system of standard inequality constraints. The original equality constraints of the NLP problem are handled by means of the classical lagrangian function, while the artificial equality constraints associated with the discrete variables are handled by means of a variant of the classic penalty method. The original and artificial inequality constraints are handled by means of the integrated nonlinear rescaling method proposed in this work. Each NLP is solved by means of a primal-dual version of the integrated nonlinear rescaling approach (PDINLR), with dynamic updating of parameters together with proposed a global convergence strategy. The PDINLR method is applied to the reactive optimal power flow problem with additional constraints associated with the actuation of voltage control devices for the associated with IEEE-14, 30 and 118 bus electrical systems. Numerical results assure the efficiency of the method PDINLR proposed for solving the problem.