This thesis concerns the approximation of optimally controlled partial differential equations for inverse problems in optimal design. Important examples of such problems are optimal material design and parameter reconstruction. In optimal material design the goal is to construct a material that meets some optimality criterion, e.g. to design a beam, with fixed weight, that is as stiff as possible. Parameter reconstrucion concerns, for example, the problem to find the interior structure of a material from surface displacement measurements resulting from applied external forces. Optimal control problems, particularly for partial differential equations, are often ill-posed and need to be regularized to obtain good approximations. We here use the theory of the corresponding Hamilton-Jacobi-Bellman equations to construct regularizations and derive error estimates for optimal design problems. The constructed Pontryagin method is a simple and general method where the first, analytical, step is to regularize the Hamiltonian. Next its Hamiltonian system is computed efficiently with the Newton method using a sparse Jacobian. An error estimate for the difference between exact and approximate objective functions is derived, depending only on the difference of the Hamiltonian and its finite dimensional regularization along the solution path and its L² projection, i.e. not on the difference of the exact and approximate solutions to the Hamiltonian systems. Another treated issue is the relevance of input data for parameter reconstruction problems, where the goal is to determine a spacially distributed coefficient of a partial differential equation from partial observations of the solution. It is here shown that the choice of input data, that generates the partial observations, affects the reconstruction, and that it is possible to formulate meaningful optimality criteria for the input data that enhances the quality of the reconstructed coefficient. In the thesis we present solutions to various applications in optimal material design and reconstruction. / Denna avhandling handlar om approximation av optimalt styrda partiella differentialekvationer för inversa problem inom optimal design. Viktiga exempel på sådana problem är optimal materialdesign och parameterskattning. Inom materialdesign är målet att konstruera ett material som uppfyller vissa optimalitetsvillkor, t.ex. att konstruera en så styv balk som möjligt under en given vikt, medan ett exempel på parameterskattning är att hitta den inre strukturen hos ett material genom att applicera ytkrafter och mäta de resulterande förskjutningarna. Problem inom optimal styrning, speciellt för styrning av partiella differentialekvationer,är ofta illa ställa och måste regulariseras för att kunna lösas numeriskt. Teorin för Hamilton-Jacobi-Bellmans ekvationer används här för att konstruera regulariseringar och ge feluppskattningar till problem inom optimaldesign. Den konstruerade Pontryaginmetoden är en enkel och generell metod där det första analytiska steget är att regularisera Hamiltonianen. I nästa steg löses det Hamiltonska systemet effektivt med Newtons metod och en gles Jacobian. Vi härleder även en feluppskattning för skillnaden mellan den exakta och den approximerade målfunktionen. Denna uppskattning beror endast på skillnaden mellan den sanna och den regulariserade, ändligt dimensionella, Hamiltonianen, båda utvärderade längst lösningsbanan och dessL²-projektion. Felet beror alltså ej på skillnaden mellan den exakta och denapproximativa lösningen till det Hamiltonska systemet. Ett annat fall som behandlas är frågan hur indata ska väljas för parameterskattningsproblem. För sådana problem är målet vanligen att bestämma en rumsligt beroende koefficient till en partiell differentialekvation, givet ofullständiga mätningar av lösningen. Här visas att valet av indata, som genererarde ofullständiga mätningarna, påverkar parameterskattningen, och att det är möjligt att formulera meningsfulla optimalitetsvillkor för indata som ökar kvaliteten på parameterskattningen. I avhandlingen presenteras lösningar för diverse tillämpningar inom optimal materialdesign och parameterskattning. / QC 20100712

Syftet med föreliggande studie var att få en större förståelse för några personers tankar och reflektioner om vad en optimal parrelation innebär. Följande frågeställningar var i fokus: Vilka aspekter kan knytas till en optimal parrelation? På vilket sätt styr olika omvärldsfaktorer? En semistrukturerad intervjuform användes och urvalet bestod av fem personer i åldrarna 30-45 år. Resultatet visade att tilliten hade en avgörande roll för välbefinnandet i en optimal parrelation. Den optimala parrelationens negativa sidor påpekades också, till fördel för den reella relationen. Vänner och intressen visade sig ha en stor betydelse för den egna parrelationens välbefinnande. Det framkom att könsrollerna försvårade den jämlika relationens utveckling, men även hur gränsöverskridande könsroller uppfattades av samhället. Det individualiserade samhället betraktades utifrån två aspekter; dels det positiva med att forma traditioner som passade den egna parrelationen, dels det negativa med denna traditionsfrihet som ansågs kunna resultera i en framtida traditionslöshet. I diskussionen lyftes följande mönster fram för problematisering: den optimala parrelationens aspekter och omvärldsfaktorernas betydelse i enlighet med Bronfenbrenners bioekologiska modell.

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On considère un problème de contrôle optimal, déterministe dont la dynamique dépend de phénomènes "rapides", modélisés sous la forme d'un temps rapide t/epsilon , epsilon petit. On étudie le cas particulier ou le temps rapide intervient de manière périodique dans la dynamique. On étudie le problème moyenne dans les cas périodique et non périodique.

In this study, we consider a relay network, with two transceivers and r relay nodes. We assume that each of relays and the two transceivers have a single antenna. For establishing the connection between these two transceivers, we propose a two-way relaying scheme which takes three phases (time slots) to accomplish the exchange of two information symbols between the two transceivers. In the first and second phases, the transceivers, transmit their signals, toward the relays, one after other. The signals that are received by relays are noisy versions of the original signals. Each relay, multiplies its received signal by a complex beamforming coefficient to adjust the phase and amplitude of the signal. Then in the third phase, each relay transmits the summation of so-obtained signals to both transceivers. Our goal is to find the optimal values of transceivers’ transmit powers and the optimal values of the beamforming coefficients by minimizing the total transmit power subject to quality of service constraints. In our approach, we minimize the total transmit power under two constraints. These two constraints are used to guarantee that the transceivers’ receive Signal-to-Noise Ratios (SNRs) are above given thresholds. To solve the underlying optimization problem, we develop two techniques. The first technique is a combination of a two-dimensional search and Second-Order Convex Cone Programming (SOCP). More specifically, the set of feasible values of transceivers’ transmit powers is quantized into a sufficient fine grid. Then, at each vertice of this grid, an SOCP problem is solved to obtain the beamforming coefficients such that for the given pair of transceivers’ transmit powers, the total transmit power is minimized. The pair of the transceivers’ transmit powers, which result in the smallest possible value of the total transmit power, leads us to the solution of the problem. This approach requires a two-dimensional search and solving an SOCP problem at each point of the corresponding two-dimensional grid. Thus, it can be prohibitively expensive in terms of computational complexity. As a second method, we resort to a gradient based steepest descent technique. Our simulation results show that this second technique performs very close to the optimal two-dimensional search based algorithm. Finally we compare our technique with multi-relay distributed beamforming schemes, previously developed in literature and show that our three-phase two-way relaying scheme requires less total power as compared to the two-phase two-way relaying method. On the other hand, the two-phase two-way relaying achieves higher data rates when compared with three-phase two-way relaying for the same total transmit power. Also, we observe that the three-phase scheme has more degrees of freedom while multi-relay distributed beamforming schemes, previously developed in literature appears to be more bandwidth efficient. / UOIT

This thesis is devoted to subriemannian optimal transportation problems. In the first part of the thesis, we consider cost functions arising from very general optimal control costs. We prove the existence and uniqueness of an optimal map between two given measures under certain regularity and growth assumptions on the Lagrangian, absolute continuity of the measures with respect to the Lebesgue class, and, most importantly, the absence of sharp abnormal minimizers. In particular, this result is applicable in the case where the cost function is square of the subriemannian distance on a subriemannian manifold with a 2-generating distribution. This unifies and generalizes the corresponding Riemannian and subriemannian results of Brenier, McCann, Ambrosio-Rigot and Bernard-Buffoni. We also establish various properties of the optimal plan when abnormal minimizers are present. The second part of the thesis is devoted to the infinite-dimensional geometry of optimal transportation on a subriemannian manifold. We start by proving the following nonholonomic version of the classical Moser theorem: given a bracket-generating distribution on a connected compact manifold (possibly with boundary), two volume forms of equal total volume can be isotoped by the flow of a vector field tangent to this distribution. Next, we describe formal solutions of the corresponding subriemannian optimal transportation problem and present the Hamiltonian framework for both the Otto calculus and its subriemannian counterpart as infinite-dimensional Hamiltonian reductions on diffeomorphism groups. Finally, we define a subriemannian analog of the Wasserstein metric on the space of densities and prove that the subriemannian heat equation defines a gradient flow on the subriemannian Wasserstein space with the potential given by the Boltzmann relative entropy functional. Measure contraction property is one of the possible generalizations of Ricci curvature bound to more general metric measure spaces. In the third part of the thesis, we discuss when a three dimensional contact subriemannian manifold satisfies such property.

This thesis is devoted to subriemannian optimal transportation problems. In the first part of the thesis, we consider cost functions arising from very general optimal control costs. We prove the existence and uniqueness of an optimal map between two given measures under certain regularity and growth assumptions on the Lagrangian, absolute continuity of the measures with respect to the Lebesgue class, and, most importantly, the absence of sharp abnormal minimizers. In particular, this result is applicable in the case where the cost function is square of the subriemannian distance on a subriemannian manifold with a 2-generating distribution. This unifies and generalizes the corresponding Riemannian and subriemannian results of Brenier, McCann, Ambrosio-Rigot and Bernard-Buffoni. We also establish various properties of the optimal plan when abnormal minimizers are present. The second part of the thesis is devoted to the infinite-dimensional geometry of optimal transportation on a subriemannian manifold. We start by proving the following nonholonomic version of the classical Moser theorem: given a bracket-generating distribution on a connected compact manifold (possibly with boundary), two volume forms of equal total volume can be isotoped by the flow of a vector field tangent to this distribution. Next, we describe formal solutions of the corresponding subriemannian optimal transportation problem and present the Hamiltonian framework for both the Otto calculus and its subriemannian counterpart as infinite-dimensional Hamiltonian reductions on diffeomorphism groups. Finally, we define a subriemannian analog of the Wasserstein metric on the space of densities and prove that the subriemannian heat equation defines a gradient flow on the subriemannian Wasserstein space with the potential given by the Boltzmann relative entropy functional. Measure contraction property is one of the possible generalizations of Ricci curvature bound to more general metric measure spaces. In the third part of the thesis, we discuss when a three dimensional contact subriemannian manifold satisfies such property.

Piezoelectric actuators have proven to be useful in suppressing disturbances and shape control of flexible structures. Large space structures such as solar arrays are susceptible to large amplitude vibrations while in orbit. Moreover, Shape control of many high precision structures such as large membrane mirrors and space antenna is of great importance. Since most of these structures need to be ultra-light-weight, only a limited number of actuators can be used. Consequently, in order to obtain the most effcient control system, the locations of the piezoelectric elements as well as the feedback gain should be optimized. These optimization problems are generally non-convex. In addition, the models for these systems typically have a large number of degrees of freedom. Researchers have used numerous optimization criteria and optimization techniques to find the optimal actuator locations in structural shape and vibration control. Due to the non-convex nature of the problem, evolutionary optimization techniques are extensively used. However, One drawback of these methods is that they do not use the gradient information and so convergence can be very slow. Classical gradient-based techniques, on the other hand, have the advantage of accurate computation; however, they may be computationally expensive, particularly since multiple initial conditions are typically needed to ensure that a global optimum is found. Consequently, a fast, yet global optimization method applicable to systems with a large number of degrees of freedom is needed. In this study, the feedback control is chosen to be an optimal linear quadratic regulator. The optimal actuator location problem is reformulated as a convex optimization problem. A subgradient-based optimization scheme which leads to the global solution of the problem is introduced to optimize the actuator locations. The optimization algorithm is applied to optimize the placement of piezoelectric actuators in vibration control of flexible structures. This method is compared with a genetic algorithm, and is observed to be faster in finding the global optimum. Moreover, by expanding the desired shape into the structure’s modes of vibration, a methodology for shape control of structures is presented. Applying this method, locations of piezoelectric actuators on flexible structures are optimized. Very few experimental studies exist on shape and vibration control of structures. To the best knowledge of the author, optimal actuator placement in shape control has not been experimentally studied in the past. In this work, vibration control of a cantilever beam is investigated for various actuator locations and the effect of optimal actuator placement is studied on suppressing disturbances to the beam. Also using the proposed shape control method, the effect of optimal actuator placement is studied on the same beam. The final shape of the beam and input voltages of actuators are compared for various actuator placements.

Enterococci bacteria that cannot be treated eectively with the antibiotic vancomycin are termed Vancomycin-Resistant Enterococci (VRE). In this thesis, we develop a mathematical framework for determining optimal strategies for prevention and treatment of VRE in an Intensive Care Unit (ICU). A system of ve ordinary dierential equations describes the movement of ICU patients in and out of dierent states related to VRE infection. Two control variables representing the prevention and treatment of VRE are incorporated into the system. An optimal control problem is formulated to minimize the VRE-related deaths and costs associated with controls over a nite time period. Pontryagin's Minimum Principle is used to characterize optimal controls by deriving a Hamiltonian expression and dierential equations for ve adjoint variables. Numerical solutions to the optimal control problem illustrate how hospital policy makers can use our mathematical framework to investigate optimal cost-eective prevention and treatment schedules during a VRE outbreak. / McAnulty College and Graduate School of Liberal Arts; / Computational Mathematics / MS; / Thesis;