Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 1981 / Vita. / Bibliography: leaves 97-98. / by Dorothy Elliott Sempolinski. / Ph. D. / Ph. D. Massachusetts Institute of Technology, Sloan School of Management

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Doctor of Philosophy / Department of Mathematics / Louis Crane / We exhibit a static, cylindrically symmetric, exact solution to the Euler-Heisenberg field equations (EHFE) and prove that its effective geometry contains (optical) black holes. It is conjectured that there are also soliton solutions to the EHFE which contain black hole geometries.

Aiming at a systematic inclusion of pseudoscalar and vector mesons as active degrees of freedom in an effective Lagrangian, studies have been performed in this thesis concerning the foundations of such an effective Lagrangian as well as tree-level and beyond-tree-level calculations. Hereby, vector mesons are described by antisymmetric tensor fields. First, an existing power counting scheme for both pseudoscalar and vector mesons is extended to include the pseudoscalar-meson singlet in a systematic way. Based on this, tree-level calculations are carried out which are in good agreement with the available experimental data and several processes are predicted. In particular, the ω-π0 transition form factor is in better agreement with experimental data than the prediction done in the vector-meson-dominance model. Furthermore, a Lagrangian with vector mesons is used together with the leading contributions of chiral perturbation theory in order to calculate tree-level reactions in the sector of odd intrinsic parity. It turns out that both the Lagrangian with vector mesons and the Lagrangian of chiral perturbation theory are needed to describe experimental data. Additionally, a feasibility check for one-loop calculations with pseudoscalar and vector mesons in the loop is performed. Thereby, only a limited number of interaction terms in the Lagrangian with vector mesons is used. The results are used to both renormalise the low-energy constants of chiral perturbation theory up to chiral order Q4 and to determine the influence of loops with vector mesons on masses and decay constants of pseudoscalar mesons.

Due to legal, economic and socio-environmental factors, reverse logistics practices and extended producer responsibility have developed into a necessity in many countries. The end results and expectations may differ, but the motivation remains the same. Two significant components in a reverse logistics system -product recovery options and return channels - are the focus of this thesis. The two main issues examined are allocation of the returned products to recovery options, and selection of the collection methods for product returns. The initial segment of this thesis involves the formulation of a linear programming model to determine the optimal allocation of returned products differing in quality to specific recovery options. This model paves the way for a study on the effects of flexibility on product recovery allocation. A computational example utilising experimental data was presented to demonstrate the viability of the proposed model. The results revealed that in comparison to a fixed match between product qualities and recovery options, the product recovery operation appeared to be more profitable with a flexible allocation. The second segment of this thesis addresses the methods employed for the initial collection of returned products. A mixed integer nonlinear programming model was developed to facilitate the selection of optimal collection methods for these products. This integrated model takes three different initial collection methods into consideration. The model is used to solve an illustrative example optimally. However, as the complexity of the issue renders this process ineffective in the face of larger problems, the Lagrangian relaxation method was proposed to generate feasible solutions within reasonable computational times. This method was put to the test and the results were found to be encouraging.

published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

A general framework is described for the solution of two-phase fluid-object interaction problems on the basis of coupling a distributed-Lagrange-multiplier fictitious domain method and a level-set method, intended for application to the problem of seabed scour by ice ridges. The resulting equations are discretized in space using stabilized finite-element methods and integrated in time using the generalized-α method. This approach is simple to implement and applicable to both structured and unstructured meshes in two and three dimensions. By means of examples, it is shown that despite the simplicity of the approach, good results are obtained in comparison with other more computationally demanding methods. A robust approach is utilized for constructing signed-distance functions on arbitrary meshes by introducing artificial numerical diffusivity to improve the robustness of classical signed-distance construction approaches without resorting to common pseudo-time relaxation. Under this approach, signed-distance functions can be rapidly constructed while preserving the numerical convergence properties and, generally, having minimal interfacial perturbation. The method is then applied with a modified deformation procedure for fast and efficient mesh adaptivity, including a discussion how it may be used in computational fluid dynamics. The two-phase fluid-object interaction approach is then customized for modeling of the seabed scour and soil-pipe interaction. In this approach, complex history-dependent soil constitutive models are replaced with a simple strain-rate dependent model. Utilization of this constitutive model along with the framework developed earlier leads to the treatment of seabed scour as a two-phase fluid-object interaction, and the soil-pipe interaction as a fluid-structure interaction problem without the need for remeshing. Good agreement with past experimental and numerical studies are obtained using our approach. The dissertation is concluded by conducting a parametric study of seabed scour in two- and three-dimensions. / text

This thesis is devoted to two $\mathcal{NP}$-complete combinatorial optimization problems arising in computational biology, the well-studied \emph{multiple sequence alignment} problem and the new formulated \emph{interval constraint coloring} problem. It shows that advanced mathematical programming techniques are capable of solving large scale real-world instances from biology to optimality. Furthermore, it reveals alternative methods that provide approximate solutions. In the first part of the thesis, we present a \emph{Lagrangian relaxation} approach for the multiple sequence alignment (MSA) problem. The multiple alignment is one common mathematical abstraction of the comparison of multiple biological sequences, like DNA, RNA, or protein sequences. If the weight of a multiple alignment is measured by the sum of the projected pairwise weights of all pairs of sequences in the alignment, then finding a multiple alignment of maximum weight is $\mathcal{NP}$-complete if the number of sequences is not fixed. The majority of the available tools for aligning multiple sequences implement heuristic algorithms; no current exact method is able to solve moderately large instances or instances involving sequences exhibiting a lower degree of similarity. We present a branch-and-bound (B\&B) algorithm for the MSA problem.\ignore{the multiple sequence alignment problem.} We approximate the optimal integer solution in the nodes of the B\&B tree by a Lagrangian relaxation of an ILP formulation for MSA relative to an exponential large class of inequalities, that ensure that all pairwise alignments can be incorporated to a multiple alignment. By lifting these constraints prior to dualization the Lagrangian subproblem becomes an \emph{extended pairwise alignment} (EPA) problem: Compute the longest path in an acyclic graph, that is penalized a charge for entering ``obstacles''. We describe an efficient algorithm that solves the EPA problem repetitively to determine near-optimal \emph{Lagrangian multipliers} via subgradient optimization. The reformulation of the dualized constraints with respect to additionally introduced variables improves the convergence rate dramatically. We account for the exponential number of dualized constraints by starting with an empty \emph{constraint pool} in the first iteration to which we add cuts in each iteration, that are most violated by the convex combination of a small number of preceding Lagrangian solutions (including the current solution). In this \emph{relax-and-cut} scheme, only inequalities from the constraint pool are dualized. The interval constraint coloring problem appears in the interpretation of experimental data in biochemistry. Monitoring hydrogen-deuterium exchange rates via mass spectroscopy is a method used to obtain information about protein tertiary structure. The output of these experiments provides aggregate data about the exchange rate of residues in overlapping fragments of the protein backbone. These fragments must be re-assembled in order to obtain a global picture of the protein structure. The interval constraint coloring problem is the mathematical abstraction of this re-assembly process. The objective of the interval constraint coloring problem is to assign a color (exchange rate) to a set of integers (protein residues) such that a set of constraints is satisfied. Each constraint is made up of a closed interval (protein fragment) and requirements on the number of elements in the interval that belong to each color class (exchange rates observed in the experiments). We introduce a polyhedral description of the interval constraint coloring problem, which serves as a basis to attack the problem by integer linear programming (ILP) methods and tools, which perform well in practice. Since the goal is to provide biochemists with all possible candidate solutions, we combine related solutions to equivalence classes in an improved ILP formulation in order to reduce the running time of our enumeration algorithm. Moreover, we establish the polynomial-time solvability of the two-color case by the integrality of the linear programming relaxation polytope $\mathcal{P}$, and also present a combinatorial polynomial-time algorithm for this case. We apply this algorithm as a subroutine to approximate solutions to instances with arbitrary but fixed number of colors and achieve an order of magnitude improvement in running time over the (exact) ILP approach. We show that the problem is $\mathcal{NP}$-complete for arbitrary number of colors, and we provide algorithms that, given an instance with $\mathcal{P}\neq\emptyset$, find a coloring that satisfies all the coloring requirements within $\pm 1$ of the prescribed value. In light of our $\mathcal{NP}$-completeness result, this is essentially the best one can hope for. Our approach is based on polyhedral theory and randomized rounding techniques. In practice, data emanating from the experiments are noisy, which normally causes the instance to be infeasible, and, in some cases, even forces $\mathcal{P}$ to be empty. To deal with this problem, the objective of the ILP is to minimize the total sum of absolute deviations from the coloring requirements over all intervals. The combinatorial approach for the two-color case optimizes the same objective function. Furthermore, we use this combinatorial method to compute, in a Lagrangian way, a bound on the minimum total error, which is exploited in a branch-and-bound manner to determine all optimal colorings. Alternatively, we study a variant of the problem in which we want to maximize the number of requirements that are satisfied. We prove that this variant is $\mathcal{APX}$-hard even in the two-color case and thus does not admit a polynomial time approximation scheme (PTAS) unless $\mathcal{P}=\mathcal{NP}$. Therefore, we slightly (by a factor of $(1+\epsilon)$) relax the condition on when a requirement is satisfied and propose a \emph{quasi-polynomial time approximation scheme} (QPTAS) which finds a coloring that ``satisfies'' the requirements of as many intervals as possible.

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Approved for public release, distribution is unlimited / A Halo orbit about a libration point of a restricted three-body system provides additional opportunities for surveillance, communication, and exploratory missions in lieu of the classical spacecraft orbit. Historically libration point missions have focused on Halo orbits and trajectories about the Sun-Earth System. This thesis will focus on libration point orbit solutions in the Earth-Moon system using the restricted three body equations of motion with three low-thrust control functions. These classical dynamics are used to design and optimize orbital trajectories about stable and unstable libration points of the Earth-Moon system using DIDO, a dynamic optimization software. The solutions for the optimized performance are based on a quadratic cost function. Specific constraints and bounds were placed on the potential solution set in order to ensure correct target trajectories. This approach revealed locally optimal solutions for orbits about a stable and unstable libration point. / Lieutenant, United States Navy