The trace form gives a connection between the representation ring and the space of invariant bilinear forms of a Lie algebra $L$. This thesis reviews the definition of the trace of an endomorphism of a finitely generated projective module over a commutative ring $R$. We then use this to look at the trace form of a finitely generated projective representation of a Lie algebra $L$ over $R$ and its representation ring. While doing so, we prove a few trace formulas which are useful in the theory of the Dynkin index, an invariant introduced by Dynkin in 1952 to study homomorphisms between simple Lie algebras.

In chapter 2, we study two Kaup-Newell-type matrix spectral problems, derive their soliton hierarchies within the zero curvature formulation, and furnish their bi-Hamiltonian structures by the trace identity to show that they are integrable in the Liouville sense. In chapter 5, we obtain the Riemann theta function representation of solutions for the first hierarchy of generalized Kaup-Newell systems. In chapter 3, using Hirota bilinear forms, we discuss positive quadratic polynomial solutions to generalized bilinear equations, which generate lump or lump-type solutions to nonlinear evolution equations, and propose an algorithm for computing higher-order lump or lump-type solutions. In chapter 4, we study mixed exponential and trigonometric wave solutions (called complexitons) to general bilinear equations, and propose two methods to find complexitons to generalized bilinear equations. We also succeed in proving that by choosing suitable complex coefficients in soliton solutions, multi-complexitons are actually real wave solutions from complex soliton solutions and establish the linear superposition principle for complexion solutions. In each chapter, we present computational examples.

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The main theme of this work is effectiveness and efficiency of Krylov subspace methods and Krylov subspace recycling. While solving long, slowly changing sequences of large linear systems, such as the ones that arise in engineering, there are many issues we need to consider if we want to make the process reliable (converging to a correct solution) and as fast as possible. This thesis is built on three main components. At first, we target bilinear and quadratic form estimation. Bilinear form $c^TA^{-1}b$ is often associated with long sequences of linear systems, especially in optimization problems. Thus, we devise algorithms that adapt cheap bilinear and quadratic form estimates for Krylov subspace recycling. In the second part, we develop a hybrid recycling method that is inspired by a complex CFD application. We aim to make the method robust and cheap at the same time. In the third part of the thesis, we optimize the implementation of Krylov subspace methods on Graphic Processing Units (GPUs). Since preconditioners based on incomplete matrix factorization (ILU, Cholesky) are very slow on the GPUs, we develop a preconditioner that is effective but well suited for GPU implementation. / Ph. D. / In many applications we encounter the repeated solution of a large number of slowly changing large linear systems. The cost of solving these systems typically dominates the computation. This is often the case in medical imaging, or more generally inverse problems, and optimization of designs. Because of the size of the matrices, Gaussian elimination is infeasible. Instead, we find a sufficiently accurate solution using iterative methods, so-called Krylov subspace methods, that improve the solution with every iteration computing a sequence of approximations spanning a Krylov subspace. However, these methods often take many iterations to construct a good solution, and these iterations can be expensive. Hence, we consider methods to reduce the number of iterations while keeping the iterations cheap. One such approach is Krylov subspace recycling, in which we recycle judiciously selected subspaces from previous linear solves to improve the rate of convergence and get a good initial guess. In this thesis, we focus on improving efficiency (runtimes) and effectiveness (number of iterations) of Krylov subspace methods. The thesis has three parts. In the first part, we focus on efficiently estimating sequences of bilinear forms, c^TA⁻¹b. We approximate the bilinear forms using the properties of Krylov subspaces and Krylov subspace solvers. We devise an algorithm that allows us to use Krylov subspace recycling methods to efficiently estimate bilinear forms, and we test our approach on three applications: topology optimization for the optimal design of structures, diffuse optical tomography, and error estimation and grid adaptation in computational fluid dynamics. In the second part, we focus on finding the best strategy for Krylov subspace recycling for two large computational fluid dynamics problems. We also present a new approach, which lets us reduce the computational cost of Krylov subspace recycling. In the third part, we investigate Krylov subspace methods on Graphics Processing Units. We use a lid driven cavity problem from computational fluid dynamics to perform a thorough analysis of how the choice of the Krylov subspace solver and preconditioner influences runtimes. We propose a new preconditioner, which is designed to work well on Graphics Processing Units.