Numerical computation of rare events via large deviation theory

An overview of rare events algorithms based on large deviation theory (LDT) is presented. It covers a range of numerical schemes to compute the large deviation minimizer in various setups, and discusses best practices, common pitfalls, and implementation trade-offs. Generalizations, extensions, and improvements of the minimum action methods are proposed. These algorithms are tested on example problems which illustrate several common difficulties which arise e.g. when the forcing is degenerate or multiplicative, or the systems are infinite-dimensional. Generalizations to processes driven by non-Gaussian noises or random initial data and parameters are also discussed, along with the connection between the LDT-based approach reviewed here and other methods, such as stochastic field theory and optimal control. Finally, the integration of this approach in importance sampling methods using e.g. genealogical algorithms is explored

An efficient spectral method for solving third-kind Volterra integral equations with non-smooth solutions

This paper is concerned with the numerical solution of the third kind Volterra integral equations with non-smooth solutions based on the recursive approach of the spectral Tau method. To this end, a new set of the fractional version of canonical basis polynomials (called FC-polynomials) is introduced. The approximate polynomial solution (called Tau-solution) is expressed in terms of FC-polynomials. The fractional structure of Tau-solution allows recovering the standard degree of accuracy of spectral methods even in the case of non-smooth solutions. The convergence analysis of the method is studied. The obtained numerical results show the accuracy and efficiency of the method compared to other existing methods

Application of general semi-infinite Programming to Lapidary Cutting Problems

We consider a volume maximization problem arising in gemstone cutting industry. The problem is formulated as a general semi-infinite program (GSIP) and solved using an interiorpoint method developed by Stein. It is shown, that the convexity assumption needed for the convergence of the algorithm can be satisfied by appropriate modelling. Clustering techniques are used to reduce the number of container constraints, which is necessary to make the subproblems practically tractable. An iterative process consisting of GSIP optimization and adaptive refinement steps is then employed to obtain an optimal solution which is also feasible for the original problem. Some numerical results based on realworld data are also presented

Intrinsic dimensionality in vision: Nonlinear filter design and applications

Biological vision and computer vision cannot be treated independently anymore. The digital revolution and the emergence of more and more sophisticated technical applications caused a symbiosis between the two communities. Competitive technical devices challenging the human performance rely increasingly on algorithms motivated by the human vision system. On the other hand, computational methods can be used to gain a richer understanding of neural behavior, e.g. the behavior of populations of multiple processing units. The relations between computational approaches and biological findings range from low level vision to cortical areas being responsible for higher cognitive abilities. In early stages of the visual cortex cells have been recorded which could not be explained by the standard approach of orientation- and frequency-selective linear filters anymore. These cells did not respond to straight lines or simple gratings but they fired whenever a more complicated stimulus, like a corner or an end-stopped line, was presented within the receptive field. Using the concept of intrinsic dimensionality, these cells can be classified as intrinsic-two-dimensional systems. The intrinsic dimensionality determines the number of degrees of freedom in the domain which is required to completely determine a signal. A constant image has dimension zero, straight lines and trigonometric functions in one direction have dimension one, and the remaining signals, which require the full number of degrees of freedom, have the dimension two. In this term the reported cells respond to two dimensional signals only. Motivated by the classical approach, which can be realized by orientation- and frequency-selective Gabor-filter functions, a generalized Gabor framework is developed in the context of second-order Volterra systems. The generalized Gabor approach is then used to design intrinsic two-dimensional systems which have the same selectivity properties like the reported cells in early visual cortex. Numerical cognition is commonly assumed to be a higher cognitive ability of humans. The estimation of the number of things from the environment requires a high degree of abstraction. Several studies showed that humans and other species have access to this abstract information. But it is still unclear how this information can be extracted by neural hardware. If one wants to deal with this issue, one has to think about the immense invariance property of number. One can apply a high number of operations to objects which do not change its number. In this work, this problem is considered from a topological perspective. Well known relations between differential geometry and topology are used to develop a computational model. Surprisingly, the resulting operators providing the features which are integrated in the system are intrinsic-two-dimensional operators. This model is used to conduct standard number estimation experiments. The results are then compared to reported human behavior. The last topic of this work is active object recognition. The ability to move the information gathering device, like humans can move their eyes, provides the opportunity to choose the next action. Studies of human saccade behavior suggest that this is not done in a random manner. In order to decrease the time an active object recognition system needs to reach a certain level of performance, several action selection strategies are investigated. The strategies considered within this work are based on information theoretical and probabilistic concepts. These strategies are finally compared to a strategy based on an intrinsic-two-dimensional operator. All three topics are investigated with respect to their relation to the concept of intrinsic dimensionality from a mathematical point of view

Physical phenomena of spectral relationships via quadratic third kind mixed integral equation with discontinuous kernel

Spectral relationships explain many physical phenomena, especially in quantum physics and astrophysics. Therefore, in this paper, we first attempt to derive spectral relationships in position and time for an integral operator with a singular kernel. Second, using these relations to solve a mixed integral equation (MIE) of the second kind in the space {L}_{2}\left[-\mathrm{1, 1}\right]\times C\left[0, T\right], T < 1. The way to do this is to derive a general principal theorem of the spectral relations from the term of the Volterra-Fredholm integral equation (V-FIE), with the help of the Chebyshev polynomials (CPs), and then use the results in the general MIE to discuss its solution. More than that, some special and important cases will be devised that help explain many phenomena in the basic sciences in general. Here, the FI term is considered in position, in ${L}_{2}\left[-\mathrm{1, 1}\right],$ and its kernel takes a logarithmic form multiplied by a general continuous function. While the VI term in time, in C\left[0, T\right], T < 1, and its kernels are smooth functions. Many numerical results are considered, and the estimated error is also established using Maple 2022

Essays on optimal stopping in discrete and continuous time

This thesis deals with optimal stopping of Markov processes in discrete and continuous time. In the first part we study discrete time random walks with infinite time horizon. Here, a main problem of interest and guiding example is the classical Chow-Robbins game, also known as the Sn/n-problem. We derive tight upper and lower bounds for the value function of the Chow- Robbins game and related problems and use these to approximate value function and continuation set of the problems. For the Chow-Robbins game we find all integer values in the continuation set for times n ≤ 489.241. Starting from there we analyze analytic properties of value function and stopping boundary. We show that the value function of the Chow-Robbins game is non-smooth on a dense subset of the continuation set. We also show that the continuation set is non-convex and find numeric evidence that the stopping boundary is not-smooth either. Similar results hold for discrete stopping problems in a fairly general setting. The second part treats continuous time stopping problems with finite time horizon, namely problems with a Brownian motion as a driving process. For these we derive a new class of Fredholm-type integral equations for the stopping set. For large problem classes of interest, we show by analytical arguments that the equation uniquely characterizes the stopping boundary of the problem. We then use the integral equations to rigorously find the limit behavior of the stopping boundary close to the terminal time. We show that the leading-order coefficient is universal for wide classes of problems. We also use the representation for numerical purposes

Blowup in diffusion equations: A survey

AbstractThis paper deals with quasilinear reaction-diffusion equations for which a solution local in time exists. If the solution ceases to exist for some finite time, we say that it blows up. In contrast to linear equations blowup can occur even if the data are smooth and well-defined for all times. Depending on the equation either the solution or some of its derivatives become singular. We shall concentrate on those cases where the solution becomes unbounded in finite time. This can occur in quasilinear equations if the heat source is strong enough. There exist many theoretical studies on the question on the occurrence of blowup. In this paper we shall recount some of the most interesting criteria and most important methods for analyzing blowup. The asymptotic behavior of solutions near their singularities is only completely understood in the special case where the source is a power. A better knowledge would be useful also for their numerical treatment. Thus, not surprisingly, the numerical analysis of this type of problems is still at a rather early stage. The goal of this paper is to collect some of the known results and algorithms and to direct the attention to some open problems