linear inverse problems
Ill-Posed and Linear Inverse Problems
... ill-posed linear inverse problems that arises in many applications is ...these problems and it’s relation to the kernel, is ...these problems we need some kind of regularization that is ...
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Statistical Physics and Information Theory Perspectives on Linear Inverse Problems.
... real-world problems in machine learning, signal processing, and communications assume that an unknown vector x is measured by a matrix A, resulting in a vector y = Ax +z, where z denotes the noise; we call this a ...
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Filter based methods for statistical linear inverse problems
... the linear inverse problem our aim is to open up the possibility of employing the filtering methodology to (static) inverse problems of the form ...the linear setting as experience has ...
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Filter based methods for statistical linear inverse problems
... the linear inverse problem our aim is to open up the possibility of employing the filtering methodology to (static) inverse problems of the form 1, and nonlinear ...the linear setting ...
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Improving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimation
... (a.k.a. fluxes) as a prototypical example. The first algorithm can be used to efficiently multiply two matrices, as long as one can be expressed as a Kronecker product of two smaller matrices, a condition that is typical ...
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Analysis of the Gibbs Sampler for hierarchical inverse problems
... assumed linear conjugate setting; we also discuss the option of integrating u out of the data likelihood and the resulting marginal ...of linear inverse problems satis- fying our assumptions ...
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Some results on a viscosity splitting algorithm in Hilbert spaces
... constrained linear inverse problems, split feasibility problem, convexly constrained minimization problems, fixed point problems, variational inequal- ities, Nash equilibrium problem in ...
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Aspects of Bayesian inverse problems
... ill-posed linear problems, subject to Gaussian observational noise, Bayesian posterior consistency is considered in the recent papers [44, 3] ∗ ...scalar linear inverse ...ill-posed ...
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Bayesian inverse problems with partial observations
... We study a nonparametric Bayesian approach to linear inverse problems under discrete observations. We use the discrete Fourier transform to convert our model into a truncated Gaussian sequence model, ...
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Convex Relaxation for Low-Dimensional Representation: Phase Transitions and Limitations
... Our results so far focused on the study of linear inverse problems. In Chapter 6, we consider a more application-oriented problem, namely, graph clustering. Graphs are important tools to represent ...
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Solutions for a variational inclusion problem with applications to multiple sets split feasibility problems
... inclusion problems in a Hilbert space and establish a strong convergence theorem of this ...following problems: multiple sets split feasibility problems, system of convex constrained linear ...
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Inverse Problems of Matrix Data Reconstruction
... explore inverse eigenvalue problems with a mixture of linear types arising in general gyroscopic ...updating problems with minimal changes while preserving specifically embedded structures, a ...
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The Method of Characteristics in Inverse Problems of Dynamics
... We will consider the characteristics x δ ( · ) and the real- izations of extremal feedbacks u α δ [t] = u δ (t, x δ (t), s δ (t)), generating them, which satisfy the condition (31). And let x δ h ( · ), u δ h ( · ) be ...
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Inverse problems for nonlinear delay systems
... the inverse problem framework above are an important component in any problem formulation and may involve constant vector parameters, time or spatially dependent functions or even probability ...encountered ...
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Identifiability Analysis of Inverse Problems in Biology
... Two global methods of sensitivity-based identifiability analysis are described and demonstrated their application to the mathematical model of the spread of TB and HIV co-infection. It is shown that four parameters are ...
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Sensitivity functions and their uses in inverse problems
... The Verhulst-Pearl logistic equation is a relatively simple example with easily studied dynamics that is useful in demonstrating the utility of the traditional sensitivity functions as well as the generalized sensitivity ...
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A Parallel Optimization Framework for Inverse Problems
... Non-gradient methods start with one or more initial guesses and follow a set of rules to move towards the solution. These optimization methods can be global or local. Examples of global approaches are genetic algorithms ...
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Adding Constraints to Bayesian Inverse Problems
... The constrained Bayesian inference approach here was de- veloped to address non-uniqueness of the solutions for in- verse problems. However, it can also be extended as a way to solve more general constrained ...
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Design solution Design start Identification solution Identification start
... As discussed for general inverse problem in Section 1, we consider two classes of inverse ion channel problems which have different practical motivations: Identification problems determi[r] ...
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On Open Problems of Nonnegative Inverse Eigenvalues Problem
... three problems, people have been study- ing them in the recent 70 years (refer to the references), the achievements people have got and their limitations and practical application depict were evaluated in schol- ...
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