417 research outputs found
Elastic demand dynamic network user equilibrium: Formulation, existence and computation
This paper is concerned with dynamic user equilibrium with elastic travel demand (E-DUE) when the trip demand matrix is determined endogenously. We present an infinite-dimensional variational inequality (VI) formulation that is equivalent to the conditions defining a continuous-time E-DUE problem. An existence result for this VI is established by applying a fixed-point existence theorem (Browder, 1968) in an extended Hilbert space. We present three computational algorithms based on the aforementioned VI and its re-expression as a differential variational inequality (DVI): a projection method, a self-adaptive projection method, and a proximal point method. Rigorous convergence results are provided for these methods, which rely on increasingly relaxed notions of generalized monotonicity, namely mixed strongly-weakly monotonicity for the projection method; pseudomonotonicity for the self-adaptive projection method, and quasimonotonicity for the proximal point method. These three algorithms are tested and their solution quality, convergence, and computational efficiency are compared. Our convergence results, which transcend the transportation applications studied here, apply to a broad family of VIs and DVIs, and are the weakest reported to date
An Explicit Method for the Split Feasibility Problem with Self-Adaptive Step Sizes
An explicit iterative method with self-adaptive step-sizes for solving the split feasibility problem is presented. Strong convergence theorem is provided
Iterative Methods for the Elasticity Imaging Inverse Problem
Cancers of the soft tissue reign among the deadliest diseases throughout the world and effective treatments for such cancers rely on early and accurate detection of tumors within the interior of the body. One such diagnostic tool, known as elasticity imaging or elastography, uses measurements of tissue displacement to reconstruct the variable elasticity between healthy and unhealthy tissue inside the body. This gives rise to a challenging parameter identification inverse problem, that of identifying the Lamé parameter μ in a system of partial differential equations in linear elasticity. Due to the near incompressibility of human tissue, however, common techniques for solving the direct and inverse problems are rendered ineffective due to a phenomenon known as the “locking effect”. Alternative methods, such as mixed finite element methods, must be applied to overcome this complication. Using these methods, this work reposes the problem as a generalized saddle point problem along with a presentation of several optimization formulations, including the modified output least squares (MOLS), energy output least squares (EOLS), and equation error (EE) frameworks, for solving the elasticity imaging inverse problem. Subsequently, numerous iterative optimization methods, including gradient, extragradient, and proximal point methods, are explored and applied to solve the related optimization problem. Implementations of all of the iterative techniques under consideration are applied to all of the developed optimization frameworks using a representative numerical example in elasticity imaging. A thorough analysis and comparison of the methods is subsequently presented
Tensor variational inequalities: Theoretical results, numerical methods and applications to an economic equilibrium model
The paper deals with the study of tensor variational inequalities. and some projection methods to solve them. In particular, some properties on the solutions to such an inequality are established and a fixed point theorem is proved. Moreover, some numerical methods are introduced and the convergence analysis of them is investigated. All the theoretical results are applied to analyze a general oligopolistic market equilibrium problem in which each firm produces several commodities and has some production excesses since the equilibrium condition is characterized by means of a tensor variational inequality. A numerical example is also discussed
An extension of the projected gradient method to a Banach space setting with application in structural topology optimization
For the minimization of a nonlinear cost functional under convex
constraints the relaxed projected gradient process is
a well known method. The analysis is classically performed in a Hilbert space
. We generalize this method to functionals which are differentiable in a
Banach space. Thus it is possible to perform e.g. an gradient method if
is only differentiable in . We show global convergence using
Armijo backtracking in and allow the inner product and the scaling
to change in every iteration. As application we present a
structural topology optimization problem based on a phase field model, where
the reduced cost functional is differentiable in . The
presented numerical results using the inner product and a pointwise
chosen metric including second order information show the expected mesh
independency in the iteration numbers. The latter yields an additional, drastic
decrease in iteration numbers as well as in computation time. Moreover we
present numerical results using a BFGS update of the inner product for
further optimization problems based on phase field models
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