The equilibriummetric forminimizing a continuous congested trafficmodel is the solution of a variational problem involving geodesic distances.The continuous equilibrium metric and its associated variational problem ar...The equilibriummetric forminimizing a continuous congested trafficmodel is the solution of a variational problem involving geodesic distances.The continuous equilibrium metric and its associated variational problem are closely related to the classical discrete Wardrop’s equilibrium.We propose an adjoint state method to numerically approximate continuous traffic congestion equilibria through the continuous formulation.The method formally derives an adjoint state equation to compute the gradient descent direction so as to minimize a nonlinear functional involving the equilibrium metric and the resulting geodesic distances.The geodesic distance needed for the state equation is computed by solving a factored eikonal equation,and the adjoint state equation is solved by a fast sweeping method.Numerical examples demonstrate that the proposed adjoint state method produces desired equilibrium metrics and outperforms the subgradient marching method for computing such equilibrium metrics.展开更多
The goal of tomography is to reconstruct a spatially-varying image function s(x,m), where x is position and m is a finite-length vector of parameters. Many reconstruction methods minimize the total L2 error E ≡ eTe, ...The goal of tomography is to reconstruct a spatially-varying image function s(x,m), where x is position and m is a finite-length vector of parameters. Many reconstruction methods minimize the total L2 error E ≡ eTe, where individual errors ei quantify misfit between predictions and observations, to quantify goodness of fit. So-called adjoint state methods allow the gradient ∂E/∂mi to be computed extremely efficiently from an adjoint field, facilitating image reconstruction by gradient-descent methods. We examine the structure of the differential equation for the adjoint field under the ray approximation and find that it has the same form as the transport equation, whose solution involves the well-known geometrical spreading function R Consequently, as R is routinely tabulated as part of a ray calculation, no extra work is needed to compute the adjoint field, permitting a rapid calculation of the gradient?∂E/∂mi.展开更多
基金supported by NSF 0810104 and NSF 1115363Leung was supported in part by Hong Kong RGC under Grant GRF603011HKUST RPC under Grant RPC11SC06.
文摘The equilibriummetric forminimizing a continuous congested trafficmodel is the solution of a variational problem involving geodesic distances.The continuous equilibrium metric and its associated variational problem are closely related to the classical discrete Wardrop’s equilibrium.We propose an adjoint state method to numerically approximate continuous traffic congestion equilibria through the continuous formulation.The method formally derives an adjoint state equation to compute the gradient descent direction so as to minimize a nonlinear functional involving the equilibrium metric and the resulting geodesic distances.The geodesic distance needed for the state equation is computed by solving a factored eikonal equation,and the adjoint state equation is solved by a fast sweeping method.Numerical examples demonstrate that the proposed adjoint state method produces desired equilibrium metrics and outperforms the subgradient marching method for computing such equilibrium metrics.
文摘The goal of tomography is to reconstruct a spatially-varying image function s(x,m), where x is position and m is a finite-length vector of parameters. Many reconstruction methods minimize the total L2 error E ≡ eTe, where individual errors ei quantify misfit between predictions and observations, to quantify goodness of fit. So-called adjoint state methods allow the gradient ∂E/∂mi to be computed extremely efficiently from an adjoint field, facilitating image reconstruction by gradient-descent methods. We examine the structure of the differential equation for the adjoint field under the ray approximation and find that it has the same form as the transport equation, whose solution involves the well-known geometrical spreading function R Consequently, as R is routinely tabulated as part of a ray calculation, no extra work is needed to compute the adjoint field, permitting a rapid calculation of the gradient?∂E/∂mi.
