Coarse structures involving low electrical contrasts can be profitably imaged by means of cheap and relatively simple methods such as travel time tomography, whereas fine structure involving sub-wavelength detail can only be recovered by inverting full-waveform data. Despite its complexity and high computation costs, full-waveform inversion of GPR data has become a popular tool for high-resolut...

A Hessian matrix in full waveform inversion (FWI) is difficult to compute directly because of high computational cost and an especially large memory requirement. Therefore, Newton-like methods are rarely feasible in realistic large-size FWI problems. We modify the quasi-Newton BFGS method to use a projected Hessian matrix that reduces both the computational cost and memory required, thereby mak...

We present an algorithm for minimizing a sum of functions that combines the computational efficiency of stochastic gradient descent (SGD) with the second order curvature information leveraged by quasi-Newton methods. We unify these disparate approaches by maintaining an independent Hessian approximation for each contributing function in the sum. We maintain computational tractability and limit ...

Primary variable switching appears as a promising numerical technique for variably saturated ̄ows. While the standard pressurebased form of the Richards equation can su€er from poor mass balance accuracy, the mixed form with its improved conservative properties can possess convergence diculties for dry initial conditions. On the other hand, variable switching can overcome most of the stated nu...

A new method for quasi-Newton minimization outperforms BFGS by combining least-change updates of the Hessian with step sizes estimated from a Wishart model of uncertainty. The Hessian update is in the Broyden family but uses a negative parameter, outside the convex range, that is usually regarded as the safe zone for Broyden updates. Although full Newton steps based on this update tend to be to...

We consider a convex optimization problem on linearly constrained cones in Euclidean Jordan algebras. The problem is solved using a damped Newton algorithm. Quadratic convergence to the global minimum is shown using an explicit step-size selection. Moreover, we prove that the algorithm is a smooth discretization of a Newton flow with Lipschitz continuous derivative.

the pseudo-elastic behavior of shape memory alloy (sma) truss and cantilever beam are investigated. brinson’s one-dimensional material model, which uses the twinned and detwinned martensite fractions separately as internal variables, is applied in the algorithm to establish the sma stress-strain characteristics. this material model also incorporates different young’s modulus for austenitic and ...

In part I of this article, we proposed a Lagrange–Newton–Krylov–Schur (LNKS) method for the solution of optimization problems that are constrained by partial differential equations. LNKS uses Krylov iterations to solve the linearized Karush–Kuhn–Tucker system of optimality conditions in the full space of states, adjoints, and decision variables, but invokes a preconditioner inspired by reduced ...

Quasi-Newton methods have played a prominent role, over many years, in the design of effective practical methods for the numerical solution of nonlinear minimization problems and in multi-dimensional zero-finding. There is a wide literature outlining the properties of these methods and illustrating their performance [e.g., [8]]. In addition, most modern optimization libraries house a quasi-Newt...