20 research outputs found
Nonmonotone globalization techniques for the Barzilai-Borwein gradient method.
In this paper we propose new globalization strategies for the Barzilai and Borwein gradient method, based on suitable relaxations of the monotonicity requirements. In particular, we define a class of algorithms that combine nonmonotone watchdog techniques with nonmonotone linesearch rules and we prove the global convergence of these schemes. Then we perform an extensive computational study, which shows the effectiveness of the proposed approach in the solution of large dimensional unconstrained optimization problems
Nonmonotone globalization techniques for the Barzilai-Borwein gradient method
ABSTRACT In this paper we propose new globalization strategies for the Barzilai and Borwein gradient method, based on suitable relaxations of the monotonicity requirements. In particular, we define a class of algorithms that combine nonmonotone watchdog techniques with nonmonotone linesearch rules and we prove the global convergence of these schemes. Then we perform an extensive computational study, which shows the effectiveness of the proposed approach in the solution of large dimensional unconstrained optimization problems
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems
The proximal gradient algorithm has been popularly used for convex
optimization. Recently, it has also been extended for nonconvex problems, and
the current state-of-the-art is the nonmonotone accelerated proximal gradient
algorithm. However, it typically requires two exact proximal steps in each
iteration, and can be inefficient when the proximal step is expensive. In this
paper, we propose an efficient proximal gradient algorithm that requires only
one inexact (and thus less expensive) proximal step in each iteration.
Convergence to a critical point %of the nonconvex problem is still guaranteed
and has a convergence rate, which is the best rate for nonconvex
problems with first-order methods. Experiments on a number of problems
demonstrate that the proposed algorithm has comparable performance as the
state-of-the-art, but is much faster
On Iterative Algorithms for Quantitative Photoacoustic Tomography in the Radiative Transport Regime
In this paper, we describe the numerical reconstruction method for
quantitative photoacoustic tomography (QPAT) based on the radiative transfer
equation (RTE), which models light propagation more accurately than diffusion
approximation (DA). We investigate the reconstruction of absorption coefficient
and/or scattering coefficient of biological tissues. Given the scattering
coefficient, an improved fixed-point iterative method is proposed to retrieve
the absorption coefficient for its cheap computational cost. And we prove the
convergence. To retrieve two coefficients simultaneously, Barzilai-Borwein (BB)
method is applied. Since the reconstruction of optical coefficients involves
the solution of original and adjoint RTEs in the framework of optimization, an
efficient solver with high accuracy is improved from~\cite{Gao}. Simulation
experiments illustrate that the improved fixed-point iterative method and the
BB method are the comparative methods for QPAT in two cases.Comment: 21 pages, 44 figure
The Barzilai and Borwein gradient method with nonmonotone line search for nonsmooth convex optimization problems
The Barzilai and Borwein gradient algorithm has received a great deal of attention in recent decades since it is simple and effective for smooth optimization problems. Whether can it be extended to solve nonsmooth problems? In this paper, we answer this question positively. The Barzilai and Borwein gradient algorithm combined with a nonmonotone line search technique is proposed for nonsmooth convex minimization. The global convergence of the given algorithm is established under suitable conditions. Numerical results show that this method is efficient
Nonmonotone globalization of the finite-difference Newton-GMRES method for nonlinear equations.
In this paper, we study nonmonotone globalization strategies, in connection with the finite-difference inexact Newton-GMRES method for nonlinear equations. We first define a globalization algorithm that combines nonmonotone watchdog rules and nonmonotone derivative-free linesearches related to a merit function, and prove its global convergence under the assumption that the Jacobian is nonsingular and that the iterations of the GMRES subspace method can be completed at each step. Then we introduce a hybrid stabilization scheme employing occasional line searches along positive bases, and establish global convergence towards a solution of the system, under the less demanding condition that the Jacobian is nonsingular at stationary points of the merit function. Through a set of numerical examples, we show that the proposed techniques may constitute useful options to be added in solvers for nonlinear systems of equations. © 2010 Taylor & Francis