An Inexact Bundle Method for Solving Large Structured Linear Matrix Inequalities

We introduce Stochastic Dynamic Cutting Plane (StoDCuP), an extension of the Dual Programming (SDDP) algorithm to solve multistage stochastic convex optimization problems. At each iteration, builds lower bounding affine functions not only for cost-to-go functions, as SDDP does, but also some or all nonlinear cost and constraint functions. show almost sure convergence StoDCuP. inexact variant St...

The dogleg method is a classical trust-region technique for globalizing Newton’s method. While it is widely used in optimization, including large-scale optimization via truncatedNewton approaches, its implementation in general inexact Newton methods for systems of nonlinear equations can be problematic. In this paper, we first outline a very general dogleg method suitable for the general inexac...

Standard Kripke models are inadequate to model situations of inexact knowledge with introspection, since positive and negative introspection force the relation of epistemic indiscernibility to be transitive and euclidean. Correlatively, Williamson’s margin for error semantics for inexact knowledge invalidates axioms 4 and 5. We present a new semantics for modal logic which is shown to be comple...

This work proposes the methodology for automatically modeling and mining inexact spatial patterns. We first choose attributed relational graph (ARG) as the information representation of the data. The pattern is modelled as a compact parametric pattern ARG (PARG). We formulate the problem as a parameter estimation task and present the procedure for estimating the parameters of the PARG model giv...

We give a proximal bundle method for minimizing a convex function f over R+. It requires evaluating f and its subgradients with a possibly unknown accuracy ε ≥ 0, and maintains a set of free variables I to simplify its prox subproblems. The method asymptotically finds points that are ε-optimal. In Lagrangian relaxation of convex programs, it allows for ε-accurate solutions of Lagrangian subprob...

We derive a simple accuracy matching strategy for inexact Gauss Newton methods and apply it to the numerical solution of boundary value problems of ordinary diierential equations by collocation. The matching strategy is based on an aane contravariant convergence theorem, i.e., the characteristic constants are invariant under aane transformations of the domain. The inexact Gauss Newton method is...

The additive Schwarz preconditioned inexact Newton (ASPIN) method was recently introduced [X.-C. Cai and D. E. Keyes, SIAM J. Sci. Comput., 24 (2002), pp. 183–200] to solve the systems of nonlinear equations with nonbalanced nonlinearities. Although the ASPIN method has successfully been used to solve some difficult nonlinear equations, its convergence property has not been studied since it was...

A Ulm-like method is proposed in [13] for solving inverse eigenvalue problems with distinct given eigenvalues. The Ulm-like method avoids solving the Jacobian equations used in Newton-like methods and is shown to be quadratically convergent in the root sense. However, the numerical experiments in [3] only show that the Ulm-like method is comparable to the inexact Newton-like method. In this sho...

This work presents computational strategies used in an implementation of the probabilistic discrete cracking model for concrete of Rossi suitable to parallel vector processor (PVP). The computational strategies used for each realization, within the framework of Monte Carlo simulation, are the inexact Newton method to solve the highly nonlinear problem and element-by-element (EBE) iterative stra...