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We study optimization problems involving eigenvalues of symmetric matrices. We present a nonsmooth optimization technique for a class of nonsmooth functions which are semi-infinite maxima of eigenvalue functions. Our strategy uses generalized gradients and UV space decomposition techniques suited for the norm and other nonsmooth performance criteria. For the class of max-functions, which possesses the so-called primal-dual gradient structure, we compute smooth trajectories along which certain second-order expansions can be obtained. We also give the first- and second-order derivatives of primal-dual function in the space of decision variables Rm under some assumptions. [ABSTRACT FROM AUTHOR]

An implementable algorithm for solving a nonsmooth convex optimization problem is proposed by combining Moreau-Yosida regularization and bundle and quasi-Newton ideas. In contrast with quasi-Newton bundle methods ofMifflin et al. (1998), we only assume that the values of the objective function and its subgradients are evaluated approximately, which makes the method easier to implement. Under some reasonable assumptions, the proposed method is shown to have a Q-superlinear rate of convergence. [ABSTRACT FROM AUTHOR]

Under the condition that the values of the objective function and its subgradient are computed approximately, we introduce a cutting plane and level bundle method for minimizing nonsmooth nonconvex functions by combining cutting plane method with the ideas of proximity control and level constraint. The proposed algorithm is based on the construction of both a lower and an upper polyhedral approximation model to the objective function and calculates new iteration points by solving a subproblem in which the model is employed not only in the objective function but also in the constraints. Compared with other proximal bundle methods, the new variant updates the lower bound of the optimal value, providing an additional useful stopping test based on the optimality gap. Another merit is that our algorithm makes a distinction between affine pieces that exhibit a convex or a concave behavior relative to the current iterate. Convergence to some kind of stationarity point is proved under some looser conditions. [ABSTRACT FROM AUTHOR]

A class of constrained nonsmooth nonconvex optimization problems, that is, piecewise C² objectives with smooth inequality constraints are discussed in this paper. Based on the VU-theory, a superlinear convergent VU-algorithm, which uses a nonconvex redistributed proximal bundle subroutine, is designed to solve these optimization problems. An illustrative example is given to show how this convergent method works on a Second-Order Cone programming problem. [ABSTRACT FROM AUTHOR]

The effects of removable devices' heterouse in different areas on the propagation of malware spreading via removable devices remain unclear. As a result, in this paper, we present a model incorporating the heterogeneous use of removable devices, obtained by dividing the using rate into local area's rate, neighbour area's rate and global area's rate, and then getting the final rate bymultiplying the corresponding area ratio. The model's equilibria and their stability conditions are obtained mathematically and verified by deterministic and stochastic simulations. Simulation results also indicate that the heterogeneity in using rate significantly changes the prospective propagation course of malware. Additionally, the thresholds of removable devices' using rate in neighbour area are given, which can guide us in designing effective countermalware method. [ABSTRACT FROM AUTHOR]

The quasidifferential of a quasidifferentiable function in the sense of Demyanov and Rubinov is not uniquely defined. Xia proposed the notion of the kernelled quasidifferential, which is expected to be a representative for the equivalence class of quasidifferentials. Although the kernelled quasidifferential is known to have good algebraic properties and geometric structure, it is still not very convenient for calculating the kernelled quasidifferentials of - f and min{fi| i #8712; a finite index set I}, where f and fi are kernelled quasidifferentiable functions. In this paper, the notion of adjoint kernelled quasidifferential, which is well-defined for - f and min{fi | i #8712; I}, is employed as a representative of the equivalence class of quasidifferentials. Some algebraic properties of the adjoint kernelled quasidifferential are given and the existence of the adjoint kernelled quasidifferential is explored by means of the minimal quasidifferential and the Demyanov difference of convex sets. Under some condition, a formula of the adjoint kernelled quasidifferential is presented. [ABSTRACT FROM AUTHOR]