In this paper, we propose a new sequential quadratic semidefinite programming (SQSDP) method for solving degenerate nonlinear programs (NSDPs), in which produce iteration points by sequence of stabilized (QSDP) subproblems, derive from the minimax problem associated with NSDP. Unlike existing SQSDP methods, proposed one allows us to solve those QSDP subproblems inexactly, and each is feasible. ...

We present the first bioluminescence tomography algorithm that makes use of the PDEconstrained concept, which has shown to lead to significant savings in computation times in similar applications. Implementing a sequential quadratic programming (SQP) method, we solve the forward and inverse problems simultaneously. Using numerical results we show that the PDEconstrained SQP approach leads to ~1...

In recent years, general-purpose sequential quadratic programming (SQP) methods have been developed that can reliably solve constrained optimization problems with many hundreds of variables and constraints. These methods require remarkably few evaluations of the problem functions and can be shown to converge to a solution under very mild conditions on the problem. Some practical and theoretical...

In this paper, assuming cooperative behavior of the decision makers, solution methods for decisionmaking problems in hierarchical organizations under fuzzy randomenvironments are considered. To deal with the formulated two-level linear programming problems involving fuzzy random variables, α-level sets of fuzzy random variables are introduced and an α-stochastic two-level linear programming pro...

We document the calibration of the local volatility in terms of local and implied instantaneous variances; we first explore the theoretical properties of the method for a particular class of volatilities. We confirm the theoretical results through a numerical procedure which uses a Gauss-Newton style approximation of the Hessian in the framework of a sequential quadratic programming (SQP) appro...