On proving existence of feasible points in equality constrained optimization problems

Various algorithms can compute approximate feasible points or approximate solutions to equality and bound constrained optimization problems. In exhaustive search algorithms for global optimizers and other contexts, it is of interest to construct bounds around such approximate feasible points, then to verify (computationally but rigorously) that an actual feasible point exists within these bounds. Hansen and others have proposed techniques for proving the existence of feasible points within given bounds, but practical implementations have not, to our knowledge, previously been described. Various alternatives are possible in such an implementation, and details must be carefully considered. Also, in addition to Hansen’s technique for handling the underdetermined case, it is important to handle the overdetermined case, when the approximate feasible point corresponds to a point with many active bound constraints. The basic ideas, along with experimental results from an actual implementation, are summarized here. This work was supported in part by National Science Foundation grant CCR-9203730.     

A review of conventional explosives detection using active neutron interrogation

Conventional explosives are relatively easy to obtain and may cause massive harm to people and property. There are several tools employed by law enforcement to detect explosives, but these can be subverted. Active neutron interrogation is a viable alternative to those techniques, and includes: fast neutron analysis, thermal neutron analysis, pulsed fast/thermal neutron analysis, neutron elastic scatter, and fast neutron radiography. These methods vary based on neutron energy and radiation detected. A thorough review of the principles behind, advantages, and disadvantages of the different types of active neutron interrogation is presented.     

On smooth reformulations and direct non-smooth computations for minimax problems

Minimax problems can be approached by reformulating them into smooth problems with constraints or by dealing with the non-smooth objective directly. We focus on verified enclosures of all globally optimal points of such problems. In smooth problems in branch and bound algorithms, interval Newton methods can be used to verify existence and uniqueness of solutions, to be used in eliminating regions containing such solutions, and point Newton methods can be used to obtain approximate solutions for good upper bounds on the global optimum. We analyze smooth reformulation approaches, show weaknesses in them, and compare reformulation to solving the non-smooth problem directly. In addition to analysis and illustrative problems, we exhibit the results of numerical computations on various test problems.     

Experimental study of dependence on humidity and flow rate for a modified flowthrough radon source

Journal of Radioanalytical and Nuclear Chemistry - Flow-through radon sources were widely utilized in the calibration of radon equipment and radon studies. In this paper a broken 266Ra source which...     

Beyond Convex? Global Optimization is Feasible Only for Convex Objective Functions: A Theorem

It is known that there are feasible algorithms for minimizing convex functions, and that for general functions, global minimization is a difficult (NP-hard) problem. It is reasonable to ask whether there exists a class of functions that is larger than the class of all convex functions for which we can still solve the corresponding minimization problems feasibly. In this paper, we prove, in essence, that no such more general class exists. In other words, we prove that global optimization is always feasible only for convex objective functions.     

An interval branch and bound algorithm for bound constrained optimization problems

In this paper, we propose modifications to a prototypical branch and bound algorithm for nonlinear optimization so that the algorithm efficiently handles constrained problems with constant bound constraints. The modifications involve treating subregions of the boundary identically to interior regions during the branch and bound process, but using reduced gradients for the interval Newton method. The modifications also involve preconditioners for the interval Gauss-Seidel method which are optimal in the sense that their application selectively gives a coordinate bound of minimum width, a coordinate bound whose left endpoint is as large as possible, or a coordinate bound whose right endpoint is as small as possible. We give experimental results on a selection of problems with different properties.