Finding optimal three-dimensional molecular configurations based on a limited amount of experimental and/or theoretical data requires efficient nonlinear optimization algorithms. Optimization methods must be able to find atomic configurations that are close to the absolute, or global, minimum error and also satisfy known physical constraints such as minimum separation distances between atoms (b...

A primal-relaxed dual global optimization algorithm is presented along with an extensive review for finding the global minimum energy configurations of microclusters composed by particles interacting with any type of two-body central forces. First, the original nonconvex expression for the total potential energy is transformed to the difference of two convex functions (DC transformation) via an...

A method of training multilayer perceptrons (MLPs) to reach a global or nearly global minimum of the standard mean squared error (MSE) criterion is proposed. It has been found that the region in the weight space that does not have a local minimum of the normalized riskaverting error (NRAE) criterion expands strictly to the entire weight space as the risk-sensitivity index increases to infinity....

The maximum a posteriori (MAP) principle is often used in image restoration and segmentation to deene the optimal solution when both the prior and likelihood distributions are available. MAP estimation is equivalent to minimizing an energy function. It is desirable to nd the global minimum. However, the minimization in the MAP image estimation is non-trivial due to the use of contextual constra...

The problem of low-rank matrix factorization has seen significant attention in recent computer vision research. Problems that use factorization to find solutions include structure from motion, non-rigid object tracking and illumination based reconstructions. Matrix decomposition algorithms, such as singular value decomposition, can be used to obtain the factorizations when all the input data ar...

In this paper an optimization approach is used to solve the problem of nding the minimum distance between concave objects, without the need for partitioning the objects into convex sub-objects. Since the optimization problem is not unimodal (i.e., has more than one local minimum point), a global optimization technique, namely a Genetic Algorithm, is used to solve the concave problem. In order t...

Climate change has direct and indirect consequences on crop production and food security. Agriculture and cropproduction is one of the factors which depend on the weather conditions and it provides the human requirements inmany aspects. The objective of this study is to assess the impacts of future climatic change on irrigated rice yieldusing the CERES-Rice model in the Southern Coast of Caspia...

augmented downhill simplex method (adsm) is introduced here, that is a heuristic combination of downhill simplex method (dsm) with random search algorithm. in fact, dsm is an interpretable nonlinear local optimization method. however, it is a local exploitation algorithm; so, it can be trapped in a local minimum. in contrast, random search is a global exploration, but less efficient. here, rand...

In the present paper, we propose the global full orthogonalization method (Gl-FOM) and global generalized minimum residual (Gl-GMRES) method for solving large and sparse general coupled matrix equations

This paper investigates the minimum cost multicommodity flow problem with uncertain costs and uncertain capacities. Uncertainty theory is used to deal with indeterminacy factors in uncertain network. An (α, β)-minimum cost multicommodity flow model is formulated. Some properties of the model are analyzed. An equivalence relationship between the (α, β)-minimum cost multicommodity flow and the mi...