Global optimization subject to bound constraints helps answer many practical questions in chemistry, molecular biology, economics. Most of algorithms for solution of global optimization problems are a combination of interval methods and exhuastive search. The efficiency of such algorithms is characterized by their ability to detect and eliminate sub-optimal feasible regions. This ability is inc...

background and objectives: hospitals usually generate large amounts of hazardous waste. if not managed properly, hospital waste can pose threats to public safety and damage the environment. waste minimization is a waste management approach that focuses on reducing the amount and toxicity of hazardous wastes. this study pursues a two-fold purpose: 1) to gain insight into hospital waste minimizat...

A theory of globally convergent trust-region methods for self-consistent field electronic structure calculations that use the density matrices as variables is developed. The optimization is performed by means of sequential global minimizations of a quadratic model of the true energy. The global minimization of this quadratic model, subject to the idempotency of the density matrix and the rank c...

Simplifying stand structure to reduce fuel density is a high priority for forest managers; however, affects to Pacific marten (Martes caurina) movement and connectivity are unknown. We evaluated whether thinning forests to reduce fuels influenced movements of Pacific marten. We collected movement paths from 22 martens using global positioning system telemetry to evaluate habitat selection and d...

Abstract. We present a line search multigrid method based on Nash’s MG/OPT multilevel optimization approach for solving discretized versions of convex infinite dimensional optimization problems. Global convergence is proved under fairly minimal requirements on the minimization method used at all grid levels. In particular, our convergence proof does not require that these minimization, or so-ca...

A Monte Carlo-minimization method has been developed to overcome the multiple-minima problem. The Metropolis Monte Carlo sampling, assisted by energy minimization, surmounts intervening barriers in moving through successive discrete local minima in the multidimensional energy surface. The method has located the lowest-energy minimum thus far reported for the brain pentapeptide [Met5]enkephalin ...

The iteratively reweighted l1 minimization algorithm (IRL1) has been widely used for variable selection, signal reconstruction and image processing. In this paper, we show that any sequence generated by the IRL1 is bounded and any accumulation point is a stationary point of the l2-lp minimization problem with 0 < p < 1. Moreover, the stationary point is a global minimizer and the convergence ra...

Many margin-based binary classification techniques such as support vector machine (SVM) andψ-learning deliver high performance. An earlier article proposed a new multicategory ψ-learning methodology that shows great promise in generalization ability. However, ψ-learning is computationally difficult because it requires handling a nonconvex minimization problem. In this article, we propose two co...

We develop a framework for a class of derivative-free algorithms for the least-squares minimization problem. These algorithms are designed to take advantage of the problem structure by building polynomial interpolation models for each function in the least-squares minimization. Under suitable conditions, global convergence of the algorithm is established within a trust region framework. Promisi...

For atomistic material models, global minimization gives the wrong qualitative behaviour; a theory of equilibrium solutions needs to be defined in different terms. In this paper, a concept based on gradient flow evolutions, to describe local minimization for simple atomistic models based on the Lennard-Jones potential, is presented. As an application of this technique, it is shown that an atomi...