Iteratively Re-weighted Least Squares minimization (IRLS) appears for the first time in the approximation practice in the Ph.D. thesis of C. L. Lawson in 1961 for L∞ minimization. In the 1970s extensions of Lawson’s algorithm for `p-minimization were proposed, as reported in the work of M. R. Osborne. IRLS has been proposed for sparse recovery in signal processing in [5] and for total variation...

Finding the energy minima of systems with constraints is a challenging problem. We develop a minimization method based on the projection operator technique to enforce distance and angle constraints in minimization and reaction-path dynamics. The application of the projection operator alone does not maintain the constraints, i.e., they are slightly violated. Therefore, we use the SHAKE-methodolo...

The Maximum Likelihood Ensemble Filter (MLEF) is a control theory based ensemble data assimilation algorithm. The MLEF is presented and its basic equations discussed. Its relation to Kalman filtering is examined, indicating that the MLEF can be viewed as a nonlinear extension of the Kalman filter in the sense that it reduces to the standard Kalman filter for linear operators and Gaussian Probab...

The affine rank minimization problem consists of finding a matrix of minimum rank that satisfies a given system of linear equality constraints. Such problems have appeared in the literature of a diverse set of fields including system identification and control, Euclidean embedding, and collaborative filtering. Although specific instances can often be solved with specialized algorithms, the gene...

Introduction The morphologic features of human sperms are key indicators for monitoring fertility problems in men. Therefore, automated analyzing methods via microscopic videos have become the most favorite policy in infertility treatment during the last decades. Materials and Methods In the proposed method, firstly a hypothesis testing framework was defined to distinguish sperms from backgroun...

The aim of the minimization analysis of network attack graphs (NAGs) is to nd a minimum critical set of exploits so that by preventing them an intruder cannot reach his goal using any attack scenario. This problem is, in fact, a constrained optimization problem. In this paper, a binary particle swarm optimization algorithm, called SwarmNAG, is presented for the minimization analysis of large-sc...

in this paper, a new continuous ant colony optimization (caco) algorithm is proposed for optimal reservoir operation. the paper presents a new method of determining and setting a complete set of control parameters for any given problem, saving the user from a tedious trial and error based approach to determine them. the paper also proposes an elitist strategy for caco algorithm where best solut...

This paper sheds a new light on submodular function minimization and maximization from the viewpoint of discrete convex analysis. L-convex functions and M-concave functions constitute subclasses of submodular functions on an integer interval. Whereas L-convex functions can be minimized efficiently on the basis of submodular (set) function minimization algorithms, M-concave functions are identif...