Binary trust-region steepest descent (BTR) and combinatorial integral approximation (CIA) are two recently investigated approaches for the solution of optimization problems with distributed binary-/discrete-valued variables (control functions). We show improved convergence results BTR by imposing a compactness assumption that is similar to theory CIA. As corollary we conclude also constitutes a...

in this study, the roll, yaw and depth fuzzy control of an au- tonomous underwater vehicle (auv) are addressed. yaw and roll angles are regulated only using their errors and rates, but due to the complexity of depth dynamic channel, additional pitch rate quantity is used to improve the depth loop performance. the discussed auv has four aps at the rear of the vehicle as actuators. two rule bases...

Impurity removal is a momentous part of zinc hydrometallurgy process. In this paper, hybrid modeling method mechanism and parameter estimation was proposed on the basis not changing actual production process lead-zinc smeltery. Firstly, overall nonlinear dynamic model established, then deviation between theoretical value detected outlet ion concentration taken as objective function to establish...

New methods make it possible to do approximate steepest descent in O(1) time per move for k-bounded pseudo-Boolean functions using stochastic local search. It is also possible to use the average fitness over the Hamming distance 2 neighborhood as a surrogate fitness function and still retain the O(1) time per move. These are average complexity results. In light of these new results, we examine ...

Automated software refactoring is known to be one of the "hard" combinatorial optimization problems of the search-based software engineering field. The difficulty is mainly due to candidate solution representation, objective function description and necessity of functional behavior preservation of software. The problem is formulated as a combinatorial optimization problem whose objective functi...

This paper presents a new pel recursive motion compensated prediction algorithm for video coding applications. The derivation of the algorithm is based on Recursive Least Squares (RLS) estimation that minimizes the mean square prediction error for each pel (picture element). A comparison with the modified Steepest-descent gradient estimation algorithm shows significant improvement in terms of m...

We address the problem of minimizing objectives from the class of piecewise differentiable functions whose nonsmoothness can be encapsulated in the absolute value function. They possess local piecewise linear approximations with a discrepancy that can be bounded by a quadratic proximal term. This overestimating local model is continuous but generally nonconvex. It can be generated in its abs-no...