The effective and efficient numerical solution of Riemann–Hilbert problems has been demonstrated in recent work. With the aid of ideas from the method of nonlinear steepest descent for Riemann– Hilbert problems, the resulting numerical methods have been shown numerically to retain accuracy as values of certain parameters become arbitrarily large. The primary aim of this paper is to prove that t...

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...

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...