Random sampling techniques have been developed for combinatorial optimization problems. In this note, we report an application of one of these techniques for training support vector machines (more precisely, primal-form maximal-margin classifiers) that solve two-group classification problems by using hyperplane classifiers. Through this research, we are aiming (I) to design efficient and theore...

This contribution describes the development and implementation of a novel software toolbox, NDOT, for the dynamic optimization (open loop optimal control) of nonlinear processes. This modular and flexible toolbox combines the control vector parameterization approach with a number of local and global nonlinear programming solvers and suitable dynamic simulation methods. NDOT is able to solve dyn...

This paper studies a parallel version of the Vector Evaluated Particle Swarm Optimization (VEPSO) method for multiobjective problems. Experiments on well known and widely used test problems are performed, aiming at investigating both the efficiency of VEPSO as well as the advantages of the parallel implementation. The obtained results are compared with the corresponding results of the Vector Ev...

Random sampling techniques have been developed for combinatorial optimization problems. In this note, we report an application of one of these techniques for training support vector machines (more precisely, primal-form maximal-margin classifiers) that solve two-group classification problems by using hyperplane classifiers. Through this research, we are aiming (I) to design efficient and theore...

Since parallel computing is again becoming a topic of interest in computer science, it is important to revisit the theoretical foundations of highly parallel computing. “Inherently sequential” computational problems see no significant speedup when run on highly parallel computers. Just as there are efficient approximations for intractable optimization problems, so too are there efficient and hi...

An unconstrained optimization problem is formulated in terms of tropical mathematics to minimize a functional that is defined on a vector set by a matrix and calculated through multiplicative conjugate transposition. For some particular cases, the minimum in the problem is known to be equal to the tropical spectral radius of the matrix. We examine the problem in the common setting of a general ...

Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter. Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome ...

Duality theory is pervasive in finite dimensional optimization. There is growing interest in solving infinite-dimensional optimization problems and hence a corresponding interest in duality theory in infinite dimensions. Unfortunately, many of the intuitions and interpretations common to finite dimensions do not extend to infinite dimensions. In finite dimensions, a dual solution is represented...

Many researchers have controlled and analyzed biped robots that walk in the sagittal plane. Nevertheless, walking robots require the capability to walk merely laterally, when they are faced with the obstacles such as a wall. In walking robot field, both nonlinearity of the dynamic equations and also having a tracking system cause an effective control has to be utilized to address these problems...