We consider an iterative computation of negative curvature directions, in large scale optimization frameworks. We show that to the latter purpose, borrowing the ideas in [1,3] and [4], we can fruitfully pair the Conjugate Gradient (CG) method with a recently introduced numerical approach involving...
Nonlinear Optimization
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We present an efficient method for addressing online the inversion of differential task kinematics for redundant manipulators, in the presence of hard limits on joint space motion that can never be violated. The proposed SNS (Saturation in the Null Space) algorithm proceeds by successively...
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With reference to robots that are redundant for a given task, we present a novel and intuitive approach allowing to define a discrete-time joint velocity command that shares the same characteristics of a second-order inverse differential scheme, with specified properties in terms of joint...
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Research in continuous optimization has been active at DIAG since its foundation. Early research was essentially devoted to the theory of exact penalization and to the development of algorithms for the solution of constrained nonlinear programming problems through unconstrained techniques....
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The Linear Complementarity Problem (LCP) consists of finding two nonnegative vectors satisfying linear constraints and complementarity conditions between pairs of components of the same order. The LCP has found many applications in several areas of science, engineering, finance and economics. In...
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In multiobjective optimization, one considers optimization problems with several competing objective functions. For instance, in engineering problems a design often has to be stable and light weighted at the same time. A classical approach to such optimization problems is to formulate suitable...
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The problem of minimizing the transfer time along a given Cartesian path for redundant robots can be approached in two steps, by separating the generation of a joint path associated to the Cartesian path from the exact minimization of motion time under kinematic/dynamic bounds along the obtained...
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We propose a class of preconditioners for large positive definite linear systems, arising in nonlinear optimization frameworks. These preconditioners can be computed as by-product of Krylov-subspace solvers. Preconditioners in our class are chosen by setting the values of some user-dependent...
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