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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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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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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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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We consider the convex quadratic linearly constrained problem with bounded variables and with huge and dense Hessian matrix that arises in many applications such as the training problem of bias support vector machines. We propose a decomposition algorithmic scheme suitable to parallel...
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In this paper we study new preconditioners to be used within the nonlinear conjugate gradient (NCG) method, for large scale unconstrained optimization. The rationale behind our proposal draws inspiration from quasi-Newton updates, and its aim is to possibly approximate in some sense the inverse of...
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