this paper has appeared in

An articulated figure is often modeled as a set of rigid segments connected with joints. Its configuration can be altered by varying the joint angles. Although it is straightforward to compute figure configurations given joint angles (forward kinematics), it is not so to find the joint angles for a desired configuration (inverse kinematics). Since the inverse kinematics problem is of special importance to an animator wishing to set a figure to a posture satisfying a set of positioning constraints, researchers have proposed many approaches. But when we try to follow these approaches in an interactive animation system where the object to operate on is as highly articulated as a realistic human figure, they fail in either generality or performance, and so a new approach is fostered. Our approach is based on nonlinear programming techniques. It has been used for several years in the spatial constraint system in the Jack TM human figure simulation software developed at the Compute...

The problem of finding a point that satisfies the sufficient decrease and curvature condition is formulated in terms of finding a point in a set T (). We describe a search algorithms for this problem that produces a sequence of iterates that converge to a point in T () and that, except for pathological cases, terminates in a finite number of steps. Numerical results for an implementation of the search algorithm on a set of test functions show that the algorithm terminates within a small number of iterations. LINE SEARCH ALGORITHMS WITH GUARANTEED SUFFICIENT DECREASE Jorge J. Mor'e and David J. Thuente 1 Introduction Given a continuously differentiable function OE : IR ! IR defined on [0; 1) with OE 0 (0) ! 0, and constants and j in (0; 1), we are interested in finding an ff ? 0 such that OE(ff) OE(0) + OE 0 (0)ff (1:1) and jOE 0 (ff)j jjOE 0 (0)j: (1:2) The development of a search procedure that satisfies these conditions is a crucial ingredient in a line search meth...

this article I will attempt to review the most recent advances in the theory of unconstrained optimization, and will also describe some important open questions. Before doing so, I should point out that the value of the theory of optimization is not limited to its capacity for explaining the behavior of the most widely used techniques. The question

To minimize a convex function, we combine Moreau-Yosida regularizations, quasiNewton matrices and bundling mechanisms. First we develop conceptual forms using "reversal " quasi-Newton formulae and we state their global and local convergence. Then, to produce implementable versions, we incorporate a bundle strategy together with a "curve-search". No convergence results are given for the implementable versions; however some numerical illustrations show their good behaviour even for large-scale problems.

Abstract. The focus of this dissertation is on matrix decompositions that use a limited amount of computer memory � thereby allowing problems with a very large number of variables to be solved. Speci�cally � we will focus on two applications areas � optimization and information retrieval. We introduce a general algebraic form for the matrix update in limited�memory quasi� Newton methods. Many well�known methods such as limited�memory Broyden Family meth� ods satisfy the general form. We are able to prove several results about methods which sat� isfy the general form. In particular � we show that the only limited�memory Broyden Family method �using exact line searches � that is guaranteed to terminate within n iterations on an n�dimensional strictly convex quadratic is the limited�memory BFGS method. Further� more � we are able to introduce several new variations on the limited�memory BFGS method that retain the quadratic termination property. We also have a new result that shows that full�memory Broyden Family methods �using exact line searches � that skip p updates to the quasi�Newton matrix will terminate in no more than n�p steps on an n�dimensional strictly convex quadratic. We propose several new variations on the limited�memory BFGS method

This paper proposes an implementable proximal quasi-Newton method for minimizing a nondifferentiable convex function f in ! n . The method is based on Rockafellar's proximal point algorithm and a cutting-plane technique. At each step, we use an approximate proximal point p a (x k ) of x k to define a v k 2 @ ffl k f(p a (x k )) with ffl k ffkv k k; where ff is a constant. The method monitors the reduction in the value of kv k k to identify when a line search on f should be used. The quasi-Newton step is used to reduce the value of kv k k. Without the differentiability of f , the method converges globally and the rate of convergence is Q-linear. Superlinear convergence is also discussed to extend the characterization result of Dennis and Mor'e. Numerical results show the good performance of the method. Key words. nondifferentiable convex optimization, proximal point, quasi-Newton method, cutting-plane method, bundle methods. AMS subject classifications. 65K05, 90C30 Abbrevia...

Mean-shift analysis is a general nonparametric clustering technique based on density estimation for the analysis of complex feature spaces. The algorithm consists of a simple iterative procedure that shifts each of the feature points to the nearest stationary point along the gradient directions of the estimated density function. It has been successfully applied to many applications such as segmentation and tracking. However, despite its promising performance, there are applications for which the algorithm converges too slowly to be practical. We propose and implement an improved version of the mean-shift algorithm using quasi-Newton methods to achieve higher convergence rates. Another benefit of our algorithm is its ability to achieve clustering even for very complex and irregular feature-space topography. Experimental results demonstrate the efficiency and effectiveness of our algorithm. 1.

We propose an algorithm for nonlinear optimization that employs both trust region techniques and line searches. Unlike traditional trust region methods, our algorithm does not resolve the subproblem if the trial step results in an increase in the objective function, but instead performs a backtracking line search from the failed point. Backtracking can be done along a straight line or along a curved path. We show that the new algorithm preserves the strong convergence properties of trust region methods. Numerical results are also presented.

We propose a BFGS primal-dual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generates feasible iterates and consists in computing approximate solutions of the optimality conditions perturbed by a sequence of positive parameters µ converging to zero. We prove that it converges q-superlinearly for each fixed µ. We also show that it is globally convergent to the analytic center of the primal-dual optimalset when µ tends to 0 and strict complementarity holds.