In this paper, we present a full Newton step feasible interior-pointmethod for circular cone optimization by using Euclidean Jordanalgebra. The search direction is based on the Nesterov-Todd scalingscheme, and only full-Newton step is used at each iteration.Furthermore, we derive the iteration bound that coincides with thecurrently best known iteration bound for small-update methods.

this article proposes a new algorithm for finding a good approximate set of non-dominated solutions for solving generalized traveling salesman problem. Random gravitational emulation search algorithm (RGES (is presented for solving traveling salesman problem. The algorithm based on random search concepts, and uses two parameters, speed and force of gravity in physics. The proposed algorithm is ...

In this paper we continue our earlier study [SM96] on boundary operators for constrained parameter optimization problems. The signi cance of this line of research is based on the observation that usually the global solution for many optimization problems lies on the boundary of the feasible region. Thus, for many constrained numerical optimization problems it might be bene cial to search just t...

An affine-scaling algorithm (ASL) for optimization problems with a single linear equality constraint and box restrictions is developed. The algorithm has the property that each iterate lies in the relative interior of the feasible set. The search direction is obtained by approximating the Hessian of the objective function in Newton’s method by a multiple of the identity matrix. The algorithm is...

Convex games are a natural generalization of matrix (normal-form) games that can compactly model many strategic interactions with interesting structure. We present a new anytime algorithm for such games that leverages fast best-response oracles for both players to build a model of the overall game. This model is used to identify search directions; the algorithm then does an exact minimization i...

Criteria containing a barrier function i.e., an unbounded function at the boundary of the feasible solution domain are frequently encountered in the optimization framework, in particular in interior point methods for constrained optimization. Barrier function has to be carefully handled in the optimization algorithm. When an iterative descent method is used for the minimization, a search along ...

We model salient objects in images as being surrounded by more edges than they have in their interior, and address their detection by an algorithm that searches for optimal nested pairs of rectangular windows. The window that maximizes the sum of edge scores within the picture-frame like region between the two rectangles is returned. The space of all pairs of nested rectangles is a subset of R8...

By a modification of the classic-Newton direction in scaled version for linear optimization, we give a new interior-point algorithm based on a very simple function. The algorithm uses full modified-Newton step, thus no need to perform line search. In the processing of the algorithm, the simple function is used to control the searching direction and measure the proximity of iterates to the centr...