A numerically stable dual method for solving strictly convex quadratic programs
An efficient and numerically stable dual algorithm for positive definite quadratic programming is described which takes advantage of the fact that the unconstrained minimum of the objective function can be used as a starting point. Its implementation ...
A quadratically convergent method for minimizing a sum of euclidean norms
We consider the problem of minimizing a sum of Euclidean norms. $$F(x) = \sum\nolimits_{i = 1}^m {||r_i } (x)||$$ here the residuals {ri(x)} are affine functions fromRn toR1 (nź1ź2,m>-2). This arises in a number of applications, including single-and ...
The best parameter subset using the Chebychev curve fitting criterion
The Chebychev (also Minimax andLź Norm) criterion has been widely studied as a method for curve fitting. Published computer codes are available to obtain the optimal parameter estimates to fit a linear function to a set of given points under the ...
A duality theorem for semi-infinite convex programs and their finite subprograms
In this paper, we first establish a general recession condition under which a semi-infinite convex program and its formal lagrangian dual have the same value. We go on to show that, under this condition, the following hold. First, every finite ...
An ellipsoid algorithm for nonlinear programming
We investigate an ellipsoid algorithm for nonlinear programming. After describing the basic steps of the algorithm, we discuss its computer implementation and present a method for measuring computational efficiency. The computational results obtained ...
Distributed asynchronous computation of fixed points
We present an algorithmic model for distributed computation of fixed points whereby several processors participate simultaneously in the calculations while exchanging information via communication links. We place essentially no assumptions on the ...
