6 citations · 15 across the 5 of their papers we have counts for
5 papers
Quasi-Newton methods for minimizing a quadratic function subject to uncertainty
Shen Peng, Gianpiero Canessa, David Ek +1
We investigate quasi-Newton methods for minimizing a strictly convex quadratic function which is subject to errors in the evaluation of the gradients. The methods all give identica…
An optimization derivation of the method of conjugate gradients
David Ek, Anders Forsgren
We give a derivation of the method of conjugate gradients based on the requirement that each iterate minimizes a strictly convex quadratic on the space spanned by the previously ob…
A structured modified Newton approach for solving systems of nonlinear equations arising in interior-point methods for quadratic programming
David Ek, Anders Forsgren
The focus in this work is on interior-point methods for inequality-constrained quadratic programs, and particularly on the system of nonlinear equations to be solved for each value…
Approximate solution of system of equations arising in interior-point methods for bound-constrained optimization
David Ek, Anders Forsgren
The focus in this paper is interior-point methods for bound-constrained nonlinear optimization, where the system of nonlinear equations that arise are solved with Newton's method.…
Exact linesearch limited-memory quasi-Newton methods for minimizing a quadratic function
David Ek, Anders Forsgren
The main focus in this paper is exact linesearch methods for minimizing a quadratic function whose Hessian is positive definite. We give a class of limited-memory quasi-Newton Hess…