Add to ORKGA quasi-Newton method is presented for minimizing a nonlinear function while constraining the variables to be nonnegative and sum to one. The nonnegativity constraints were eliminated by working with the squares of the variables and the resulting problem was solved using Tapia's general theory of quasi-Newton methods for constrained optimization. A user's guide for a computer program implementing this algorithm is provided
Quasi-Newton methods are often used in the frame of non-linear optimization. In those methods, the q...
This thesis begins with the history of operations research and introduces two of its major branches,...
This thesis is concerned with analyzing and improving the performance of quasi-Newton methods for f...
AbstractMost “quasi-Newton” methods in common use for function minimisation use a quadratic model to...
summary:A survey note whose aim is to establish the heuristics and natural relations in a class of Q...
In this paper, we propose a modification of the self-scaling quasi-Newton (DFP) method for unconstra...
AbstractThe secant equation, which underlies all standard ‘quasi-Newton’ minimisation methods, arise...
The success of Newton’s method for smooth optimization, when Hessians are available, motivated the i...
Abstract: "We propose a quasi-Newton algorithm for solving optimization problems with nonlinear equa...
International audienceA new result in convex analysis on the calculation of proximity operators in c...
AbstractQuasi-Newton methods update, at each iteration, the existing Hessian approximation (or its i...
Nonlinear optimization problems that are encountered in science and industry are examined. A method ...
Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained...
Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained...
AbstractA bound on the possible deterioration in the condition number of the inverse Hessian approxi...
Quasi-Newton methods are often used in the frame of non-linear optimization. In those methods, the q...
This thesis begins with the history of operations research and introduces two of its major branches,...
This thesis is concerned with analyzing and improving the performance of quasi-Newton methods for f...
AbstractMost “quasi-Newton” methods in common use for function minimisation use a quadratic model to...
summary:A survey note whose aim is to establish the heuristics and natural relations in a class of Q...
In this paper, we propose a modification of the self-scaling quasi-Newton (DFP) method for unconstra...
AbstractThe secant equation, which underlies all standard ‘quasi-Newton’ minimisation methods, arise...
The success of Newton’s method for smooth optimization, when Hessians are available, motivated the i...
Abstract: "We propose a quasi-Newton algorithm for solving optimization problems with nonlinear equa...
International audienceA new result in convex analysis on the calculation of proximity operators in c...
AbstractQuasi-Newton methods update, at each iteration, the existing Hessian approximation (or its i...
Nonlinear optimization problems that are encountered in science and industry are examined. A method ...
Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained...
Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained...
AbstractA bound on the possible deterioration in the condition number of the inverse Hessian approxi...
Quasi-Newton methods are often used in the frame of non-linear optimization. In those methods, the q...
This thesis begins with the history of operations research and introduces two of its major branches,...
This thesis is concerned with analyzing and improving the performance of quasi-Newton methods for f...