Lines Matching refs:quadratic

1199 determine a radius such that the reduction predicted by the quadratic
1206 technique solves the first quadratic optimization problem by using a
1230 objective function to the reduction predicted by the quadratic model for
1233 where $q_k$ is the quadratic model. The radius is then updated as
1258 The Newton trust-region method solves the constrained quadratic
1275 rejected, the trust-region radius is reduced, and the quadratic program
1447 The quadratic optimization problem is approximately solved by applying
1474 determine a radius such that the reduction predicted by the quadratic
1481 technique solves the first quadratic optimization problem by using a
1489 predicted by the quadratic model for the full step,
1491 where $q_k$ is the quadratic model. The radius is then updated as
1942 checking if a function is locally quadratic; if so, go do a gradient
1945 The minimum number of quadratic-like steps before a restart is set using
2286 problem. The direction is obtain by solving the quadratic problem
2476 bound-constrained quadratic program, it may not be convex and the BQPIP
2532 interpolatory quadratic model of each residual component. The $m$
2533 quadratic models
2808 it assumes that the objective function is quadratic and convex.
2810 Since the objective function is quadratic, the algorithm does not use a
2820 quadratic optimization. It can be set by using the TAO solver of
2822 assumes the objective function is quadratic, it evaluates the function,
2861 applies a preconditioned conjugate gradient method to a quadratic model