Lines Matching full:trust
55 1. **Trust Region** The trust region approach approximates the
57 over a subset of the search space known as the trust region. If the
59 the trust region is expanded; conversely, otherwise it is
70 Trust region methods are in some sense dual to line search methods:
71 trust region methods first choose a step size (the size of the trust
76 .. _section-trust-region-methods:
78 Trust Region Methods
81 The basic trust region algorithm looks something like this.
83 1. Given an initial point :math:`x` and a trust region radius :math:`\mu`.
94 Here, :math:`\mu` is the trust region radius, :math:`D(x)` is some
99 of the trust region depending on how well the linearization predicts
103 The key computational step in a trust-region algorithm is the solution
110 giving rise to a different concrete trust-region algorithm. Currently
111 Ceres, implements two trust-region algorithms - Levenberg-Marquardt
130 It was also the first trust region algorithm to be developed
204 Another strategy for solving the trust region problem :eq:`trp` was
219 x^{\text{Cauchy}}` that solves the trust region problem. Ceres
226 contained in the trust-region. For more details on the exact reasoning
231 the point that minimizes the trust region problem in this subspace
252 the way the trust region step is computed. e.g., consider the
290 In this case, we solve for the trust region step for the full problem,
317 Note that the basic trust-region algorithm described in
318 Algorithm~\ref{alg:trust-region} is a descent algorithm in that they
332 enables the non-monotonic trust region algorithm as described by Conn,
340 The option to take non-monotonic steps is available for all trust
412 Recall that in both of the trust-region methods described above, the
787 :ref:`section-trust-region-methods` and
995 The trust region step computation algorithm used by
1013 Relax the requirement that the trust-region algorithm take strictly
1045 The size of the initial trust region. When the
1053 The trust region radius is not allowed to grow beyond this value.
1059 The solver terminates, when the trust region becomes smaller than
1066 Lower threshold for relative decrease before a trust-region step is
1074 regularize the the trust region step. This is the lower bound on
1082 regularize the the trust region step. This is the upper bound on
1089 The step returned by a trust region strategy can sometimes be
1092 optimizer can go ahead and try solving with a smaller trust
1266 on each Newton/Trust region step using a coordinate descent
1280 inner iterations in subsequent trust region minimizer iterations is
1331 function value to the change in the the value of the trust
1333 #. ``mu`` is the size of the trust region radius.
1335 the trust region step. For direct/factorization based solvers it
1365 List of iterations at which the trust region minimizer should dump
1366 the trust region problem. Useful for testing and benchmarking. If
1384 The format in which trust region problems should be logged when
1563 // acceptance criterion used by the non-monotonic trust region
1570 // ordinary trust region algorithm is used, this means that the
1573 // non-monotonic trust region algorithm is used
1595 // For trust region algorithms, the ratio of the actual change in
1599 // Size of the trust region at the end of the current iteration. For
1625 trust region step solver.
