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Searched
refs:Problems
(Results
1 - 4
of
4
) sorted by null
/external/oauth/core/src/main/java/net/oauth/
OAuthMessage.java
240
OAuthProblemException problem = new OAuthProblemException(OAuth.
Problems
.PARAMETER_ABSENT);
241
problem.setParameter(OAuth.
Problems
.OAUTH_PARAMETERS_ABSENT, OAuth.percentEncode(absent));
OAuth.java
56
public static class
Problems
{
/external/ceres-solver/docs/
changes.tex
45
non-separable non-linear least squares
problems
. With
68
\item Better handling of empty and constant
Problems
.
227
\item Add an optional lower bound to the Levenberg-Marquardt regularizer to prevent oscillating between well and ill posed linear
problems
.
233
\item New iterative linear solver for general sparse
problems
- \texttt{CGNR} and a block Jacobi preconditioner for it.
236
\item Support for writing the linear least squares
problems
to disk in text format so that they can loaded into \texttt{MATLAB}.
solving.tex
15
The general strategy when solving non-linear optimization
problems
is to solve a sequence of approximations to the original problem~\cite{nocedal2000numerical}. At each iteration, the approximation is solved to determine a correction $\Delta x$ to the vector $x$. For non-linear least squares, an approximation can be constructed by using the linearization $F(x+\Delta x) \approx F(x) + J(x)\Delta x$, which leads to the following linear least squares problem:
20
Unfortunately, na\"ively solving a sequence of these
problems
and
24
comes in. Algorithm~\ref{alg:trust-region} describes the basic trust-region loop for non-linear least squares
problems
.
59
The Levenberg-Marquardt algorithm~\cite{levenberg1944method, marquardt1963algorithm} is the most popular algorithm for solving non-linear least squares
problems
. It was also the first trust region algorithm to be developed~\cite{levenberg1944method,marquardt1963algorithm}. Ceres implements an exact step~\cite{madsen2004methods} and an inexact step variant of the Levenberg-Marquardt algorithm~\cite{wright1985inexact,nash1990assessing}.
77
For all but the smallest
problems
the solution of~\eqref{eq:simple} in each iteration of the Levenberg-Marquardt algorithm is the dominant computational cost in Ceres. Ceres provides a number of different options for solving~\eqref{eq:simple}. There are two major classes of methods - factorization and iterative.
121
Some non-linear least squares
problems
have additional structure in
137
$a_1$ and $a_2$ from the problem entirely.
Problems
like these are
148
squares
problems
and refer to {\em Variable Projection} as
168
least squares solve. For non-linear
problems
, any method for solving
169
the $a_1$ and $a_2$ optimization
problems
will do. The only constrain
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