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  /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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