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  /external/antlr/antlr-3.4/runtime/Delphi/Sources/Antlr3.Runtime/
Antlr.Runtime.Tools.pas 123 { Methods }
140 { Methods }
193 { Methods }
Antlr.Runtime.Collections.pas 58 { Methods }
Antlr.Runtime.pas 66 { Methods }
195 { Methods }
294 { Methods }
320 { Methods }
395 { Methods }
418 { Methods }
439 { Methods }
590 /// The goal of all lexer rules/methods is to create a token object.
640 { Methods }
701 { Methods }
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  /external/clang/lib/Serialization/
ASTWriter.cpp     [all...]
ASTReader.cpp 434 // Load instance methods
441 // Load factory methods
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  /external/clang/lib/AST/
VTableBuilder.cpp 176 const OverridingMethods& Methods = I->second;
178 for (OverridingMethods::const_iterator I = Methods.begin(),
179 E = Methods.end(); I != E; ++I) {
444 /// Offsets - Keeps track of methods and their offsets.
479 // relationship between the two methods.
500 // The methods must have the same name.
600 /// Methods for iterating over the components.
861 /// MethodInfoMap - The information for all methods in the vtable we're
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  /external/antlr/antlr-3.4/runtime/CSharp2/Sources/Antlr3.Runtime.Tests/
ITreeNodeStreamFixture.cs 630 #region Helper Methods
  /external/clang/lib/Rewrite/Frontend/
RewriteModernObjC.cpp     [all...]
  /external/clang/lib/Sema/
SemaDeclCXX.cpp     [all...]
  /external/ceres-solver/docs/
solving.tex 5 \section{Trust Region Methods}
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.
79 The factorization methods are based on computing an exact solution of~\eqref{eq:lsqr} using a Cholesky or a QR factorization and lead to an exact step Levenberg-Marquardt algorithm. But it is not clear if an exact solution of~\eqref{eq:lsqr} is necessary at each step of the LM algorithm to solve~\eqref{eq:nonlinsq}. In fact, we have already seen evidence that this may not be the case, as~\eqref{eq:lsqr} is itself a regularized version of~\eqref{eq:linearapprox}. Indeed, it is possible to construct non-linear optimization algorithms in which the linearized problem is solved approximately. These algorithms are known as inexact Newton or truncated Newton methods~\cite{nocedal2000numerical}.
99 ourselves to moving along the direction of the gradient. Dogleg methods finds a vector $\Delta x$ defined by $\Delta
216 Recall that in both of the trust-region methods described above, the key computational cost is the solution of a linear least squares problem of the form
314 option: sparse direct methods. These methods store $S$ as a sparse
318 Sparse direct methods, depending on the exact sparsity structure of the Schur complement,
342 Equation~\eqref{eq:schurtrick1} is closely related to {\em Domain Decomposition methods} for solving large linear systems that arise in structural engineering and partial differential eq (…)
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  /external/webkit/PerformanceTests/SunSpider/tests/sunspider-0.9/
string-unpack-code.js     [all...]
  /external/webkit/PerformanceTests/SunSpider/tests/sunspider-0.9.1/
string-unpack-code.js     [all...]
  /prebuilts/tools/common/m2/internal/com/google/code/findbugs/findbugs/2.0.1/
findbugs-2.0.1.jar 

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