Searched refs:a_2 (Results 1 – 5 of 5) sorted by relevance
| /external/libgdx/extensions/gdx-bullet/jni/src/bullet/BulletDynamics/Dynamics/ |
| D | btRigidBody.cpp | 308 const btScalar a_0 = a[0], a_1 = a[1], a_2 = a[2]; in btSetCrossMatrixMinus() local
309 res.setValue(0, +a_2, -a_1, in btSetCrossMatrixMinus()
310 -a_2, 0, +a_0, in btSetCrossMatrixMinus()
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| /external/lldb/test/functionalities/data-formatter/data-formatter-advanced/ |
| D | main.cpp | 108 int a_2; member
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| /external/eigen/unsupported/Eigen/ |
| D | Polynomials | 101 The roots of \f$ p(x) = a_0 + a_1 x + a_2 x^2 + a_{3} x^3 + x^4 \f$ are the eigenvalues of
107 0 & 1 & 0 & a_2 \\
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| /external/ceres-solver/docs/source/ |
| D | solving.rst | 271 .. math:: y = a_1 e^{b_1 x} + a_2 e^{b_3 x^2 + c_1}
275 :math:`a_1, a_2, b_1, b_2`, and :math:`c_1`.
278 :math:`a_2`, and given any value for :math:`b_1, b_2` and :math:`c_1`,
280 of :math:`a_1` and :math:`a_2`. It's possible to analytically
281 eliminate the variables :math:`a_1` and :math:`a_2` from the problem
297 additional optimization step to estimate :math:`a_1` and :math:`a_2`
302 linear in :math:`a_1` and :math:`a_2`, i.e.,
304 .. math:: y = f_1(a_1, e^{b_1 x}) + f_2(a_2, e^{b_3 x^2 + c_1})
308 and `a_2`. For the linear case, this amounts to doing a single linear
310 the :math:`a_1` and :math:`a_2` optimization problems will do. The
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| /external/bison/tests/ |
| D | actions.at | 274 %type <val> a_1 a_2 a_5
278 exp: a_1 a_2 { $<val>$ = 3; } { $<val>$ = $<val>3 + 1; } a_5
286 a_2: { $$ = 2; };
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