allow to terminate btSolveProjectedGaussSeidel MLCP solver based on a least squares residual threshold (m_leastSquaresResidualThreshold)
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@@ -23,7 +23,18 @@ subject to the following restrictions:
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///This solver is mainly for debug/learning purposes: it is functionally equivalent to the btSequentialImpulseConstraintSolver solver, but much slower (it builds the full LCP matrix)
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class btSolveProjectedGaussSeidel : public btMLCPSolverInterface
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{
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public:
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btScalar m_leastSquaresResidualThreshold;
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btScalar m_leastSquaresResidual;
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btSolveProjectedGaussSeidel()
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:m_leastSquaresResidualThreshold(0),
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m_leastSquaresResidual(0)
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{
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}
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virtual bool solveMLCP(const btMatrixXu & A, const btVectorXu & b, btVectorXu& x, const btVectorXu & lo,const btVectorXu & hi,const btAlignedObjectArray<int>& limitDependency, int numIterations, bool useSparsity = true)
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{
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if (!A.rows())
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@@ -36,10 +47,11 @@ public:
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int i, j, numRows = A.rows();
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float delta;
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btScalar delta;
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for (int k = 0; k <numIterations; k++)
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{
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m_leastSquaresResidual = 0.f;
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for (i = 0; i <numRows; i++)
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{
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delta = 0.0f;
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@@ -61,9 +73,10 @@ public:
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delta += A(i,j) * x[j];
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}
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float aDiag = A(i,i);
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btScalar aDiag = A(i,i);
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btScalar xOld = x[i];
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x [i] = (b [i] - delta) / aDiag;
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float s = 1.f;
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btScalar s = 1.f;
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if (limitDependency[i]>=0)
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{
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@@ -76,6 +89,17 @@ public:
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x[i]=lo[i]*s;
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if (x[i]>hi[i]*s)
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x[i]=hi[i]*s;
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btScalar diff = x[i] - xOld;
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m_leastSquaresResidual += diff*diff;
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}
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btScalar eps = m_leastSquaresResidualThreshold;
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if ((m_leastSquaresResidual < eps) || (k >=(numIterations-1)))
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{
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#ifdef VERBOSE_PRINTF_RESIDUAL
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printf("totalLenSqr = %f at iteration #%d\n", m_leastSquaresResidual,k);
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#endif
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break;
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}
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}
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return true;
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