computation of modelMismatch in LM
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7bf0d8ebf1
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c9ead8bbd3
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@ -163,14 +163,20 @@ void LevenbergMarquardtOptimizer::iterate() {
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// linearizedCostChange = oldCost - newLinearizedCost = f'f/2 - 1/2 [ f'f + 2f'J * step + step' * J' * J * step]
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// = -f'J * step - step' * J' * J * step / 2 = -(f' + modelResidual') * (modelResidual)
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// (with modelResidual = J * step)
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/* Errors modelResidualList = (*linear) * delta; // modelResidual = A * delta
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Vector modelResidual = concatVectors(modelResidualList); // TODO: is this an ordered list?
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Vector residuals = - linear->jacobian().second; // TODO: optimize this computation, TODO: is there a minus sign?
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double linearizedCostChange = dot(- modelResidual, (residuals + modelResidual / 2.0) );
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// Errors modelResidualList = (*linear) * delta; // modelResidual = A * delta
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// modelResidualList.print("");
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// Vector modelResidual = concatVectors(modelResidualList); // TODO: is this an ordered list?
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//cout << "modelResidual: " << modelResidual << endl;
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// cout << "linear->jacobian().second: " << linear->jacobian().second << endl;
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// cout << "linear->augmentedJacobian().second: " << linear->augmentedJacobian() << endl;
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// cout << "linear->augmentedHessian().second: " << linear->augmentedHessian() << endl;
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// Vector residuals = linear->jacobian().second; // TODO: optimize this computation, TODO: is there a minus sign?
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// double linearizedCostChange = dot(- modelResidual, (- residuals + modelResidual / 2.0) );
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//
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// // Measure of mismatch between original (usually nonlinear) system and its linearized version
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// modelMismatch = costChange / linearizedCostChange;
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// Measure of mismatch between original (usually nonlinear) system and its linearized version
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modelMismatch = costChange / linearizedCostChange;
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*/
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if (error <= state_.error) {
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state_.values.swap(newValues);
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