Allow for negative error: set relDecrease to 0. Commented parameters.

gtsam/nonlinear/NonlinearOptimizationParameters.h

@@ -23,13 +23,29 @@
 
 namespace gtsam {
 
-// a container for all related parameters
+	/**
-struct NonlinearOptimizationParameters {
+	 *  a container for all related parameters
+	 */
+	struct NonlinearOptimizationParameters {
 
-	typedef boost::shared_ptr<NonlinearOptimizationParameters> shared_ptr ;
+		typedef boost::shared_ptr<NonlinearOptimizationParameters> shared_ptr;
 		typedef NonlinearOptimizationParameters This;
-	typedef boost::shared_ptr<NonlinearOptimizationParameters> sharedThis ;
+		typedef boost::shared_ptr<NonlinearOptimizationParameters> sharedThis;
 
+		double absDecrease_; ///< threshold for the absolute decrease per iteration
+
+		/**
+		 * Relative decrease threshold, where relative error = (new-current)/current.
+		 * This can be set to 0 if there is a possibility for negative error values.
+		 */
+		double relDecrease_; ///< threshold for the relative decrease per iteration
+
+		double sumError_; ///< threshold for the sum of error
+		size_t maxIterations_; ///< maximum number of iterations
+		double lambda_; ///< starting lambda value
+		double lambdaFactor_; ///< factor by which lambda is multiplied
+
+		/// verbosity levels
 		typedef enum {
 			SILENT,
 			ERROR,
@@ -42,86 +58,87 @@ struct NonlinearOptimizationParameters {
 			LINEAR,
 			DAMPED
 		} verbosityLevel;
+		verbosityLevel verbosity_; ///< verbosity
 
+		/// trust-region method mode
 		typedef enum {
-		FAST,
+			FAST, BOUNDED, CAUTIOUS
-		BOUNDED,
-		CAUTIOUS
 		} LambdaMode;
+		LambdaMode lambdaMode_; ///<trust-region method mode
 
-	// thresholds
+		/// if true, solve whole system with QR, otherwise use LDL when possible
-	double absDecrease_; /* threshold for the absolute decrease per iteration */
+		bool useQR_;
-	double relDecrease_; /* threshold for the relative decrease per iteration */
-	double sumError_; /* threshold for the sum of error */
-	size_t maxIterations_ ;
-	double lambda_ ;
-	double lambdaFactor_ ;
 
-	// flags and mode switches
+		/// Default constructor
-	verbosityLevel verbosity_;
+		NonlinearOptimizationParameters() :
-	LambdaMode lambdaMode_;
+				absDecrease_(1e-6), relDecrease_(1e-6), sumError_(0.0), maxIterations_(
-	bool useQR_; 					/// if true, solve whole system with QR, otherwise use LDL when possible
+						100), lambda_(1e-5), lambdaFactor_(10.0), verbosity_(SILENT), lambdaMode_(
+						BOUNDED), useQR_(false) {
+		}
 
-	NonlinearOptimizationParameters(): absDecrease_(1e-6), relDecrease_(1e-6), sumError_(0.0),
+		/// Constructor from doubles
-			maxIterations_(100), lambda_(1e-5), lambdaFactor_(10.0), verbosity_(SILENT),
+		NonlinearOptimizationParameters(double absDecrease, double relDecrease,
-			lambdaMode_(BOUNDED),	useQR_(false) {}
+				double sumError, int iIters = 100, double lambda = 1e-5,
+				double lambdaFactor = 10, verbosityLevel v = SILENT,
+				LambdaMode lambdaMode = BOUNDED, bool useQR = false) :
+				absDecrease_(absDecrease), relDecrease_(relDecrease), sumError_(
+						sumError), maxIterations_(iIters), lambda_(lambda), lambdaFactor_(
+						lambdaFactor), verbosity_(v), lambdaMode_(lambdaMode), useQR_(useQR) {
+		}
 
-	NonlinearOptimizationParameters(double absDecrease, double relDecrease, double sumError,
+		/// Copy constructor
-			int iIters = 100, double lambda = 1e-5, double lambdaFactor = 10,
+		NonlinearOptimizationParameters(
-			verbosityLevel v = SILENT, LambdaMode lambdaMode = BOUNDED, bool useQR = false)
+				const NonlinearOptimizationParameters &parameters) :
-	:absDecrease_(absDecrease), relDecrease_(relDecrease), sumError_(sumError),
+				absDecrease_(parameters.absDecrease_), relDecrease_(
-	 maxIterations_(iIters), lambda_(lambda), lambdaFactor_(lambdaFactor), verbosity_(v),
+						parameters.relDecrease_), sumError_(parameters.sumError_), maxIterations_(
-	 lambdaMode_(lambdaMode), useQR_(useQR) {}
+						parameters.maxIterations_), lambda_(parameters.lambda_), lambdaFactor_(
+						parameters.lambdaFactor_), verbosity_(parameters.verbosity_), lambdaMode_(
+						parameters.lambdaMode_), useQR_(parameters.useQR_) {
+		}
 
-	NonlinearOptimizationParameters(const NonlinearOptimizationParameters &parameters):
+		/// a copy of old instance except new lambda
-		absDecrease_(parameters.absDecrease_),
-		relDecrease_(parameters.relDecrease_),
-		sumError_(parameters.sumError_),
-		maxIterations_(parameters.maxIterations_),
-		lambda_(parameters.lambda_),
-		lambdaFactor_(parameters.lambdaFactor_),
-		verbosity_(parameters.verbosity_),
-		lambdaMode_(parameters.lambdaMode_),
-		useQR_(parameters.useQR_) {}
-
-	/* a copy of old instance except some parameters */
 		sharedThis newLambda_(double lambda) const {
-		sharedThis ptr (boost::make_shared<NonlinearOptimizationParameters>(*this)) ;
+			sharedThis ptr(
-		ptr->lambda_ = lambda ;
+					boost::make_shared < NonlinearOptimizationParameters > (*this));
-		return ptr ;
+			ptr->lambda_ = lambda;
+			return ptr;
 		}
 
-	/* new instance with default parameters except some partially assigned parameters */
+		/// a copy of old instance except new verbosity
-
 		static sharedThis newVerbosity(verbosityLevel verbosity) {
-		sharedThis ptr (boost::make_shared<NonlinearOptimizationParameters>()) ;
+			sharedThis ptr(boost::make_shared<NonlinearOptimizationParameters>());
-		ptr->verbosity_ = verbosity ;
+			ptr->verbosity_ = verbosity;
-		return ptr ;
+			return ptr;
 		}
 
+		/// a copy of old instance except new maxIterations
 		static sharedThis newMaxIterations(int maxIterations) {
-		sharedThis ptr (boost::make_shared<NonlinearOptimizationParameters>()) ;
+			sharedThis ptr(boost::make_shared<NonlinearOptimizationParameters>());
-		ptr->maxIterations_ = maxIterations ;
+			ptr->maxIterations_ = maxIterations;
-		return ptr ;
+			return ptr;
 		}
 
+		/// a copy of old instance except new lambda
 		static sharedThis newLambda(double lambda) {
-		sharedThis ptr (boost::make_shared<NonlinearOptimizationParameters>());
+			sharedThis ptr(boost::make_shared<NonlinearOptimizationParameters>());
-		ptr->lambda_ = lambda ;
+			ptr->lambda_ = lambda;
-		return ptr ;
+			return ptr;
 		}
 
-	static sharedThis newDrecreaseThresholds(double absDecrease, double relDecrease) {
+		/// a copy of old instance except new thresholds
-		sharedThis ptr (boost::make_shared<NonlinearOptimizationParameters>());
+		static sharedThis newDrecreaseThresholds(double absDecrease,
+				double relDecrease) {
+			sharedThis ptr(boost::make_shared<NonlinearOptimizationParameters>());
 			ptr->absDecrease_ = absDecrease;
 			ptr->relDecrease_ = relDecrease;
-		return ptr ;
+			return ptr;
 		}
 
+		/// a copy of old instance except new QR flag
 		static sharedThis newFactorization(bool useQR) {
-		sharedThis ptr (boost::make_shared<NonlinearOptimizationParameters>());
+			sharedThis ptr(boost::make_shared<NonlinearOptimizationParameters>());
-		ptr->useQR_= useQR;
+			ptr->useQR_ = useQR;
-		return ptr ;
+			return ptr;
 		}
-};
+	};
 }

gtsam/nonlinear/NonlinearOptimizer.cpp

@@ -67,7 +67,7 @@ bool check_convergence(
 		else
 			cout << "relativeDecrease: " << setprecision(12) << relativeDecrease << " >= " << relativeErrorTreshold << endl;
 	}
-	bool converged = (relativeDecrease < relativeErrorTreshold)
+	bool converged = (relativeErrorTreshold && (relativeDecrease < relativeErrorTreshold))
 			|| (absoluteDecrease < absoluteErrorTreshold);
 	if (verbosity >= 1 && converged)
 		cout << "converged" << endl;