OpenCLTemplate.LinearAlgebra.floatOptimization.CurveFitting Class Reference

Curve fitting applications. More...

List of all members.

Classes
class	PNormMinClass
Static Public Member Functions
static float[]	LeastSquares (float[,] A, float[] b, float[] W, float lambda)
	Computes least squares fitting of Ax = b weighted by W and returns x.
static float[]	LeastSquares (float[,] A, float[] b)
	Computes least squares fitting of Ax = b and returns x.
static float[]	LeastSquares (float[,] A, float[] b, float lambda)
	Computes least squares fitting of Ax = b and returns x.
static float[]	PNormMinimization (float[] x0, float[,] A, float[] b, float[] W, float[] lambda, float p, float q)
	Computes the p-norm minimization of Ax - b with weights w, using q-norm regularization with weights lambda on x.
static float[]	PNormMinimization (float[] x0, float[,] A, float[] b, float[] W, float[] lambda, float p, float q, float[,] AeqConstr, float[] bEqConstr)
	Computes the p-norm minimization of Ax - b with weights w, using q-norm regularization with weights lambda on x.
Static Public Attributes
static CLCalc.Program.Kernel	kernelpNorm
	Computes p-norm of a vector, sum(|xi|^p).
static CLCalc.Program.Kernel	kerneldpNorm
	Computes gradients of p-norm.

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Detailed Description

Curve fitting applications.

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Member Function Documentation

static float [] OpenCLTemplate.LinearAlgebra.floatOptimization.CurveFitting.LeastSquares	(	float	A[,],
		float[]	b,
		float	lambda
	)			[static]

Computes least squares fitting of Ax = b and returns x.

Parameters:

	A	Dependent variables measurements
	b	Independent variables measurements
	lambda	Regularization term

static float [] OpenCLTemplate.LinearAlgebra.floatOptimization.CurveFitting.LeastSquares	(	float	A[,],
		float[]	b
	)			[static]

Computes least squares fitting of Ax = b and returns x.

Parameters:

	A	Dependent variables measurements
	b	Independent variables measurements

static float [] OpenCLTemplate.LinearAlgebra.floatOptimization.CurveFitting.LeastSquares	(	float	A[,],
		float[]	b,
		float[]	W,
		float	lambda
	)			[static]

Computes least squares fitting of Ax = b weighted by W and returns x.

Parameters:

	A	Dependent variables measurements
	b	Independent variables measurements
	W	Weights
	lambda	Regularization term

static float [] OpenCLTemplate.LinearAlgebra.floatOptimization.CurveFitting.PNormMinimization	(	float[]	x0,
		float	A[,],
		float[]	b,
		float[]	W,
		float[]	lambda,
		float	p,
		float	q,
		float	AeqConstr[,],
		float[]	bEqConstr
	)			[static]

Computes the p-norm minimization of Ax - b with weights w, using q-norm regularization with weights lambda on x.

Parameters:

	x0	Start point
	A	Dependent variables
	b	Independent variables measurements
	W	Weights of each equation
	lambda	Regularization term of each component of x
	p	Ax - b minimization exponent
	q	x regularization exponent
	AeqConstr	Equality constraint matrix AeqConstr * x = bConstr
	bEqConstr	Equality constraint right hand side

static float [] OpenCLTemplate.LinearAlgebra.floatOptimization.CurveFitting.PNormMinimization	(	float[]	x0,
		float	A[,],
		float[]	b,
		float[]	W,
		float[]	lambda,
		float	p,
		float	q
	)			[static]

Computes the p-norm minimization of Ax - b with weights w, using q-norm regularization with weights lambda on x.

Parameters:

	x0	Start point
	A	Dependent variables
	b	Independent variables measurements
	W	Weights of each equation
	lambda	Regularization term of each component of x
	p	Ax - b minimization exponent
	q	x regularization exponent

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Member Data Documentation

CLCalc.Program.Kernel OpenCLTemplate.LinearAlgebra.floatOptimization.CurveFitting.kerneldpNorm [static]

Computes gradients of p-norm.

CLCalc.Program.Kernel OpenCLTemplate.LinearAlgebra.floatOptimization.CurveFitting.kernelpNorm [static]

Computes p-norm of a vector, sum(|xi|^p).