runOptimizationOptiSolve: Calculate the solution for the Quadratic Programming (QP)...

In dp3ll1n/SLCDP: Simulation and inference of cell differentiation process governed by logistic growth dynamics

Description

It combines input matrices and constraints for solving the QP problem.

Usage

runOptimizationOptiSolve(MX, dX, W1Inv = NULL, constraints, bvec = NULL)

Arguments

MX	Predictor matrix. It can be M matrix in OLS or Jacobian matrix in the Gauss-Newton algorithm.
dX	Response vector. It can be deltas vector OLS or endValues in the Gauss-Newton algorithm.
W1Inv	Inverse of the estimated covariance matrix. If NULL it is set to the appropriate identity matrix.
constraints	list containing the constraints as generated by setConstraints.
bvec=NULL	values for the inequality constraints (rates >= bvec). If NULL, bvec is filled with 0s.

Value

QP solution.

Examples

runOptimizationOptiSolve(MX,dX,W1Inv=NULL,constraints,bvec=NULL)

dp3ll1n/SLCDP documentation built on Feb. 6, 2021, 9:17 p.m.