Optimization

UNCONSTRAINED MINIMIZATION

UNIVARIATE FUNCTION

ROUTINE DESCRIPTION
UVMIF Finds the minimum point of a smooth function of a single variable using only function evaluations.
UVMID Finds the minimum point of a smooth function of a single variable using both function evaluations and first derivative evaluations.
UVMGS Finds the minimum point of a non-smooth function of a single variable.
 

MULTIVARIATE FUNCTION

ROUTINE DESCRIPTION
UMINF Minimizes a function of N variables using a quasi-Newton method and a finite-difference gradient.
UMING Minimizes a function of N variables using a quasi-Newton method and a user-supplied gradient.
UMIDH Minimizes a function of N variables using a modified Newton method and a finite-difference Hessian.
UMIAH Minimizes a function of N variables using a modified Newton method and a user-supplied Hessian.
UMCGF Minimizes a function of N variables using a conjugate gradient algorithm and a finite-difference gradient.
UMCGG Minimizes a function of N variables using a conjugate gradient algorithm and a user-supplied gradient.
UMPOL Minimizes a function of N variables using a direct search polytope algorithm.
 

NONLINEAR LEAST SQUARES

ROUTINE DESCRIPTION
UNLSF Solves a nonlinear least-squares problem using a modified Levenberg-Marquardt algorithm and a finite-difference Jacobian.
UNLSJ Solves a nonlinear least squares problem using a modified Levenberg-Marquardt algorithm and a user-supplied Jacobian.
 

MINIMIZATION WITH SIMPLE BOUNDS

ROUTINE DESCRIPTION
BCONF Minimizes a function of N variables subject to bounds on the variables using a quasi- Newton method and a finite-difference gradient.
BCONG Minimizes a function of N variables subject to bounds on the variables using a quasi- Newton method and a user-supplied gradient.
BCODH Minimizes a function of N variables subject to bounds on the variables using a modified Newton method and a finite-difference Hessian.
BCOAH Minimizes a function of N variables subject to bounds on the variables using a modified Newton method and a user-supplied Hessian.
BCPOL Minimizes a function of N variables subject to bounds on the variables using a direct search complex algorithm.
BCLSF Solves a nonlinear least squares problem subject to bounds on the variables using a modified Levenberg-Marquardt algorithm and a finite-difference Jacobian.
BCLSJ Solves a nonlinear least squares problem subject to bounds on the variables using a modified Levenberg-Marquardt algorithm and a user-supplied Jacobian.
BCNLS Solves a nonlinear least-squares problem subject to bounds on the variables and general linear constraints.
 

LINEARLY CONSTRAINED MINIMIZATION

ROUTINE DESCRIPTION
READ_MPS Reads an MPS file containing a linear programming problem or a quadratic programming problem.
MPS_FREE Deallocates the space allocated for the IMSL derived type s_MPS. This routine is usually used in conjunction with READ_MPS.
DENSE_LP Solves a linear programming problem using an active set strategy.
DLPRS Solves a linear programming problem via the revised simplex algorithm.
SLPRS Solves a sparse linear programming problem via the revised simplex algorithm.
TRAN Solves a transportation problem.
QPROG Solves a quadratic programming problem subject to linear equality/inequality constraints.
LCONF Minimizes a general objective function subject to linear equality/inequality constraints.
LCONG Minimizes a general objective function subject to linear equality/inequality constraints and a user-supplied gradiient.
 

NONLINEARLY CONSTRAINED MINIMIZATION

ROUTINE DESCRIPTION
NNLPF Nonlinearly Constrained Minimization using a sequential equality constrained QP method.
NNLPG Nonlinearly Constrained Minimization using a sequential equality constrained QP method and a user-supplied gradient.
 

SERVICE ROUTINES

ROUTINE DESCRIPTION
CDGRD Approximates the gradient using central differences.
FDGRD Approximates the gradient using forward differences.
FDHES Approximates the Hessian using forward differences and function values.
GDHES Approximates the Hessian using forward differences and a user-supplied gradient.
DDJAC Approximates the Jacobian of M functions in N unknowns using divided differences.
FDJAC Approximate the Jacobian of M functions in N unknowns using forward differences.
CHGRD Checks a user-supplied gradient of a function.
CHHES Checks a user-supplied Hessian of an analytic function.
CHJAC Checks a user-supplied Jacobian of a system of equations with M functions in N unknowns.
GGUES Generates points in an N-dimensional space.