NAG CL Interface
Optimization

Settings help

CL Name Style:


This selection provides a list of functions from the NAG Library for solving various mathematical optimization problems ordered by the problem type. It spans across Chapters E04, E05 and H. Please refer to the E04, E05 and H Chapter Introductions for help with the problem type classification, algorithmic details and advice on the selection of the right solver. See the full chapter tables of contents as listed above for additional functionality not listed here, such as file I/O or option setting functions.
Linear programming (LP),  
dense,  
active-set method/primal simplex,  
alternative 1   e04mfc
alternative 2   e04ncc
sparse,  
interior point method (IPM)   e04mtc
active-set method/primal simplex,  
recommended (see Section 4.3 in the E04 Chapter Introduction)   e04nqc
alternative   e04nkc
Quadratic programming (QP),  
dense,  
active-set method for (possibly nonconvex) QP problem   e04nfc
active-set method for convex QP problem   e04ncc
sparse,  
active-set method sparse convex QP problem,  
recommended (see Section 4.3 in the E04 Chapter Introduction)   e04nqc
alternative   e04nkc
interior point method (IPM) for (possibly nonconvex) QP problems   e04stc
Second-order Cone Programming (SOCP),  
dense or sparse,  
interior point method   e04ptc
Semidefinite programming (SDP),  
generalized augmented Lagrangian method for SDP and SDP with bilinear matrix inequalities (BMI-SDP)   e04svc
Nonlinear programming (NLP),  
dense,  
active-set sequential quadratic programming (SQP),  
direct communication,  
recommended (see Section 4.3 in the E04 Chapter Introduction)   e04ucc
alternative   e04wdc
reverse communication   e04ufc
sparse,  
active-set sequential quadratic programming (SQP)   e04src
interior point method (IPM)   e04stc
active-set sequential quadratic programming (SQP),  
alternative   e04vhc
alternative   e04ugc
Nonlinear programming (NLP) – derivative-free optimization (DFO),  
model-based method for bound-constrained optimization   e04jcc
model-based method for bound-constrained optimization,  
reverse communication   e04jec
direct communication   e04jdc
Nelder–Mead simplex method for unconstrained optimization   e04cbc
Nonlinear programming (NLP) – special cases,  
unidimensional optimization (one-dimensional) with bound constraints,  
method based on quadratic interpolation, no derivatives   e04abc
method based on cubic interpolation   e04bbc
unconstrained,  
preconditioned conjugate gradient method   e04dgc
bound-constrained,  
first order active-set method (nonlinear conjugate gradient)   e04kfc
quasi-Newton algorithm, first derivatives   e04kbc
modified Newton algorithm, first and second derivatives   e04lbc
Nonlinear programming (NLP) – global optimization,  
bound constrained,  
branching algorithm, multi-level coordinate search   e05kbc
branching algorithm, multi-level coordinate search (D)   e05jbc
heuristic algorithm, particle swarm optimization (PSO)   e05sac
generic, including nonlinearly constrained,  
heuristic algorithm, particle swarm optimization (PSO)   e05sbc
multi-start   e05ucc
Linear least squares, linear regression, data fitting,  
constrained,  
bound-constrained least squares problem   e04pcc
linearly-constrained active-set method   e04ncc
Data fitting,  
general loss functions (for sum of squares, see nonlinear least squares)   e04gnc
Nonlinear least squares, data fitting,  
unconstrained,  
combined Gauss–Newton and modified Newton algorithm,  
no derivatives   e04fcc
combined Gauss–Newton and quasi-Newton algorithm,  
first derivatives   e04gbc
covariance matrix for nonlinear least squares problem (unconstrained)   e04ycc
constrained,  
nonlinear constraints active-set sequential quadratic programming (SQP)   e04unc
bound constrained,  
model-based derivative-free algorithm,  
direct communication   e04ffc
reverse communication   e04fgc
trust region algorithm,  
first derivatives, optionally second derivatives   e04ggc
Nonlinear least squares, data fitting – global optimization,  
generic, including nonlinearly constrained,  
multi-start   e05usc
Mixed integer linear programming (MILP),  
dense,  
branch and bound method   h02bbc
large-scale,  
branch and bound method   h02bkc
Mixed integer nonlinear programming (MINLP),  
dense,  
mixed integer sequential quadratic programming (MISQP)   h02dac
Operations Research (OR),  
feature selection,  
best subset of given size,  
direct communication   h05abc
reverse communication   h05aac
transportation problem   h03abc
travelling salesman problem, simulated annealing   h03bbc