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H (Mip)
Operations Research

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H (Mip) Chapter Introduction – A description of the Chapter and an overview of the algorithms available.

Mark of

h02bff 16 nagf_mip_ilp_mpsx
Interpret MPSX data file defining IP or LP problem, optimize and print solution
h02bkf 29.3 (Experimental) nagf_mip_handle_solve_milp
Mixed integer linear programming (MILP), large-scale, branch and bound method
h02buf 16 nagf_mip_ilp_mpsx_convert
Convert MPSX data file defining IP or LP problem to format required by h02bbf or e04mff/​e04mfa
h02bvf 16 nagf_mip_ilp_print
Print IP or LP solutions with user-specified names for rows and columns
h02cbf 19 nagf_mip_iqp_dense
Integer QP problem (dense)
h02ccf 19 nagf_mip_iqp_dense_optfile
Read optional parameter values for h02cbf from external file
h02cdf 19 nagf_mip_iqp_dense_optstr
Supply optional parameter values to h02cbf
h02cef 19 nagf_mip_iqp_sparse
Integer LP or QP problem (sparse), using e04nkf/​e04nka
h02cff 19 nagf_mip_iqp_sparse_optfile
Read optional parameter values for h02cef from external file
h02cgf 19 nagf_mip_iqp_sparse_optstr
Supply optional parameter values to h02cef
h02daf 25 nagf_mip_sqp
Mixed integer nonlinear programming
h02zkf 25 nagf_mip_optset
Option setting routine for h02daf
h02zlf 25 nagf_mip_optget
Option getting routine for h02daf
h03abf 4 nagf_mip_transportation
Transportation problem, modified ‘stepping stone’ method
h03adf 18 nagf_mip_shortestpath
Shortest path problem, Dijkstra's algorithm
h03bbf 25 nagf_mip_tsp_simann
Travelling Salesman Problem, simulated annealing
h05aaf 24 nagf_mip_best_subset_given_size_revcomm
Best n subsets of size p (reverse communication)
h05abf 24 nagf_mip_best_subset_given_size
Best n subsets of size p (direct communication)
h02bbf 14
Integer LP problem (dense)
h02bzf 15
Integer programming solution, extracts further information on solution obtained by h02bbf