Merge pull request #414 from JuliaOpt/removenlp

[DNMY] remove NLP algorithm (now in Pavito.jl)

REQUIRE

@@ -1,5 +1,4 @@
 julia 0.6
 MathProgBase 0.5 0.8
 JuMP 0.15
-ConicNonlinearBridge
 ConicBenchmarkUtilities

examples/cardls.jl

@@ -70,8 +70,6 @@ mip_solver = CplexSolver(
 )
 
 
-# Specify mixed-integer conic solver (Pajarito conic algorithm)
-
 # using SCS
 # conic_solver = SCSSolver(eps=1e-6, max_iters=1000000, verbose=0)
 
@@ -87,20 +85,6 @@ micp_solver = PajaritoSolver(
 )
 
 
-# Specify mixed-integer NLP solver (Pajarito nonlinear algorithm)
-
-using Ipopt
-nlp_solver = IpoptSolver(print_level=0)
-
-minlp_solver = PajaritoSolver(
-    mip_solver_drives=mip_solver_drives,
-    log_level=1,
-    rel_gap=rel_gap,
-	mip_solver=mip_solver,
-	cont_solver=nlp_solver,
-)
-
-
 #=========================================================
 Specify/generate data
 =========================================================#
@@ -125,11 +109,8 @@ xB = 4  # Bound on absolute values of estimate variables (|x_j| <= xB)
 Solve JuMP models
 =========================================================#
 
-println("\n\n****MIQP model with MINLP solver****\n")
-miqp_cardls(m, d, A, b, k, rho, xB, minlp_solver)
-
-println("\n\n****MIQP model with conic solver****\n")
+println("\n\n****MIQP model****\n")
 miqp_cardls(m, d, A, b, k, rho, xB, micp_solver)
 
-println("\n\n****MISDP model with conic solver****\n")
+println("\n\n****MISDP model****\n")
 misdp_cardls(m, d, A, b, k, rho, micp_solver)

examples/runcbf.jl

@@ -26,7 +26,7 @@ solver = PajaritoSolver(
     mip_solver=mip_solver,
     cont_solver=cont_solver,
     log_level=3,
-    )
+)
 
 dat = readcbfdata(ARGS[1])
 (c, A, b, con_cones, var_cones, vartypes, sense, objoffset) = cbftompb(dat)

src/Pajarito.jl

@@ -16,10 +16,8 @@ __precompile__()
 
 module Pajarito
     import MathProgBase
-    using ConicNonlinearBridge
 
     include("conic_dual_solver.jl")
     include("solver.jl")
     include("conic_algorithm.jl")
-    include("nonlinear_algorithm.jl")
 end

src/conic_algorithm.jl

@@ -11,8 +11,6 @@ This mixed-integer conic programming algorithm is described in:
   (available online at http://arxiv.org/abs/1511.06710)
 Model MICP with JuMP.jl conic format or Convex.jl DCP format
 http://mathprogbasejl.readthedocs.org/en/latest/conic.html
-TODO features
-- implement warm-starting: use set_best_soln!
 =========================================================#
 
 using JuMP
@@ -46,38 +44,38 @@ type PajaritoConicModel <: MathProgBase.AbstractConicModel
     mip_solver_drives::Bool     # Let MIP solver manage convergence ("branch and cut")
     mip_solver::MathProgBase.AbstractMathProgSolver # MIP solver (MILP or MISOCP)
     mip_subopt_solver::MathProgBase.AbstractMathProgSolver # MIP solver for suboptimal solves (with appropriate options already passed)
-    mip_subopt_count::Int       # (Conic only) Number of times to use `mip_subopt_solver` between `mip_solver` solves
-    round_mip_sols::Bool        # (Conic only) Round integer variable values before solving subproblems
-    use_mip_starts::Bool        # (Conic only) Use conic subproblem feasible solutions as MIP warm-starts or heuristic solutions
-
-    cont_solver::MathProgBase.AbstractMathProgSolver # Continuous solver (conic or nonlinear)
-    solve_relax::Bool           # (Conic only) Solve the continuous conic relaxation to add initial subproblem cuts
-    solve_subp::Bool            # (Conic only) Solve the continuous conic subproblems to add subproblem cuts
-    dualize_relax::Bool         # (Conic only) Solve the conic dual of the continuous conic relaxation
-    dualize_subp::Bool          # (Conic only) Solve the conic duals of the continuous conic subproblems
-
-    all_disagg::Bool            # (Conic only) Disaggregate cuts on the nonpolyhedral cones
-    soc_disagg::Bool            # (Conic only) Disaggregate SOC using extended formulation
-    soc_abslift::Bool           # (Conic only) Use SOC absolute value lifting
-    soc_in_mip::Bool            # (Conic only) Use SOC cones in the MIP model (if `mip_solver` supports MISOCP)
-    sdp_eig::Bool               # (Conic only) Use PSD cone eigenvector cuts
-    sdp_soc::Bool               # (Conic only) Use PSD cone eigenvector SOC cuts (if `mip_solver` supports MISOCP)
-    init_soc_one::Bool          # (Conic only) Use SOC initial L_1 cuts
-    init_soc_inf::Bool          # (Conic only) Use SOC initial L_inf cuts
-    init_exp::Bool              # (Conic only) Use Exp initial cuts
-    init_sdp_lin::Bool          # (Conic only) Use PSD cone initial linear cuts
-    init_sdp_soc::Bool          # (Conic only) Use PSD cone initial SOC cuts (if `mip_solver` supports MISOCP)
-
-    scale_subp_cuts::Bool       # (Conic only) Use scaling for subproblem cuts
-    scale_subp_factor::Float64  # (Conic only) Fixed multiplicative factor for scaled subproblem cuts
-    scale_subp_up::Bool         # (Conic only) Scale up any scaled subproblem cuts that are smaller than the equivalent separation cut
-    viol_cuts_only::Bool        # (Conic only) Only add cuts violated by current MIP solution
-    sep_cuts_only::Bool         # (Conic only) Add primal cuts, do not add subproblem cuts
-    sep_cuts_always::Bool       # (Conic only) Add primal cuts and subproblem cuts
-    sep_cuts_assist::Bool       # (Conic only) Add subproblem cuts, and add primal cuts only subproblem cuts cannot be added
-
-    cut_zero_tol::Float64       # (Conic only) Zero tolerance for cut coefficients
-    mip_feas_tol::Float64       # (Conic only) Absolute feasibility tolerance used for primal cuts (set equal to feasibility tolerance of `mip_solver`)
+    mip_subopt_count::Int       # Number of times to use `mip_subopt_solver` between `mip_solver` solves
+    round_mip_sols::Bool        # Round integer variable values before solving subproblems
+    use_mip_starts::Bool        # Use conic subproblem feasible solutions as MIP warm-starts or heuristic solutions
+
+    cont_solver::MathProgBase.AbstractMathProgSolver # Continuous conic solver
+    solve_relax::Bool           # Solve the continuous conic relaxation to add initial subproblem cuts
+    solve_subp::Bool            # Solve the continuous conic subproblems to add subproblem cuts
+    dualize_relax::Bool         # Solve the conic dual of the continuous conic relaxation
+    dualize_subp::Bool          # Solve the conic duals of the continuous conic subproblems
+
+    all_disagg::Bool            # Disaggregate cuts on the nonpolyhedral cones
+    soc_disagg::Bool            # Disaggregate SOC using extended formulation
+    soc_abslift::Bool           # Use SOC absolute value lifting
+    soc_in_mip::Bool            # Use SOC cones in the MIP model (if `mip_solver` supports MISOCP)
+    sdp_eig::Bool               # Use PSD cone eigenvector cuts
+    sdp_soc::Bool               # Use PSD cone eigenvector SOC cuts (if `mip_solver` supports MISOCP)
+    init_soc_one::Bool          # Use SOC initial L_1 cuts
+    init_soc_inf::Bool          # Use SOC initial L_inf cuts
+    init_exp::Bool              # Use Exp initial cuts
+    init_sdp_lin::Bool          # Use PSD cone initial linear cuts
+    init_sdp_soc::Bool          # Use PSD cone initial SOC cuts (if `mip_solver` supports MISOCP)
+
+    scale_subp_cuts::Bool       # Use scaling for subproblem cuts
+    scale_subp_factor::Float64  # Fixed multiplicative factor for scaled subproblem cuts
+    scale_subp_up::Bool         # Scale up any scaled subproblem cuts that are smaller than the equivalent separation cut
+    viol_cuts_only::Bool        # Only add cuts violated by current MIP solution
+    sep_cuts_only::Bool         # Add primal cuts, do not add subproblem cuts
+    sep_cuts_always::Bool       # Add primal cuts and subproblem cuts
+    sep_cuts_assist::Bool       # Add subproblem cuts, and add primal cuts only subproblem cuts cannot be added
+
+    cut_zero_tol::Float64       # Zero tolerance for cut coefficients
+    mip_feas_tol::Float64       # Absolute feasibility tolerance used for primal cuts (set equal to feasibility tolerance of `mip_solver`)
 
     dump_subproblems::Bool      # Save each conic subproblem in conic benchmark format (CBF) at a specified location
     dump_basename::String       # Basename of conic subproblem CBF files: "/path/to/foo" creates files "/path/to/foo_NN.cbf" where "NN" is a counter