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Add Subgradients and Automated Function Writing (#7)
-- Subgradients are now included in SCMC, which enables this package to use gradient-based lower-bounding methods. -- An automated function generator (`fgen`) is included, which stitches together smaller SCMC functions to create relaxation-generating functions for arbitrarily complicated expressions. -- The ParBB algorithm is updated to make use of subgradients through a GPU-accelerated two-phase simplex method, which parallelizes individual simplex operations to apply simultaneously to multiple simplex tableaus. -- Updated documentation to reflect current SCMC operation.
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‎.github/workflows/ci.yml

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matrix:
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include:
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- version: '1.8'
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- version: '1.10' # LTS
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os: ubuntu-latest
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arch: x64
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- version: '1.8'
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- version: '1.10' # LTS
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os: windows-latest
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arch: x64
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- version: '1'

‎Project.toml

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name = "SourceCodeMcCormick"
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uuid = "a7283dc5-4ecf-47fb-a95b-1412723fc960"
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authors = ["Robert Gottlieb <[email protected]>"]
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version = "0.3.1"
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version = "0.4.0"
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[deps]
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CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
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DocStringExtensions = "0.8 - 0.9"
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Graphs = "1"
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IfElse = "0.1.0 - 0.1.1"
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SymbolicUtils = "1"
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Symbolics = "5"
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julia = "1.6"
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SymbolicUtils = "3"
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Symbolics = "6"
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julia = "1.10"
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[extras]
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McCormick = "53c679d3-6890-5091-8386-c291e8c8aaa1"

‎README.md

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‎examples/ParBB/extension.jl

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@@ -48,14 +48,300 @@ Base.@kwdef mutable struct PointwiseGPU <: ExtendGPU
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"Flag for stack prepopulation. Good if the total number
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of nodes throughout the solve is expected to be large (default = true)"
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prepopulate::Bool = true
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"Frequency of garbage collection (number of iterations)"
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gc_freq::Int = 300
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"(In development) Number of points to use for multistarting the NLP solver"
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multistart_points::Int = 1
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end
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function PointwiseGPU(convex_func, var_count::Int; alpha::Float64 = 0.01, node_limit::Int = 50000,
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prepopulate::Bool = true, multistart_points::Int = 1)
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prepopulate::Bool = true, gc_freq::Int = 300, multistart_points::Int = 1)
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return PointwiseGPU(convex_func, var_count, node_limit, alpha,
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Vector{Float64}(undef, node_limit), Vector{Float64}(undef, node_limit), Vector{NodeBB}(undef, node_limit), 0,
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Matrix{Float64}(undef, node_limit, var_count),
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Matrix{Float64}(undef, node_limit, var_count), prepopulate, gc_freq, multistart_points)
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end
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"""
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$(TYPEDEF)
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The SubgradGPU integrator is meant to be paired with the SourceCodeMcCormick
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package. SubgradGPU differs from PointwiseGPU in that SubgradGPU requires
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the `convex_func_and_subgrad` term to return both evaluations of the convex
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relaxation and evaluations of the subgradient of the convex relaxation.
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$(TYPEDFIELDS)
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"""
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Base.@kwdef mutable struct SubgradGPU <: ExtendGPU
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"A user-defined function taking argument `p` and returning a vector
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of convex evaluations of the objective function [outdated description, [cv, lo, subgrad]]"
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convex_func_and_subgrad
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"Number of decision variables"
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np::Int
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"The number of nodes to evaluate in parallel (default = 10000)"
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node_limit::Int64 = 50000
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"A parameter that changes how far spread out points are. Should be
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in the range (0.0, 1.0]"
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α::Float64 = 0.5
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"Lower bound storage to hold calculated lower bounds for multiple nodes."
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lower_bound_storage::Vector{Float64} = Vector{Float64}()
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"Upper bound storage to hold calculated upper bounds for multiple nodes."
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upper_bound_storage::Vector{Float64} = Vector{Float64}()
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"Node storage to hold individual nodes outside of the main stack"
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node_storage::Vector{NodeBB} = Vector{NodeBB}()
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"An internal tracker of nodes in internal storage"
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node_len::Int = 0
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"Variable lower bounds to evaluate"
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all_lvbs::Matrix{Float64} = Matrix{Float64}()
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"Variable upper bounds to evaluate"
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all_uvbs::Matrix{Float64} = Matrix{Float64}()
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"Internal tracker for the count in the main stack"
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# node_count::Int = 0
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"Flag for stack prepopulation. Good if the total number
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of nodes throughout the solve is expected to be large (default = true)"
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prepopulate::Bool = true
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"(In development) Number of points to use for multistarting the NLP solver"
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multistart_points::Int = 1
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end
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function SubgradGPU(convex_func_and_subgrad, var_count::Int; alpha::Float64 = 0.01, node_limit::Int = 50000,
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prepopulate::Bool = true, multistart_points::Int = 1)
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return SubgradGPU(convex_func_and_subgrad, var_count, node_limit, alpha,
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Vector{Float64}(undef, node_limit), Vector{Float64}(undef, node_limit), Vector{NodeBB}(undef, node_limit), 0,
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Matrix{Float64}(undef, node_limit, var_count),
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Matrix{Float64}(undef, node_limit, var_count), prepopulate, multistart_points)
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end
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"""
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$(TYPEDEF)
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The SimplexGPU integrator is meant to be paired with the SourceCodeMcCormick
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package. SimplexGPU differs from SubgradGPU in that SimplexGPU can handle
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inequality constraints, and that relaxations are made tighter by solving
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linear programs within the lower bounding routine to make better use of
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subgradient information. Like SubgradGPU, SimplexGPU requires the
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`convex_func_and_subgrad` term to return both evaluations of the convex
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relaxation and evaluations of the subgradient of the convex relaxation.
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$(TYPEDFIELDS)
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"""
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Base.@kwdef mutable struct SimplexGPU_OnlyObj <: ExtendGPU
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"A user-defined function taking argument `p` and returning a vector
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of convex evaluations of the objective function [outdated description, [cv, lo, subgrad]]"
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convex_func_and_subgrad
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"Number of decision variables"
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np::Int
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"The number of nodes to evaluate in parallel (default = 1024)"
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node_limit::Int64 = 1024
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"Lower bound storage to hold calculated lower bounds for multiple nodes."
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lower_bound_storage::Vector{Float64} = Vector{Float64}()
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"Upper bound storage to hold calculated upper bounds for multiple nodes."
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upper_bound_storage::Vector{Float64} = Vector{Float64}()
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"Node storage to hold individual nodes outside of the main stack"
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node_storage::Vector{NodeBB} = Vector{NodeBB}()
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"An internal tracker of nodes in internal storage"
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node_len::Int = 0
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"Variable lower bounds to evaluate"
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all_lvbs::Matrix{Float64} = Matrix{Float64}()
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"Variable upper bounds to evaluate"
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all_uvbs::Matrix{Float64} = Matrix{Float64}()
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"Flag for stack prepopulation. Good if the total number
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of nodes throughout the solve is expected to be large (default = true)"
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prepopulate::Bool = true
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"Total number of cuts to do on each node"
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max_cuts::Int = 3
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"Frequency of garbage collection (number of iterations)"
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gc_freq::Int = 15
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"(In development) Number of points to use for multistarting the NLP solver"
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multistart_points::Int = 1
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relax_time::Float64 = 0.0
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opt_time::Float64 = 0.0
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lower_counter::Int = 0
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node_counter::Int = 0
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end
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function SimplexGPU_OnlyObj(convex_func_and_subgrad, var_count::Int; node_limit::Int = 1024,
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prepopulate::Bool = true, max_cuts::Int = 3, gc_freq::Int = 15,
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multistart_points::Int = 1)
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return SimplexGPU_OnlyObj(convex_func_and_subgrad, var_count, node_limit,
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Vector{Float64}(undef, node_limit), Vector{Float64}(undef, node_limit), Vector{NodeBB}(undef, node_limit), 0,
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Matrix{Float64}(undef, node_limit, var_count),
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Matrix{Float64}(undef, node_limit, var_count), prepopulate, max_cuts, gc_freq, multistart_points, 0.0, 0.0, 0, 0)
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end
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"""
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$(TYPEDEF)
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The SimplexGPU_ObjAndCons structure is meant to handle optimization problems
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with nontrivial constraints as well as a potentially nonlinear objective
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function. Note that this struct requires the functions representing the
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objective function and constraints to mutate arguments, rather than return
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a tuple of results. SimplexGPU_ObjAndCons is not designed to handle mixed-integer
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problems; NLPs only.
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$(TYPEDFIELDS)
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"""
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Base.@kwdef mutable struct SimplexGPU_ObjAndCons <: ExtendGPU
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"A SCMC-generated or user-defined function taking arguments [cv, lo, [cv_subgrad]..., p...],
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which modifies `cv` to hold the convex relaxation of the objective function, `lo` to hold
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the lower bound of the inclusion monotonic interval extension of the objective function,
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and n instances of `cv_subgrad` that will hold the n subgradients of the convex relaxation
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of the objective function (where n is the dimensionality of the problem), all evaluated at
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points `p`"
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obj_fun
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"A vector of SCMC-generated or user-defined functions, each with the same form as `obj_fun`,
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but with arguments [cv, [cv_subgrad]..., p...], representing all of the LEQ inequality constraints"
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leq_cons
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"A vector of SCMC-generated or user-defined functions, taking arguments [cc, [cc_subgrad]..., p...],
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defined similarly to the objective function and GEQ constraints, representing all of the
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GEQ inequality constraints"
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geq_cons
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"A vector of SCMC-generated or user-defined functions, taking arguments
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[cv, cc, [cv_subgrad]..., [cc_subgrad]..., p...], with terms defined similarly to
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the objective function and inequality constraints, representing all of the equality constraints"
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eq_cons
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"Number of decision variables"
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np::Int
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"The number of nodes to evaluate in parallel (default = 1024)"
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node_limit::Int64 = 1024
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"Lower bound storage to hold calculated lower bounds for multiple nodes."
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lower_bound_storage::Vector{Float64} = Vector{Float64}()
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"Upper bound storage to hold calculated upper bounds for multiple nodes."
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upper_bound_storage::Vector{Float64} = Vector{Float64}()
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"Node storage to hold individual nodes outside of the main stack"
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node_storage::Vector{NodeBB} = Vector{NodeBB}()
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"An internal tracker of nodes in internal storage"
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node_len::Int = 0
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"Variable lower bounds to evaluate"
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all_lvbs::Matrix{Float64} = Matrix{Float64}()
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"Variable upper bounds to evaluate"
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all_uvbs::Matrix{Float64} = Matrix{Float64}()
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"Flag for stack prepopulation. Good if the total number
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of nodes throughout the solve is expected to be large (default = true)"
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prepopulate::Bool = true
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"Total number of cuts to do on each node"
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max_cuts::Int = 3
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"Frequency of garbage collection (number of iterations)"
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gc_freq::Int = 15
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"(In development) Number of points to use for multistarting the NLP solver"
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multistart_points::Int = 1
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relax_time::Float64 = 0.0
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opt_time::Float64 = 0.0
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lower_counter::Int = 0
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node_counter::Int = 0
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end
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function SimplexGPU_ObjAndCons(obj_fun, var_count::Int; geq_cons=[], leq_cons=[], eq_cons=[], node_limit::Int = 1024,
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prepopulate::Bool = true, max_cuts::Int = 3, gc_freq::Int = 15, multistart_points::Int = 1)
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return SimplexGPU_ObjAndCons(obj_fun, leq_cons, geq_cons, eq_cons, var_count, node_limit,
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Vector{Float64}(undef, node_limit), Vector{Float64}(undef, node_limit), Vector{NodeBB}(undef, node_limit), 0,
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Matrix{Float64}(undef, node_limit, var_count),
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Matrix{Float64}(undef, node_limit, var_count), prepopulate, max_cuts, gc_freq, multistart_points, 0.0, 0.0, 0, 0)
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end
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Base.@kwdef mutable struct SimplexGPU_ObjOnly_Mat <: ExtendGPU
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"A SCMC-generated or user-defined function taking arguments [cv, lo, [cv_subgrad]..., p...],
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which modifies `cv` to hold the convex relaxation of the objective function, `lo` to hold
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the lower bound of the inclusion monotonic interval extension of the objective function,
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and n instances of `cv_subgrad` that will hold the n subgradients of the convex relaxation
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of the objective function (where n is the dimensionality of the problem), all evaluated at
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points `p`"
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obj_fun
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"Number of decision variables"
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np::Int
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"The number of nodes to evaluate in parallel (default = 1024)"
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node_limit::Int64 = 1024
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"Lower bound storage to hold calculated lower bounds for multiple nodes."
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lower_bound_storage::Vector{Float64} = Vector{Float64}()
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"Upper bound storage to hold calculated upper bounds for multiple nodes."
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upper_bound_storage::Vector{Float64} = Vector{Float64}()
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"Node storage to hold individual nodes outside of the main stack"
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node_storage::Vector{NodeBB} = Vector{NodeBB}()
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"An internal tracker of nodes in internal storage"
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node_len::Int = 0
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"Variable lower bounds to evaluate"
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all_lvbs::Matrix{Float64} = Matrix{Float64}()
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"Variable upper bounds to evaluate"
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all_uvbs::Matrix{Float64} = Matrix{Float64}()
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"Flag for stack prepopulation. Good if the total number
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of nodes throughout the solve is expected to be large (default = true)"
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prepopulate::Bool = true
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"Total number of cuts to do on each node"
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max_cuts::Int = 3
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"Frequency of garbage collection (number of iterations)"
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gc_freq::Int = 15
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"(In development) Number of points to use for multistarting the NLP solver"
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multistart_points::Int = 1
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relax_time::Float64 = 0.0
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opt_time::Float64 = 0.0
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lower_counter::Int = 0
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node_counter::Int = 0
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end
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function SimplexGPU_ObjOnly_Mat(obj_fun, var_count::Int; node_limit::Int = 1024,
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prepopulate::Bool = true, max_cuts::Int = 3, gc_freq::Int = 15, multistart_points::Int = 1)
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return SimplexGPU_ObjOnly_Mat(obj_fun, var_count, node_limit,
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Vector{Float64}(undef, node_limit), Vector{Float64}(undef, node_limit), Vector{NodeBB}(undef, node_limit), 0,
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Matrix{Float64}(undef, node_limit, var_count),
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Matrix{Float64}(undef, node_limit, var_count), prepopulate, max_cuts, gc_freq, multistart_points, 0.0, 0.0, 0 ,0)
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end
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"""
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$(TYPEDEF)
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This is a testing method/struct, to see if we can check fewer points per node
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when we construct the LPs and still get all the same benefits. The normal
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SimplexGPU method uses 2n+1 points, where n is the problem dimensionality.
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This method only uses a single point in the center of the node, and can
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therefore get away with more simultaneous LPs, since each one is significantly
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smaller.
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$(TYPEDFIELDS)
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"""
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Base.@kwdef mutable struct SimplexGPU_Single <: ExtendGPU
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"A user-defined function taking argument `p` and returning a vector
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of convex evaluations of the objective function [outdated description, [cv, lo, subgrad]]"
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convex_func_and_subgrad
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"Number of decision variables"
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np::Int
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"The number of nodes to evaluate in parallel (default = 2500)"
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node_limit::Int64 = 2500
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"A parameter that changes how far spread out points are. Should be
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in the range (0.0, 1.0]"
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α::Float64 = 0.5
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"Lower bound storage to hold calculated lower bounds for multiple nodes."
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lower_bound_storage::Vector{Float64} = Vector{Float64}()
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"Upper bound storage to hold calculated upper bounds for multiple nodes."
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upper_bound_storage::Vector{Float64} = Vector{Float64}()
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"Node storage to hold individual nodes outside of the main stack"
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node_storage::Vector{NodeBB} = Vector{NodeBB}()
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"An internal tracker of nodes in internal storage"
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node_len::Int = 0
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"Variable lower bounds to evaluate"
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all_lvbs::Matrix{Float64} = Matrix{Float64}()
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"Variable upper bounds to evaluate"
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all_uvbs::Matrix{Float64} = Matrix{Float64}()
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"Flag for stack prepopulation. Good if the total number
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of nodes throughout the solve is expected to be large (default = true)"
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prepopulate::Bool = true
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"Total number of cuts to do on each node"
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max_cuts::Int = 3
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"(In development) Number of points to use for multistarting the NLP solver"
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multistart_points::Int = 1
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end
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function SimplexGPU_Single(convex_func_and_subgrad, var_count::Int; alpha::Float64 = 0.01, node_limit::Int = 2500,
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prepopulate::Bool = true, max_cuts::Int = 3, multistart_points::Int = 1)
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return SimplexGPU_Single(convex_func_and_subgrad, var_count, node_limit, alpha,
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Vector{Float64}(undef, node_limit), Vector{Float64}(undef, node_limit), Vector{NodeBB}(undef, node_limit), 0,
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Matrix{Float64}(undef, node_limit, var_count),
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Matrix{Float64}(undef, node_limit, var_count), prepopulate, max_cuts, multistart_points)
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end

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