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BlockNLPAlgorithms.jl

This package implements several popular decomposition algorithms for block-structured nonlinear optimization problems (NLPs). It requires the NLPs to be implemented as AbstractBlockNLPModels; for more details on modeling block-structured NLPs, please refer to the documentation of BlockNLPModels.jl.

Available Decomposition Algorithms

  1. Dual Decomposition Algorithm
  2. Alternating Direction Method of Multipliers or ADMM
  3. Proximal ADMM

Installation

This package is currently under active development and has not been registered yet. However, to access the current code, one can enter the following command in Julia REPL:

]add "https://github.com/exanauts/BlockNLPAlgorithms.jl"

To confirm if the package was installed correctly, please run:

test BlockNLPAlgorithms

The test code generates and solves a small instance of a BlockNLPModel with both dual decomposition and ADMM, using MadNLP.jl to solve the subproblems.

Quickstart Guide

To solve a AbstractBlockNLPModel with a decomposition algorithms implemented in this package, one can use the following method:

solve(model::AbstractBlockNLPModel,method::Type{<:AbstractBlockNLPSolver};options...), where

  • model is the block NLP
  • method refers to the choice of the decomposition algorithms, user can choose one of the three available options:
    • BlockNLPAlgorithms.DualDecomposition
    • BlockNLPAlgorithms.ADMM
    • BlockNLPAlgorithms.ProxADMM
  • options are the optional arguments to customize the behavior of the decomposition solver. Available solver options are listed in the "Solver Options" subsection.

Instructions for interfacing a subproblem solver

This package allows the user to provide their own subproblem solver. As an illustrative example, we show the steps to interface this package with a projected gradient descent based solver in MySolver.jl file in the test directory.

Solver options

verbosity::Int: This options sets the verbosity level of solver's output. Valid range is [0,2] with the default level being set to 1.

primal_start::Vector{Float64}: Sets the initial guess values for the primal variable values. This gets initialized as a vector of zeros if not provided by the user.

dual_start::Vector{Float64}: Sets the initial guess values for the dual variable values. This gets initialized as a vector of zeros if not provided by the user.

dynamic_step_size::Bool: Determines whether the step size (or the penalty parameter) values is fixed or dynamic. This is set to false by default.

step_size_min::Float64: Determines the minimum and initial value of the step size (or the penalty parameter) used while solving the BlockNLPModel. In case dynamic_step_size is set to false, step size value remains fixed at this value.

step_size_max::Float64: Determines the maximum value of the step size (or the penalty parameter) used while solving the BlockNLPModel. This option does not do anything if dynamic_step_size is set to false.

damping_param::Float64: This option can be used to make the dual-update step size value different from the penalty parameter value in case of algorithms such as ADMM. More specifically, this option is implemented such that step-size = damping_param * penalty parameter. Default value is set to 1.

update_scheme::Symbol: Helps choose the primal update scheme during an iteration of the decomposition algorithm. User can choose between :GAUSS_SEIDEL (default) and :JACOBI update schemes.

subproblem_solver::AbstractBlockSolver: This option determines the solver used to solve the NLP subproblems obtained after decomposition. By default, subproblems are solved using MadNLP.jl.

proximal_penalty::Union{AbstractArray,UniformScaling}: This option can be used to set the weights of the proximal terms in case of the proximal ADMM algorithm. By default, the weights are set as 1e-3 .* I, where I is the identity matrix.

pr_feas_tol::Float64: Termination tolerance for the primal feasibility criterion. The default value is 1e-4.

du_feas_tol::Float64: Termination tolerance for the dual feasibility criterion. The default value is 1e-4.

max_iter::Int: Maximum number of algorithm iterations. Solver terminates with exit status :Maximum_Iterations_Exceeded if primal and dual feasibility termination criteria tolerances are not met and decomposition algorithm's iteration count exceeds max_iter. The default value for this option is 100.

max_wall_time::Float64: Maximum wall time (in seconds) for the decomposition algorithm. Solver terminates with exit status :Maximum_WallTime_Exceeded if primal and dual feasibility termination criteria tolerances are not met and decomposition algorithm's iteration count exceeds max_wall_time. The default value for this option is 600 s.

Acknowledgements

This package's development was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy’s Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation’s exascale computing imperative.