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https://github.com/kibaekkim/dspopt.jl
Julia modeling interface to parallel decomposition solver DSP
https://github.com/kibaekkim/dspopt.jl
julia modeling optimization stochastic-programming structured-programming
Last synced: about 1 month ago
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Julia modeling interface to parallel decomposition solver DSP
- Host: GitHub
- URL: https://github.com/kibaekkim/dspopt.jl
- Owner: kibaekkim
- License: mit
- Created: 2020-06-30T16:47:37.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-09-02T17:09:29.000Z (over 2 years ago)
- Last Synced: 2024-11-01T17:42:14.244Z (about 2 months ago)
- Topics: julia, modeling, optimization, stochastic-programming, structured-programming
- Language: Julia
- Homepage:
- Size: 234 KB
- Stars: 12
- Watchers: 3
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DSPopt.jl
[](https://codecov.io/gh/kibaekkim/DSPopt.jl)
DSPopt.jl is an interface to a parallel decomposition mixed-integer programming solver [DSP](https://github.com/Argonne-National-Laboratory/DSP).
This package allows users to define block structures in optimization model written in [StructJuMP](https://github.com/StructJuMP/StructJuMP.jl)
and solve the block-structured problem using the parallle solver ``DSP``.
This package can model and solve (by interfacing `DSP`) the following optimization problems:
- Two-stage stochastic (mixed-integer) quadratic constrained programming (TSSP)
- The Wasserstein-based distributionally robust variant of TSSP
- Block-structured (mixed-integer) linear programming
## Intallation
> **_NOTE:_** You need to install solver [DSP](https://github.com/Argonne-National-Laboratory/DSP) first. This package provides an interface only.
```julia
] add DSPopt
```
## Examples
This is one simple example of `stochastic` form.
Please find more examples in `./examples` particularly for `block` form.
```julia
using MPI
using StructJuMP
using DSPopt
# Comment out this line if you want to run in serial
MPI.Init()
# Initialize DSPopt.jl with the communicator.
DSPopt.parallelize(MPI.COMM_WORLD)
xi = [[7,7] [11,11] [13,13]]
# create StructuredModel with number of scenarios
m = StructuredModel(num_scenarios = 3)
@variable(m, 0 <= x[i=1:2] <= 5, Int)
@objective(m, Min, -1.5 * x[1] - 4 * x[2])
for s = 1:3
# create a StructuredModel linked to m with id s and probability 1/3
blk = StructuredModel(parent = m, id = s, prob = 1/3)
@variable(blk, y[j=1:4], Bin)
@objective(blk, Min, -16 * y[1] + 19 * y[2] + 23 * y[3] + 28 * y[4])
@constraint(blk, 2 * y[1] + 3 * y[2] + 4 * y[3] + 5 * y[4] <= xi[1,s] - x[1])
@constraint(blk, 6 * y[1] + y[2] + 3 * y[3] + 2 * y[4] <= xi[2,s] - x[2])
# Quadratic constraints supported from DSP version 2.0.0 or higher
@constraint(blk, const_quad, w[1]^2 <= 1600)
if s == 1
@constraint(blk, const_quad2, 2 * w[1]^2 + 2 * w[2]^2 <= 400)
elseif s == 2
@constraint(blk, const_quad2, 5 * w[1] - w[2]^2 -2 * w[3]^2 >= -100)
end
end
# The Wasserstein ambiguity set of order-2 can be imposed with the size limit of 1.0.
DSPopt.set(WassersteinSet, 2, 1.0)
status = optimize!(m,
is_stochastic = true, # Needs to indicate that the model is a stochastic program.
solve_type = DSPopt.DW, # see instances(DSPopt.Methods) for other methods
)
# Comment out this line if you want to run in serial
MPI.Finalize()
```
## Acknowledgements
This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357.