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Parametron

Efficiently solving parameterized families of optimization problems in Julia

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Parametron

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Parametron makes it easy to set up and efficiently (ideally, with zero allocation) solve instances of a parameterized family of optimization problems.

Example 1

As an example, we'll use the OSQP solver to solve the following quadratic program:

Minimize ||A x - b||^2
subject to C x = d

with decision variable vector x, and where A, b, C, and d are parameters with random values, to be re-sampled each time the problem is re-solved.

Here's the basic problem setup:

# create a MathOptInterface optimizer instance
using OSQP
optimizer = OSQP.Optimizer()

# create a Parametron.Model, which holds problem information
using Parametron
using Random, LinearAlgebra
model = Model(optimizer)

# create decision variables and parameters
n = 8; m = 2
x = [Variable(model) for _ = 1 : n]
A = Parameter(rand!, zeros(n, n), model)
b = Parameter(rand!, zeros(n), model)
C = Parameter(rand!, zeros(m, n), model)
d = Parameter(zeros(m), model) do d
    # do syntax makes it easy to create custom Parameters
    rand!(d)
    d .*= 2
end

# the @expression macro can be used to create 'lazy' expressions,
# which can be used in constraints or the objective function, and
# can be evaluated at a later time, automatically updating the
# Parameters in the process (if needed).
residual = @expression A * x - b

# set the objective function
@objective(model, Minimize, residual ⋅ residual)

# add the constraints. You could have multiple @constraint calls
# as well. ==, <=, and >= are supported.
@constraint(model, C * x == d)

Now that the problem is set up, we can solve and obtain the solution as follows:

julia> solve!(model)
-----------------------------------------------------------------
           OSQP v0.3.0  -  Operator Splitting QP Solver
              (c) Bartolomeo Stellato,  Goran Banjac
        University of Oxford  -  Stanford University 2017
-----------------------------------------------------------------
problem:  variables n = 8, constraints m = 2
          nnz(P) + nnz(A) = 88
settings: linear system solver = suitesparse ldl,
          eps_abs = 1.0e-03, eps_rel = 1.0e-03,
          eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,
          rho = 1.00e-01 (adaptive),
          sigma = 1.00e-06, alpha = 1.60, max_iter = 4000
          check_termination: on (interval 25),
          scaling: on, scaled_termination: off
          warm start: on, polish: off

iter   objective    pri res    dua res    rho        time
   1  -7.8949e-01   9.57e-01   1.02e+03   1.00e-01   1.34e-04s
  25  -2.0032e+00   2.87e-04   4.82e-03   1.00e-01   1.76e-04s

status:               solved
number of iterations: 25
optimal objective:    -2.0032
run time:             1.81e-04s
optimal rho estimate: 5.16e-02

julia> value.(model, x)
8-element Array{Float64,1}:
 -0.365181
 -0.119036
 -0.267222
  1.41655
  0.69472
  0.993475
 -0.631194
 -1.02733

Note that the next time solve! is called, the update functions of our parameters (A, b, C, and d) will be called again (once for each parameter), resulting in a different optimum:

julia> solve!(model)
iter   objective    pri res    dua res    rho        time
   1  -1.4419e+00   2.57e-01   5.79e+02   1.00e-01   1.53e-05s
  25  -3.2498e+00   1.34e-04   2.74e-03   1.00e-01   3.10e-05s

status:               solved
number of iterations: 25
optimal objective:    -3.2498
run time:             3.63e-05s
optimal rho estimate: 7.79e-02

julia> value.(model, x)
8-element Array{Float64,1}:
  0.220836
 -0.462071
  0.509146
  0.667908
 -0.850638
  0.7877
  1.01581
 -0.992135

Note that the solver is warm-started. Also note that updating the parameters and solving a new QP instance is quite fast:

julia> using MathOptInterface; using OSQP.MathOptInterfaceOSQP: OSQPSettings; MathOptInterface.set(optimizer, OSQPSettings.Verbose(), false) # silence the optimizer

julia> using BenchmarkTools

julia> @btime solve!($model)
  51.863 μs (0 allocations: 0 bytes)

The performance and lack of allocations stems from the fact that the 'lazy expressions' used for the constraints and objective function are automatically optimized to calls to in-place functions.

Example 2

Of course, in many real-world problems you are unlikely to update your parameters with random values. Here's an illustration showing how you might control these values more directly, fitting a vector g in a model

g' * X[:,i] ≈ p[i]

for a set of vectors in columns of X.

This example also demonstrates a different style of updating parameters. Whereas in the previous example we supplied an 'update function' (e.g., rand!) that is automatically called when solve! is called, in this example we use the syntax

Parameter(model, val=some_manually_updated_mutable_object)

to create a Parameter whose value may be updated manually between calls to the solve! function.

using Parametron, OSQP.MathOptInterfaceOSQP
using Random

n, m = 5, 15
Xdata = randn(n, m)
pdata = Vector{Float64}(undef, m);
model = Model(OSQP.Optimizer())
X = Parameter(model, val=Xdata)
p = Parameter(model, val=pdata)
g = [Variable(model) for _ = 1:n]
resid = @expression X'*g - p
@objective(model, Minimize, resid'*resid)

# Try with a specific ground-truth `g`
ggt = randn(n)
pdata .= Xdata'*ggt  # set the values in-place using `.=`
solve!(model)

julia> value.(model, g)
5-element Array{Float64,1}:
  0.6710700783457044
  1.3999896266657308
  0.5666247642146109
 -1.018123491138979
 -0.7464853233374451

julia> ggt
5-element Array{Float64,1}:
  0.671068170731507
  1.399985646860983
  0.5666231534233734
 -1.0181205969900424
 -0.7464832010803155

You can re-fit the model after updating pdata and/or Xdata in-place.

First Commit

04/23/2018

Last Touched

8 days ago

Commits

207 commits

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