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Constrained mixed order surrogate assisted optimization using GPs #75

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merged 4 commits into from
Jun 20, 2021

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In this PR, I implement a way to replace expensive functions with a GP and use first order optimization algorithms on the surrogate model. This is mixed order because the expensive function will be evaluated without its gradient.

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Codecov Report

Merging #75 (189bb66) into master (4957ee7) will increase coverage by 1.28%.
The diff coverage is 96.74%.

Impacted file tree graph

@@            Coverage Diff             @@
##           master      #75      +/-   ##
==========================================
+ Coverage   65.18%   66.46%   +1.28%     
==========================================
  Files          34       35       +1     
  Lines        2430     2529      +99     
==========================================
+ Hits         1584     1681      +97     
- Misses        846      848       +2     
Impacted Files Coverage Δ
src/Nonconvex.jl 100.00% <ø> (ø)
src/functions/functions.jl 69.64% <50.00%> (-2.06%) ⬇️
src/algorithms/bayesian.jl 100.00% <100.00%> (ø)
src/models/dict_model.jl 100.00% <100.00%> (ø)
src/models/model.jl 65.06% <100.00%> (+0.42%) ⬆️
src/models/vec_model.jl 69.04% <100.00%> (ø)
src/wrappers/ipopt.jl 84.21% <100.00%> (-0.12%) ⬇️
src/wrappers/juniper.jl 100.00% <100.00%> (ø)
src/wrappers/pavito.jl 100.00% <100.00%> (ø)

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@mohamed82008 mohamed82008 merged commit 107daca into master Jun 20, 2021
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2 participants