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matbesancon / MathOptSetDistances.jl

Distances to sets for MathOptInterface
MIT License
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Exponential Cone #29

Closed tjdiamandis closed 3 years ago

tjdiamandis commented 3 years ago

Projection onto exponential cone and its derivative.

The function _in_exp_cone(v::AbstractVector{T}; dual=false) may be necessary and incorporated into the distance function. However, in the projection I allow some tolerance for the points close to the boundary.

codecov[bot] commented 3 years ago

Codecov Report

Merging #29 (edad4d4) into master (3af4edb) will increase coverage by 1.10%. The diff coverage is 93.33%.

Impacted file tree graph

@@            Coverage Diff             @@
##           master      #29      +/-   ##
==========================================
+ Coverage   88.16%   89.26%   +1.10%     
==========================================
  Files           5        6       +1     
  Lines         355      438      +83     
==========================================
+ Hits          313      391      +78     
- Misses         42       47       +5
Impacted Files Coverage Δ
src/MathOptSetDistances.jl 100.00% <ø> (ø)
src/distance_sets.jl 72.25% <50.00%> (-0.59%) :arrow_down:
src/utils.jl 94.11% <94.11%> (ø)
src/projections.jl 98.78% <96.29%> (-1.22%) :arrow_down:
src/chainrules.jl 100.00% <0.00%> (ø)

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matbesancon commented 3 years ago

A major drawback is the addition of a dependency here on NonlinearSolve. Ideally this package will be used by others requiring set distances and projections so we should avoid bringing in a lot of direct and transitive dependencies)

tjdiamandis commented 3 years ago

Julia 1.0 test failing due to a new version of diagm being used. Is the "correct" way to construct a diagonal matrix from a vector Diagonal(v)?

matbesancon commented 3 years ago

Yes Diagonal(v) works. Usually it should be preferred when you will not add non-diagonal entrie to the matrix afterwards, because it creates a special matrix type:

julia> diagm([1,2])
2×2 Array{Int64,2}:
 1  0
 0  2

julia> Diagonal([1,2])
2×2 Diagonal{Int64,Array{Int64,1}}:
 1  ⋅
 ⋅  2
tjdiamandis commented 3 years ago

Sorry for the type return errors. Thanks so much for the thorough review! I understand the general design principle now.

matbesancon commented 3 years ago

no problem, thanks a lot for the work. This is also me failing to catch everything at once, hence the back and forth which can feel tedious.

For diagm, the issue is that Julia 1.0 did not have the default diagm(v) = diagm(0 => v) yet, with 0 the convention to indicate the main diagonal of the matrix

matbesancon commented 3 years ago

Tests look green, we'll let the PR there for a day if other folks want to take a look or add something, but looks good on my side!

matbesancon commented 3 years ago

Thanks again for the PR!