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Do gradient descent entirely in BigFloat

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The previos version accidentally returned steps in Float64.
This commit is contained in:
Aaron Fenyes 2024-07-02 12:35:12 -07:00
parent 242d630cc6
commit e7dde5800c
1 changed files with 2 additions and 2 deletions
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4
engine-proto/gram-test/descent-test.jl
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@ -11,7 +11,7 @@ using PolynomialRoots
# subspace of matrices whose entries vanish at each empty index of `target` # subspace of matrices whose entries vanish at each empty index of `target`
function proj_diff(target, attempt) function proj_diff(target, attempt)
J, K, values = findnz(target) J, K, values = findnz(target)
result = zeros(size(target)...) result = zeros(BigFloat, size(target)...)
for (j, k, val) in zip(J, K, values) for (j, k, val) in zip(J, K, values)
result[j, k] = val - attempt[j, k] result[j, k] = val - attempt[j, k]
end end
@ -65,7 +65,7 @@ guess = sqrt(0.5) * BigFloat[
steps = 600 steps = 600
line_search_max_steps = 100 line_search_max_steps = 100
init_stepsize = BigFloat(1) init_stepsize = BigFloat(1)
step_shrink_factor = BigFloat(0.5) step_shrink_factor = BigFloat(0.9)
target_improvement_factor = BigFloat(0.5) target_improvement_factor = BigFloat(0.5)
# complete the gram matrix using gradient descent # complete the gram matrix using gradient descent