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Hi, and thanks for this great crate! I got a panic at minilp-0.2.2/src/solver.rs:1301:10:
self.lu_factors = lu_factorize(
basic_vars.len(),
|c| {
orig_constraints_csc
.outer_view(basic_vars[c]).unwrap().into_raw_storage()},0.1,&mutself.scratch,).unwrap();// TODO: When is singular basis matrix possible? Report as a proper error.// ^^^^^^^^^^^^^^^^^^^^^ this line
I have a somehow weird query, which contains 30 variables in format a[i] = a[i-1] * 1000 and two variables x, y with x = a[30] and y = a[30] + 1 and x <= y conditions. I guess singularity occurs due floating point errors, but I know nothing about simplex and your code.
Unrelated to this, how reliable is an Infeasible answer? People can attack my code by creating some constraints that are actually feasible but my code detects it as Infeasible, so even a panic is better than a false Infeasible for me. Would it work correct even in case of floating point errors? How I can validating the result, or restrict the input to prevent that?
The text was updated successfully, but these errors were encountered:
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Hi, and thanks for this great crate! I got a panic at
minilp-0.2.2/src/solver.rs:1301:10
:I have a somehow weird query, which contains 30 variables in format
a[i] = a[i-1] * 1000
and two variablesx, y
withx = a[30]
andy = a[30] + 1
andx <= y
conditions. I guess singularity occurs due floating point errors, but I know nothing about simplex and your code.Unrelated to this, how reliable is an
Infeasible
answer? People can attack my code by creating some constraints that are actually feasible but my code detects it asInfeasible
, so even a panic is better than a falseInfeasible
for me. Would it work correct even in case of floating point errors? How I can validating the result, or restrict the input to prevent that?The text was updated successfully, but these errors were encountered: