Debugging a wrong answer

What to read when a model solves to the wrong number, or does not solve, in the order that finds the fault soonest. Each step needs the file, or the file and its data, and none needs a solver run you have not already paid for.

1. Check the file against the sink

import lpspec as lps

lps.check('dispatch.yaml', sink='highs')

check raises on a construct outside the language, and with sink= on one the solver cannot take. A warning names a rewrite the sink will make: an sos: set on highs arrives as binaries, so the solve comes back with no duals (checking against a sink).

2. Read the shape the build produced

model = lps.build('dispatch.yaml', sources)
report = model.diagnostics()
report.columns, report.rows, report.nonzeros  # 8, 4, 8
report.omissions  # rows a constraint declared and did not build
report.sparse_parameters  # parameters whose table is short of their coordinates

A count smaller than you expected is a mask, or a table with a row missing. omissions names the constraint, sparse_parameters the parameter; which one you have is the difference between a where: you wrote and a row you lost (diagnostics).

3. Read the row that is wrong

print(model.row('power_balance', snapshot=2))
# power_balance[snapshot=2]: +1 p[2, wind] +1 p[2, gas] == 180

The line is the row as the solver got it: every coefficient the data produced, and no term for a variable a where: removed. Here solar is absent because its p_max is 0.0. A term you expected and do not see is a mask; a coefficient you did not expect is the data (reading one row).

4. When the solve is infeasible

result = model.solve()
result.status, result.termination_condition  # 'warning', 'infeasible'
result.has_primal  # False: nothing to read, and every reader raises

There is no IIS (irreducible infeasible subsystem) read-back. Locate the fault instead with a slack: add a variable to the balance row, minimise it, and read where it is nonzero. The feasibility model is that file for a dispatch. Then read the row at a snapshot the slack lands on, as in step 3.

5. When the number is wrong and the rows look right

Build the same file on the other lane and compare the objectives:

from lpspec import linopy as lpspec_linopy

m = lpspec_linopy.build('dispatch.yaml', sources)
m.solve()
m.objective.value  # against result.objective

Two lanes agreeing on a number you still believe is wrong means the file says something other than what you meant. Render it as math and read the constraint as written: typeset.