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upper bound not reported correctly in CBC Progress Callback

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Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
45/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Stale
Tech stack
python
Domain
backend

Research direction

Start with the provided Python reproduction, focusing on Model.store_search_progress_log, Model.optimize, and search_progress_log.log. Compare the CBC progress callback's recorded ub and lb values during optimization; done means ub is reported consistently with the expected progression from infinity toward a finite value.

Written by the indexing model from the issue text.

Description

Describe the bug
The CBC progress callback method doesn't report the correct values of ub (upper bound). While the lb (lower bound) is reported correctly (it increases over time from zero), the ub drops immediately from infinity to zero

To Reproduce

import mip
import tempfile

lp_str = '''
Minimize
 obj: xMAX
 
Subject To
 xMAX(b): xMAX - x(b) >= 0
 xMAX(d): xMAX - x(d) >= 0
 xMAX(f): xMAX - x(f) >= 0
 xMAX(g): xMAX - x(g) >= 0
 xMAX(l): xMAX - x(l) >= 0
 xMAX(n): xMAX - x(n) >= 0
 dc_a(g,b): x(g) - x(b) + 1000 t(g,b) >= 1
 dc_b(g,b): x(b) - x(g) - 1000 t(g,b) >= -999
 dc_a(g,d): x(g) - x(d) + 1000 t(g,d) >= 1
 dc_b(g,d): x(d) - x(g) - 1000 t(g,d) >= -999
 dc_a(g,f): x(g) - x(f) + 1000 t(g,f) >= 1
 dc_b(g,f): x(f) - x(g) - 1000 t(g,f) >= -999
 dc_a(g,n): x(g) - x(n) + 1000 t(g,n) >= 1
 dc_b(g,n): x(n) - x(g) - 1000 t(g,n) >= -999
 dc_a(g,l): x(g) - x(l) + 1000 t(g,l) >= 1
 dc_b(g,l): x(l) - x(g) - 1000 t(g,l) >= -999
 dc_a(b,f): x(b) - x(f) + 1000 t(b,f) >= 1
 dc_b(b,f): x(f) - x(b) - 1000 t(b,f) >= -999
 dc_a(b,n): x(b) - x(n) + 1000 t(b,n) >= 1
 dc_b(b,n): x(n) - x(b) - 1000 t(b,n) >= -999
 dc_a(b,l): x(b) - x(l) + 1000 t(b,l) >= 1
 dc_b(b,l): x(l) - x(b) - 1000 t(b,l) >= -999
 dc_a(f,l): x(f) - x(l) + 1000 t(f,l) >= 1
 dc_b(f,l): x(l) - x(f) - 1000 t(f,l) >= -999

Bounds
 x(b) <= 5
 x(d) <= 5
 x(f) <= 5
 x(g) <= 5
 x(l) <= 5
 x(n) <= 5

Binaries
 t(g,b) t(g,d) t(g,f) t(g,n)
 t(g,l) t(b,f) t(b,n)
 t(b,l) t(f,l)

Generals
 x(b) x(d) x(f) x(g) x(l) x(n)
 
End
'''

with tempfile.NamedTemporaryFile(suffix='.lp', delete=False) as tmp:
    # Store string to tmp file
    tmp.write(lp_str.encode('utf-8'))
    tmp.close()
    # Instantiate a problem model and have it read the tmp file
    test_mdl = mip.Model()
    test_mdl.read(path=tmp.name)

test_mdl.store_search_progress_log = True

test_mdl.optimize(max_seconds=300)

for second, (lb, ub) in test_mdl.search_progress_log.log:
	print(ub)

Expected behavior
The ub should gradually decrease from infinity to a finite value.

Desktop:

  • Operating System, version: Win 11
  • Python version: 3.11
  • Python-MIP version: 1.15
Dominant language
Linear Programming
Stars
601
Forks
108
No merged PRs in 30d

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