Comments (4)
In my testing, I found that that was hardly (never) faster, and was sometimes actually slower than just autosolving all the time.
My hunch for why this could be is that keeping the system in a solved state requires fewer pivots in total to stay at a correct solution, rather than solving from scratch from a random unsolved state.
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I've only tested this on other implementations, but this might not be the case for your implementation...
If not, then it's no problem I guess!
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Interesting.
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Another benefit of always solving is that you know immediately upon adding a constraint if the system is over-constrained. Without this, it can be difficult to debug the bad constraint if the exception is deferred until everything is added.
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Related Issues (20)
- pip install fails on Python 3.11b3 HOT 17
- Failed building wheel for kiwisolver :( HOT 4
- Cann't install on python 3.11b3 HOT 2
- 1.4.3 has no attribute __version__ HOT 2
- ERROR: Failed building wheel for kiwisolver: C2440: 'static_cast': cannot convert from 'expr_type' to 'type' HOT 2
- Provide cp311 releases HOT 1
- Objective cell memory leak? HOT 26
- Continuous fuzzing by way of Google's OSS-Fuzz HOT 1
- Tests fail on pypy
- #egg=kiwisolver See assertion error when installing kiwisolver HOT 4
- about duplicate constraints HOT 3
- Add Python API to get all constraints HOT 14
- Is it possible to get the absolute value of an expression? HOT 10
- Is it possible to tell the Solver to prefer smaller or larger solutions? HOT 3
- Missing type hint for `Constraint.violated()` HOT 1
- Python 3.12 manylinux wheels HOT 2
- PyInstaller executable with matplotlib cannot find _cext DLL HOT 6
- `top_level.txt` include a non existing module `src`
- DLL load failed while importing _cext HOT 2
- How do we define an "objective function" and maximize or minimize it? HOT 1
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