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Resolve Memory issues in sparse models
OpenJun 29, 2026
No due date
•Last updated Tracking memory and sparsity related issues and PR's
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Optionally release memory during solving process
enhancementNew feature or requestNew feature or requestStatus: Open.#219 In PyPSA/linopy;- Status: Open.#248 In PyPSA/linopy;
perf: default integer arrays to int32 for ~25% memory reduction
performanceThis improves performance while not (meaningfully) altering behaviour for usersThis improves performance while not (meaningfully) altering behaviour for usersStatus: Open (in progress).@/dotagainst a sparse matrix densifies the result to full_termperformanceThis improves performance while not (meaningfully) altering behaviour for usersThis improves performance while not (meaningfully) altering behaviour for usersStatus: Open.#748 In PyPSA/linopy;mergeof ragged expressions is the peak allocation in PyPSA model buildsperformanceThis improves performance while not (meaningfully) altering behaviour for usersThis improves performance while not (meaningfully) altering behaviour for usersStatus: Open.#749 In PyPSA/linopy;groupby-sum pads every group to the largest group size
performanceThis improves performance while not (meaningfully) altering behaviour for usersThis improves performance while not (meaningfully) altering behaviour for usersStatus: Open.#745 In PyPSA/linopy;Umbrella: long-format / sparse
_termkernel (dense-_termmemory cluster)performanceThis improves performance while not (meaningfully) altering behaviour for usersThis improves performance while not (meaningfully) altering behaviour for usersStatus: Open.#756 In PyPSA/linopy;Multi-key groupby([names]).sum() densifies to a dense cartesian grid (memory)
performanceThis improves performance while not (meaningfully) altering behaviour for usersThis improves performance while not (meaningfully) altering behaviour for usersStatus: Open.#757 In PyPSA/linopy;