LumiX 0.1.2

Added

• CONTRIBUTING.md with comprehensive development guidelines
• CODE_OF_CONDUCT.md with academic-focused community standards
• SECURITY.md with vulnerability reporting policy
• This CHANGELOG.md to track project changes
• Regression tests for the linearizer (both objective senses), the CP-SAT adapter and expression arithmetic

Fixed

• Linearizer kept every objective as a minimization. LXLinearizer.linearize_model()
  compared the objective sense to "maximize" while LXObjectiveSense values are
  "min"/"max". Maximized product, min and piecewise objectives returned wrong optima
  (example 06 reported an area of 16 instead of 100).
• LXOptimizer.enable_linearization() had no effect. solve() now linearizes the model
  with the solver's capabilities when linearization is enabled and the model needs it.
• CP-SAT crashed the process with OR-Tools ≥ 9.13. Constraints are now named with
  with_name() instead of writing through Proto().
• priority_to_weight(1) returned 1e5 instead of the documented 1e6.
• Adding two expressions that share a variable silently dropped terms. e1 + e2 combined
  the two where filters with AND, so instances selected by only one of them were lost.
  Calling add_term twice for the same variable overwrote the first term. Both now keep every
  term: an instance is included when either term includes it, with the sum of the
  coefficients that apply to it.
• expr * scalar always raised ValueError. It now scales every term (including
  multi-indexed terms) and the constant.
• Version strings in lumix.__version__ and the documentation now match the package version.
• pytest coverage now measures lumix (was the old optixng name).

Behavior changes

These fixes intentionally change results of code that relied on the old behavior:

• Maximized nonlinear objectives solved through the linearizer now return the correct
  (different) optimum.
• Goal-programming objective values are 10× larger with the default weights because
  priority 1 now weighs 1e6. The ranking of priorities is unchanged; the optimal plan is
  usually the same.
• enable_linearization() now linearizes. Models that were linearized by hand are not
  linearized twice: needs_linearization() is false for them.
• Expressions built by adding expressions (or repeated add_term calls) on the same variable
  now contain all of their terms; models that silently lost terms will change.