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pympcc 1.0.0
pympcc 1.0.0

Getting started

  • Installation
  • Quickstart

User guide

  • Problem setup
  • Strategies
  • Strategy selection
  • Troubleshooting
  • Diagnostics
  • Sparse Jacobians and the slack strategy
  • Parametric sensitivity
  • Differentiable solve (solve_jax)
  • Bilevel KKT-emitter frontend
  • EPEC: equilibrium problems with equilibrium constraints
  • Presolve and multistart
  • AMPL .nl reader

Examples

  • Examples
    • A first MPCC, walked through
    • Tour of strategies
    • MCP variable-paired form
    • Sparse Jacobians
    • Stationarity hierarchy: S / M / C / W
    • KKT residual: how to read it
    • Slack-lifting strategy
    • NCP-function variants
    • Multistart
    • Stateful warm hot-start
    • Presolve
    • Diagnostics tour
    • TNLP refinement
    • Bilevel KKT emission
    • Differentiable solve with jax.grad
    • Parametric sweep with pympcc.sensitivity

Theory primer

  • Theory primer
    • Why MPCCs are hard
    • Constraint qualifications
    • Stationarity hierarchy
    • Second-order sufficient conditions

Reference

  • API reference
    • pympcc.MPCCProblem
    • pympcc.StructuredMPCC
    • pympcc.ParametricMPCC
    • pympcc.solve
    • pympcc.MPCCSolver
    • pympcc.solve_jax
    • pympcc.multistart
    • pympcc.MPCCResult
    • pympcc.IterationInfo
    • pympcc.IPOPTStatus
    • pympcc.MultiStartResult
    • pympcc.TNLPResult
    • pympcc.SensitivityResult
    • pympcc.active_sets
    • pympcc.classify_cq
    • pympcc.degeneracy_report
    • pympcc.initial_point_statistics
    • pympcc.jac_norms
    • pympcc.merit_cross_check
    • pympcc.sosc_check
    • pympcc.classify_stationarity
    • pympcc.compute_kkt_residual
    • pympcc.verify_b_stationarity
    • pympcc.sensitivity
    • pympcc.active_row_labels
    • pympcc.presolve
    • pympcc.PresolveMap
    • pympcc.autoscale_comp_pairs
    • pympcc.unscale_multipliers
    • pympcc.bilevel.from_lower_level
    • pympcc.frontend.ampl.from_nl
  • Changelog
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pympcc.unscale_multipliers¶

pympcc.unscale_multipliers(result, mpcc_mult_G, mpcc_mult_H)[source]¶

Module-level alias for MPCCResult.unscale_comp_multipliers().

Return type:

tuple[ndarray, ndarray]

Parameters:
  • result (MPCCResult)

  • mpcc_mult_G (ndarray)

  • mpcc_mult_H (ndarray)

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  • pympcc.unscale_multipliers
    • unscale_multipliers()