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Main Authors: Lin, Kangyu, Ohtsuka, Toshiyuki
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2503.05181
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author Lin, Kangyu
Ohtsuka, Toshiyuki
author_facet Lin, Kangyu
Ohtsuka, Toshiyuki
contents Our recent study (Lin and Ohtsuka, 2024) proposed a new penalty method for solving mathematical programming with complementarity constraints (MPCC). This method first reformulates MPCC as a parameterized nonlinear programming called gap penalty reformulation and then solves a sequence of gap penalty reformulations with an increasing penalty parameter. This study examines the convergence behavior of the new penalty method. We prove that it converges to a strongly stationary point of MPCC, provided that: (i) The MPCC linear independence constraint qualification holds. (ii) The upper-level strict complementarity condition holds. (iii) The gap penalty reformulation satisfies the second-order necessary conditions in terms of the second-order directional derivative. Because strong stationarity is used to identify the MPCC local minimum, our analysis indicates that the new penalty method can find an MPCC solution.
format Preprint
id arxiv_https___arxiv_org_abs_2503_05181
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Gap Penalty Reformulation for Mathematical Programming with Complementarity Constraints: Convergence Analysis
Lin, Kangyu
Ohtsuka, Toshiyuki
Optimization and Control
Our recent study (Lin and Ohtsuka, 2024) proposed a new penalty method for solving mathematical programming with complementarity constraints (MPCC). This method first reformulates MPCC as a parameterized nonlinear programming called gap penalty reformulation and then solves a sequence of gap penalty reformulations with an increasing penalty parameter. This study examines the convergence behavior of the new penalty method. We prove that it converges to a strongly stationary point of MPCC, provided that: (i) The MPCC linear independence constraint qualification holds. (ii) The upper-level strict complementarity condition holds. (iii) The gap penalty reformulation satisfies the second-order necessary conditions in terms of the second-order directional derivative. Because strong stationarity is used to identify the MPCC local minimum, our analysis indicates that the new penalty method can find an MPCC solution.
title A Gap Penalty Reformulation for Mathematical Programming with Complementarity Constraints: Convergence Analysis
topic Optimization and Control
url https://arxiv.org/abs/2503.05181