← 研踪 PaperTrailarXiv:2611.07815 · v1
Newton Steps are Faster for Linear Complementarity Problems
首次提交 2026-11-05 18:53
版本更新 2026-11-05 18:53
雷达观测 2026-11-06 11:06
摘要
Linear Complementarity Problems (LCPs) with sufficient matrices form a well-studied class of problems, dating back from the foundational works of Cottle and Dantzig (1968). LCPs capture convex quadratic programs, and admit reductions from problems such as discounted stochastic games or mean-payoff games. While the original approach for solving these problems was based on pivoting methods, in their 1991 book, Kojima, Megiddo, Noma, and Yoshise introduced an interior point method (IPM) with running time that is polynomial in the input size, but which depends linearly on a certain parameter called the handicap number of the matrix. Despite the ensuing rich literature on sufficient LCP IPMs, the linear dependence on the handicap number has not been improved since then. We present an IPM whose running time depends on the square root of the handicap number. The algorithm can decrease the duality gap by a constant factor in $O\left(\min\left\{\sqrt{n} (1+κ),n^{9/8}\sqrt{1+κ}\right\}\right)$ iterations. Curiously, this can already be achieved using standard Newton updates. We analyze the IPM in a norm defined by a local weighting, where centralizing Newton steps quadratically converge to the central path, without any dependence on the handicap number. The weights arise from a characterization by Rump (Linear Algebra and Its Applications, 1997, 1998, 2003) of the sign-real spectral radius and its relation to sufficiency; another ingredient of our proof is a new operator norm characterization of the handicap number. Moreover, the connection to Rump's work also enables us to show coNP-completeness of testing sufficiency, and approximation hardness of the handicap number.
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Newton Steps are Faster for Linear Complementarity Problems
Linear Complementarity Problems (LCPs) with sufficient matrices form a well-studied class of problems, dating back from the foundational works of Cottle and Dantzig (1968). LCPs capture convex quadratic programs, and admit reductions from problems such as discounted stochastic games or mean-payoff games. While the original approach for solving these problems was based on pivoting methods, in their 1991 book, Kojima, Megiddo, Noma, and Yoshise introduced an interior point method (IPM) with running time that is polynomial in the input size, but which depends linearly on a certain parameter called the handicap number of the matrix. Despite the ensuing rich literature on sufficient LCP IPMs, the linear dependence on the handicap number has not been improved since then. We present an IPM whose running time depends on the square root of the handicap number. The algorithm can decrease the duality gap by a constant factor in $O\left(\min\left\{\sqrt{n} (1+κ),n^{9/8}\sqrt{1+κ}\right\}\right)$ iterations. Curiously, this can already be achieved using standard Newton updates. We analyze the IPM in a norm defined by a local weighting, where centralizing Newton steps quadratically converge to the central path, without any dependence on the handicap number. The weights arise from a characterization by Rump (Linear Algebra and Its Applications, 1997, 1998, 2003) of the sign-real spectral radius and its relation to sufficiency; another ingredient of our proof is a new operator norm characterization of the handicap number. Moreover, the connection to Rump's work also enables us to show coNP-completeness of testing sufficiency, and approximation hardness of the handicap number.
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