Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method. We present a homogeneous algorithm equipped with a modified potential function for the monotone complementarity problem. We show that this potential function is reduced by at least a constant amount if a scaled Lipschitz condition (SLC) is satisfied. A practical algorithm based on this potential function is implemented in a software package named iOptimize. The implementation in iOptimize maintains global linear and polynomial time convergence properties, while achieving practical performance. It either successfully solves the problem, or concludes that the SLC is not satisfied. When compared with the mature software package MOSEK (barrier solver version 22.214.171.124), iOptimize solves convex quadratic programming problems, convex quadratically constrained quadratic programming problems, and general convex programming problems in fewer iterations. Moreover, several problems for which MOSEK fails are solved to optimality. We also find that iOptimize detects infeasibility more reliably than the general nonlinear solvers Ipopt (version 3.9.2) and Knitro (version 8.0).
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Alizadeh, Morteza; Amiri-Aref, Mehdi; Mustafee, Navonil; Matilal, Sumohon: A robust stochastic casualty collection points location problem (2019)
- Petra, Cosmin G.; Potra, Florian A.: A homogeneous model for monotone mixed horizontal linear complementarity problems (2019)
- Huang, Kuo-Ling; Mehrotra, Sanjay: Solution of monotone complementarity and general convex programming problems using a modified potential reduction interior point method (2017)
- Huang, Kuo-Ling; Mehrotra, Sanjay: An empirical evaluation of a walk-relax-round heuristic for mixed integer convex programs (2015)