Modified predictor-corrector algorithm for locating weighted centers in linear programming
نویسندگان
چکیده
منابع مشابه
A Polynomial Predictor-Corrector Trust-Region Algorithm for Linear Programming
In this paper we present a scaling-invariant interior-point predictor-corrector type algorithm for linear programming (LP) whose iteration-complexity is polynomially bounded by the dimension and the logarithm of a certain condition number of the LP constraint matrix. At the predictor stage, the algorithm either takes the step along the standard affine scaling direction or a new trust-region typ...
متن کاملUsing a Modified Predictor-corrector Algorithm for Model Predictive Control
A modified predictor-corrector algorithm is presented. This algorithm obtains a pre-specified point on the primal-dual central-path. It is shown to be suitable for a recently proposed class of receding horizon control laws which include a recentred barrier in the cost function. The significance of these controllers is that hard constraints are replaced by penalty type soft constraints, which ha...
متن کاملMehrotra-type predictor-corrector algorithm revisited
Motivated by a numerical example which shows that a feasible version of Mehrotra’s original predictor-corrector algorithm might be inefficient in practice, Salahi et al., proposed a so-called safeguard based variant of the algorithm that enjoys polynomial iteration complexity while its practical efficiency is preserved. In this paper we analyze the same Mehrotra’s algorithm from a different per...
متن کاملAn Infeasible Start Predictor Corrector Method for Semi-deenite Linear Programming
In this paper we present an infeasible start path following predictor corrector method for semideenite linear programming problem. This method does not assume that the dual pair of semideenite programs have feasible solutions, and, in at most O(jlog((A;b;C))jn) iterations of the predictor corrector method, nds either an approximate solution to the dual pair or shows that there is no optimal sol...
متن کاملGPU Predictor-Corrector Interior Point Method for Large-Scale Linear Programming
This master’s thesis concerns the implementation of a GPUaccelerated version of Mehrotra’s predictor-corrector interior point algorithm for large-scale linear programming (LP). The implementations are tested on LP problems arising in the financial industry, where there is high demand for faster LP solvers. The algorithm was implemented in C++, MATLAB and CUDA, using double precision for numeric...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Optimization Theory and Applications
سال: 1994
ISSN: 0022-3239,1573-2878
DOI: 10.1007/bf02192939