“…x + := x + ∆x, y + := y + ∆y, s + := s + ∆s. (12) Furthermore, in each iteration of the algorithm, a quantity is needed to measure how far an iterate is from the central path. We consider the proximity measure defined by…”
Section: New Search Directionsmentioning
confidence: 99%
“…The author is improved this algorithm so that the algorithm performs only one feasibility step in each iteration and does not need centering steps [20]. Kheirfam [11,12,13,14] extended the algorithm proposed in [20] to HLCP, the Cartesian P * (κ)-LCP, the convex quadratic symmetric cone optimization (CQSCO) and SO. By considering the AET technique based on the function ψ(t) = t − √ t, Darvay et al [3] have introduced a full-Newton step IPM for LO.…”
In this paper, we study an infeasible interior-point method for linear optimization with full-Newton step. The introduced method uses an algebraic equivalent transformation on the centering equation of the system which defines the central path. We prove that the method finds an ε-optimal solution of the underlying problem in polynomial time.
“…x + := x + ∆x, y + := y + ∆y, s + := s + ∆s. (12) Furthermore, in each iteration of the algorithm, a quantity is needed to measure how far an iterate is from the central path. We consider the proximity measure defined by…”
Section: New Search Directionsmentioning
confidence: 99%
“…The author is improved this algorithm so that the algorithm performs only one feasibility step in each iteration and does not need centering steps [20]. Kheirfam [11,12,13,14] extended the algorithm proposed in [20] to HLCP, the Cartesian P * (κ)-LCP, the convex quadratic symmetric cone optimization (CQSCO) and SO. By considering the AET technique based on the function ψ(t) = t − √ t, Darvay et al [3] have introduced a full-Newton step IPM for LO.…”
In this paper, we study an infeasible interior-point method for linear optimization with full-Newton step. The introduced method uses an algebraic equivalent transformation on the centering equation of the system which defines the central path. We prove that the method finds an ε-optimal solution of the underlying problem in polynomial time.
“…Each main iteration of the aforementioned IIPMs is composed of one so-called feasibility step and a several centering steps to get an -optimal solution of the underlying problem. Recently, Roos [23] and Kheirfam [12,13,14] proposed IIPMs for LO, HLCP, the Cartesian P * (κ)-SCLCP and SCO so that their algorithms do not need centering steps and take only one feasibility step in order to get a new iterate close enough to the central path.…”
Section: Behrouz Kheirfam and Guoqiang Wangmentioning
confidence: 99%
“…Motivated by Roos [23] and Kheirfam [12,13,14], we present a full-NT step IIPM for CO. Each main iteration of the proposed algorithm is consisted of only one feasibility step. Moreover, we analyze the algorithm and derive the iteration-complexity bound which matches the currently best-known iteration bound for IIPMs.…”
Section: Behrouz Kheirfam and Guoqiang Wangmentioning
In this paper, we design a primal-dual infeasible interior-point method for circular optimization that uses only full Nesterov-Todd steps. Each main iteration of the algorithm consisted of one so-called feasibility step. Furthermore, giving a complexity analysis of the algorithm, we derive the currently best-known iteration bound for infeasible interior-point methods.2010 Mathematics Subject Classification. 90C51.
“…Roos [23] introduced an improved version of the method for LO that does not require centering steps, while the aforementioned methods require several (at most three) centering steps in each (main) iteration. Kheirfam extended this method to HLCP [11], the Cartesian P * (κ)-LCP [12], the convex quadratic symmetric cone optimization (CQSCO) [13] and SO [14]. Kheirfam [7] proposed an infeasible version of the method presented in [4] for SDLCP.…”
In this work, we investigate a full Newton step infeasible interior-point method for linear optimization based on a new search direction which is obtained from an algebraic equivalent transformation of the central path system. Furthermore, we prove that the proposed method obtains an ε-optimal solution to the original problem in polynomial time.
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