A sufficient descent LS conjugate gradient method for unconstrained optimization problems

Li, Min; Feng, Heying

doi:10.1016/j.amc.2011.06.034

Cited by 18 publications

(16 citation statements)

References 13 publications

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“…The CPU processor used was Intel (R) Core TM i3-M350 (2.27GHz) with RAM 4 GB. (5,5), (20,20),(50,50) 8 TRECANNI 2 (5,5), (10,10),(50,50) 9 EXTENDED WOOD 4 (5,..,5), (20,..,20), (30,..,30) 10 CLOVILLE 4 (2,..,2),(4,..,4),(10,..,10) 11 HAGER 2 4 (7,7), (15,15),(22,) (7,..,7), (15,..,15), ( (15,15), (30,30),(150,150) (15,.,15), (30,..,30),(150,..,150) 16 EXTENDED PENALTY (1,1), (5,5),(10,10) (1,..,1),(5,..,5),(10,..,10) 23 EXTENDED TRIDIAGONAL1 2 10,100,1000 (6,6), (12,12),(17,17) (6,..,6), (12,..,12), (17,..,17) 24 DIAGONAL 4 2 10,100,1000…”

Section: Resultsmentioning

confidence: 99%

“…(1,1), (20,20),(40,40) (1,..,1), (20,..,20),(40,..,40) 25 EXTENDED HIMMELBLAU 2 10,100,1000 (10,10),(50,50),(125,125) (10,..,10),(50,..,50),(125,..,125) 26 FLETCHCR 2 10,100,1000 (3,3), (12,12), (15,15) (3,..,3), (12,..,12), (15,..,15) 27 EXTENDED DENSCHNB 2 10,100,1000 (5,5), (30,30),(50,50) (5,..,5), (30,..,30),(50,..,50) 28 EXTENDED BLOCK DIAG-ONAL BD1 2 10,100,1000…”

Section: Resultsmentioning

confidence: 99%

“…CD method and DY method were also proved to have global convergence under Strong Wolfe line search [12,13]. Some convergent versions were proposed by using some new complicated line searches, or through restricting the parameter to a nonnegative number [20]. However, to the best of our knowledge, the global convergence of PRP, LS and HS methods have not been proven under all mentioned line searches.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Solving a large scale nonlinear unconstrained optimization problems by using new coefficient of conjugate gradient method with exact line search direction

Alkouli¹,

Mamat²,

Rivaie³

et al. 2018

IJET

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In this paper, an efficient modification of nonlinear conjugate gradient method and an associated implementation, based on an exact line search, are proposed and analyzed to solve large-scale unconstrained optimization problems. The method satisfies the sufficient descent property. Furthermore, global convergence result is proved. Computational results for a set of unconstrained optimization test problems, some of them from CUTE library, showed that this new conjugate gradient algorithm seems to converge more stable and outperforms the other similar methods in many situations.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solving a large scale nonlinear unconstrained optimization problems by using new coefficient of conjugate gradient method with exact line search direction

Alkouli¹,

Mamat²,

Rivaie³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…Simulation 1: Comparison with the line search (LS) [10] Consider a synthesis problem using a 17-element half-wave-length spacing linear array with the following configurations: θ 1 = −4.5, θ 2 = −3.0, θ 3 = 3.0, and θ 4 = 4.5. The simulations based on the DA and LS algorithms were executed 10 times independently, and the results are listed in Table 1.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For the comparison utilizing Wolfe's rule, we first describe the parameter. Wolfe's rule defines step length α k such that [10] …”

Section: New Descent Algorithmmentioning

confidence: 99%

Penalty Function Solution to Pattern Synthesis of Antenna Array by a Descent Algorithm

Zeng¹,

Feng²

2013

PIER B

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Abstract-In this paper, an algorithm based on penalty cost function for synthesizing flat-top patterns is proposed. A descent algorithm (DA) as its optimizing approach is proposed in this paper as well. Apparently, whole algorithm efficiency totally depends on the DA. Unlike traditional descent method, the DA defines step length by solving a inequality, instead of Wolf or Armijo-type search rule, stimulation results indicate that it can improve the computational efficiency. Under mild conditions, we prove that the DA has strong convergence properties. Several numerical examples are presented to illustrate the effectiveness of the proposed algorithm. The results indicate that the approach is effective in the pattern shape precisely in both mainlobe and sidelobe region for arbitrary linear arrays.

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An Efficient Hybrid Conjugate Gradient Method for Unconstrained Optimisation

Alkhouli

Hamatta

Mamat

et al. 2020

Forum for Interdisciplinary Mathematics

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A sufficient descent LS conjugate gradient method for unconstrained optimization problems

Cited by 18 publications

References 13 publications

Solving a large scale nonlinear unconstrained optimization problems by using new coefficient of conjugate gradient method with exact line search direction

Solving a large scale nonlinear unconstrained optimization problems by using new coefficient of conjugate gradient method with exact line search direction

Penalty Function Solution to Pattern Synthesis of Antenna Array by a Descent Algorithm

An Efficient Hybrid Conjugate Gradient Method for Unconstrained Optimisation

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