A RIEMANNIAN-GEOMETRICAL APPROACH TO STRICTLY CONVEX QUADRATIC PROGRAMMING WITH CONVEXITY-PRESERVING METRIC PARAMETERIZATION
Author:
Affiliation:
1. Mitsubishi Electric Corporation
2. Kyoto University
Publisher
The Operations Research Society of Japan
Subject
Management Science and Operations Research,General Decision Sciences
Link
https://www.jstage.jst.go.jp/article/jorsj/66/4/66_219/_pdf
Reference28 articles.
1. [1] P-A. Absil, C.G. Baker and K.A. Gallivan: Trust-region methods on Riemannian manifolds. Foundation of Computational Mathematics, 7 (2007), 303–330.
2. [2] F. Alvarez, J. Bolte and J. Munier: A unifying local convergence result for Newton's method in Riemannian manifolds. Foundation of Computational Mathematics, 8 (2008), 197–226.
3. [3] D.P. Bertsekas: Nonlinear Programming (Athena Scientific, 2016).
4. [4] T.J. Carpenter, I.J. Lusting, J.M. Mulvey and D.F. Shanno: Higher-order predictor-corrector interior point methods with application to quadratic objectives. SIAM Journal of Optimization, 3 (1993), 696–725.
5. [5] F. Curtis and J. Nocedal: Steplength selection in interior-point methods for quadratic programming. Applied Mathematics Letters, 20 (2007), 516–523.
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