ms·1977년 8월 1일
Generalized Programming by Linear Approximation of the Dual Gradient: Convex Programming Case
Michael Wagner, J. Franklin Sharp
Management Science
0
피인용
0.0
FWCI
0
IS/마케팅/OM 탑저널 피인용
6
IS/마케팅/OM 탑저널 참고문헌
- 주제수리최적화 · 생산·최적화
01Abstract
A modified version of Generalized Programming is presented for solving convex programming problems. The procedure uses convenient linear approximations of the gradient of the dual in order to approximate the Kuhn-Tucker conditions for the dual. Solution points of these approximate Kuhn-Tucker conditions are then used for column generation. Computational results are reported.
02연구 흐름
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03비슷한 논문
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04이후 연구
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05선행 연구
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06서지 정보
- 저널Management Science · 23(12) · 1307–1313
- 토픽Optimization and Variational Analysis · Computational Theory and Mathematics
- DOI10.1287/mnsc.23.12.1307
- 저자Michael Wagner, J. Franklin Sharp