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SIAMJO
2008
74views more  SIAMJO 2008»
15 years 6 months ago
Lagrangian-Dual Functions and Moreau--Yosida Regularization
In this paper, we consider the Lagrangian dual problem of a class of convex optimization problems. We first discuss the semismoothness of the Lagrangian-dual function . This prope...
Fanwen Meng, Gongyun Zhao, Mark Goh, Robert de Sou...
SIAMJO
2008
84views more  SIAMJO 2008»
15 years 6 months ago
Regularity Conditions via Quasi-Relative Interior in Convex Programming
We give some new regularity conditions for Fenchel duality in separated locally convex vector spaces, written in terms of the notion of quasi interior and quasi-relative interior, ...
Radu Ioan Bot, Ernö Robert Csetnek, Gert Wank...
SIAMJO
2008
93views more  SIAMJO 2008»
15 years 6 months ago
Multivariable Utility Functions
Abstract. Utility functions of several variables are ubiquitous in economics. Their maximization requires inversion of the gradient map. Using convex analysis tools, we provide a r...
Maria B. Chiarolla, Ulrich G. Haussmann
SIAMJO
2008
99views more  SIAMJO 2008»
15 years 6 months ago
The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs
Many optimization problems are naturally delivered in an uncertain framework, and one would like to exercise prudence against the uncertainty elements present in the problem. In pr...
Marco C. Campi, Simone Garatti
SIAMJO
2008
66views more  SIAMJO 2008»
15 years 6 months ago
Further Relaxations of the Semidefinite Programming Approach to Sensor Network Localization
Recently, a semidefinite programming (SDP) relaxation approach has been proposed to solve the sensor network localization problem. Although it achieves high accuracy in estimating ...
Zizhuo Wang, Song Zheng, Yinyu Ye, Stephen Boyd