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Convex Matroid Optimization [PDF]
SIAM Journal on Discrete Mathematics, 2002 We consider a problem of optimizing convex functionals over matroid bases. It is richly expressive and captures certain quadratic assignment and clustering problems. While generally NP-hard, we show it is polynomial time solvable when a suitable parameter is restricted.
Shmuel Onn
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Convex Optimization in R [PDF]
Journal of Statistical Software, 2014 Convex optimization now plays an essential role in many facets of statistics. We briefly survey some recent developments and describe some implementations of these methods in R .
Roger Koenker, Ivan Mizera
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Convex Combinatorial Optimization [PDF]
Discrete & Computational Geometry, 2004 We introduce the convex combinatorial optimization problem, a far reaching generalization of the standard linear combinatorial optimization problem. We show that it is strongly polynomial time solvable over any edge-guaranteed family, and discuss several applications.
Shmuel Onn, Uriel G. Rothblum
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Optimization of Convex Risk Functions [PDF]
SSRN Electronic Journal, 2004 We consider optimization problems involving convex risk functions. By employing techniques of convex analysis and optimization theory in vector spaces of measurable functions, we develop new representation theorems for risk models, and optimality and duality theory for problems with convex risk functions.
Andrzej Ruszczyński, Alexander Shapiro
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Quasi-Herglotz functions and convex optimization [PDF]
Royal Society Open Science, 2020 We introduce the set of quasi-Herglotz functions and demonstrate that it has properties useful in the modelling of non-passive systems. The linear space of quasi-Herglotz functions constitutes a natural extension of the convex cone of Herglotz functions.
Y. Ivanenko +5 more
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Convex Optimization of Bioprocesses [PDF]
IEEE Transactions on Automatic Control, 2022 We optimize a general model of bioprocesses, which is nonconvex due to the microbial growth in the biochemical reactors. We formulate a convex relaxation and give conditions guaranteeing its exactness in both the transient and steady state cases. When the growth kinetics are modeled by the Monod function under constant biomass or the Contois function ...
Josh A. Taylor +2 more
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Convex projection and convex multi-objective optimization [PDF]
Journal of Global Optimization, 2021 AbstractIn this paper we consider a problem, called convex projection, of projecting a convex set onto a subspace. We will show that to a convex projection one can assign a particular multi-objective convex optimization problem, such that the solution to that problem also solves the convex projection (and vice versa), which is analogous to the result ...
Gabriela Kováčová, Birgit Rudloff
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Quasi Semi and Pseudo Semi (p,E)-Convexity in Non-Linear Optimization Programming
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2023 The class of quasi semi -convex functions and pseudo semi -convex functions are presented in this paper by combining the class of -convex functions with the class of quasi semi -convex functions and pseudo semi -convex functions, respectively.
Revan I. Hazim, Saba N. Majeed
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Optimization Method for Wide Beam Sonar Transmit Beamforming
Sensors, 2022 Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles.
Louise Rixon Fuchs +2 more
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Quantum algorithms and lower bounds for convex optimization [PDF]
Quantum, 2020 While recent work suggests that quantum computers can speed up the solution of semidefinite programs, little is known about the quantum complexity of more general convex optimization.
Shouvanik Chakrabarti +3 more
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