Results 301 to 310 of about 6,410,351 (366)

Convex Sets and Convex Programming

1981
In this chapter we concentrate on properties of convex sets in a Hilbert space and some of the related problems of importance in application to convex programming: variational problems for convex functions over convex sets, central to which are the Kuhn-Tucker theorem and the minimax theorem of von Neumann, which in turn are based on the “separation ...
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Random Convex Programs

SIAM Journal on Optimization, 2010
Random convex programs (RCPs) are convex optimization problems subject to a finite number $N$ of random constraints. The optimal objective value $J^*$ of an RCP is thus a random variable. We study the probability with which $J^*$ is no longer optimal if a further random constraint is added to the problem (violation probability, $V^*$).
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Interior-point polynomial algorithms in convex programming

Siam studies in applied mathematics, 1994
Y. Nesterov, A. Nemirovski
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Preliminaries: Convex Analysis and Convex Programming

2001
In this chapter, we give some definitions and results connected with convex analysis, convex programming, and Lagrangian duality. In Part Two, these concepts and results are utilized in developing suitable optimality conditions and numerical methods for solving some convex problems.
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The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming

, 1967
L. Bregman
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Basic Convex Programming

2001
Convex programming studies problems of the form (CP) where the objective function f: Rn → R and the constraints fi: Rn → R, i * P are “convex functions”. “Convexity” is a magic word in the world of optimization, because it allows the results for local optima to be extended to global optima.
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Optimally linearizing the alternating direction method of multipliers for convex programming

Computational optimization and applications, 2019
B. He, Fengming Ma, Xiaoming Yuan
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Technical Note - Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming

Operational Research, 1973
A. Soyster
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A new iteration technique for nonlinear operators as concerns convex programming and feasibility problems

Numerical Algorithms, 2019
D. R. Sahu, A. Pitea, M. Verma
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Motion planning around obstacles with convex optimization

Science Robotics, 2023
Tobia Marcucci
exaly