Optimization of “log x" entropy over linear equality constraints
SIAM Journal on Control and Optimization
A parallel algorithm for a class of convex programs
SIAM Journal on Control and Optimization
On the convergence of the proximal point algorithm for convex minimization
SIAM Journal on Control and Optimization
How to deal with the unbounded in optimization: theory and algorithms
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
On Generalizations of the Frank-Wolfe Theorem to Convex and Quasi-Convex Programmes
Computational Optimization and Applications
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We consider a convex function f(x) with unbounded level sets. Many algorithms, if applied to this class of functions, do not guarantee convergence to the global infimum. Our approach to this problem leads to a derivation of the equation of a parametrized curve x(t), such that an infimum of f(x) along this curve is equal to the global infimum of the function on /Bbb Rn.We also investigate properties of the vectors of recession, showing in particular how to determine a cone of recession of the convex function. This allows us to determine a vector of recession required to construct the minimizing trajectory.