Abstract. Minimizing a convex function over a convex set in n-dimensional space is a basic, general problem with many interesting special cases. Here, we present a simple new algorithm for convex optimization based on sampling by a random walk. It extends naturally to minimizing quasi-convex functions and to other generalizations
Abstract. We propose a randomized method for general convex optimization problems; namely, the minim...
Let D be a DAG and let X be any non-empty subset of D\u27s vertices. X is a convex set of D if D con...
xvi, 152 p. : ill. ; 30 cm.PolyU Library Call No.: [THS] LG51 .H577P AMA 2013 HuThe purpose of this ...
Several Markov chain sampling algorithms, including the Hit-and-Run algorithm, are unified within th...
This paper deals with the sampled scenarios approach to robust convex programming. It has been shown...
In this paper, we prove new complexity bounds for methods of convex optimization based only on compu...
htmlabstractWe consider maximising a concave function over a convex set by a simplerandomised algori...
In this paper we develop probabilistic arguments for justifying the quality of an approximate soluti...
We consider the problem of optimizing an approximately convex function over a bounded convex set in ...
We consider maximising a concave function over a convex set by a simple randomised algorithm. The st...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
Lagrangian relaxation and approximate optimization algorithms have received much attention in the la...
We consider unconstrained randomized optimization of smooth convex functions in the gradient-free se...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
International audienceWe discuss the possibility to accelerate solving extremely large-scale well st...
Abstract. We propose a randomized method for general convex optimization problems; namely, the minim...
Let D be a DAG and let X be any non-empty subset of D\u27s vertices. X is a convex set of D if D con...
xvi, 152 p. : ill. ; 30 cm.PolyU Library Call No.: [THS] LG51 .H577P AMA 2013 HuThe purpose of this ...
Several Markov chain sampling algorithms, including the Hit-and-Run algorithm, are unified within th...
This paper deals with the sampled scenarios approach to robust convex programming. It has been shown...
In this paper, we prove new complexity bounds for methods of convex optimization based only on compu...
htmlabstractWe consider maximising a concave function over a convex set by a simplerandomised algori...
In this paper we develop probabilistic arguments for justifying the quality of an approximate soluti...
We consider the problem of optimizing an approximately convex function over a bounded convex set in ...
We consider maximising a concave function over a convex set by a simple randomised algorithm. The st...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
Lagrangian relaxation and approximate optimization algorithms have received much attention in the la...
We consider unconstrained randomized optimization of smooth convex functions in the gradient-free se...
Random convex programs (RCPs) are convex optimization problems subject to a finite number of constra...
International audienceWe discuss the possibility to accelerate solving extremely large-scale well st...
Abstract. We propose a randomized method for general convex optimization problems; namely, the minim...
Let D be a DAG and let X be any non-empty subset of D\u27s vertices. X is a convex set of D if D con...
xvi, 152 p. : ill. ; 30 cm.PolyU Library Call No.: [THS] LG51 .H577P AMA 2013 HuThe purpose of this ...