We present a novel optimization approach for the timetabling problem of a railway company,
ie, scheduling of a set of trains to obtain a profit maximizing timetable, while not violating …

Bundle methods are at the moment the most efficient and promising methods for nonsmooth
optimization. They have been successfully used in many practical applications, for example …

One of the largest bottlenecks in iron and steel production is the steelmaking-continuous
casting (SCC) process, which consists of steel-making, refining and continuous casting. The …

For non-smooth convex optimization, we consider level bundle methods built using an
oracle that computes values for the objective function and a subgradient at any given …

Inverse Imaging with Poisson Data is an invaluable resource for graduate students,
postdocs and researchers interested in the application of inverse problems to the domains of …

Minimization problems often occur in modeling phenomena dealing with real-life
applications that nowadays handle large-scale data and require real-time solutions. For …

Bundle methods are often the algorithms of choice for nonsmooth convex optimization,
especially if accuracy in the solution and reliability are a concern. We review several …

Convergence of a simple subgradient level method Page 1 Math. Program. 85: 207–211 (1999)
/ DOI 10.1007/s10107980003a © Springer-Verlag 1999 Jean-Louis Goffin · Krzysztof C. Kiwiel …

We propose a bundle method for minimizing nonsmooth convex functions that combines
both the level and the proximal stabilizations. Most bundle algorithms use a cutting-plane …

Many practical optimization problems involve nonsmooth (that is, not necessarily
differentiable) functions of hundreds or thousands of variables. In such problems, the direct …