Exact penalty methods for the solution of constrained optimization problems are based on
the construction of a function whose unconstrained minilnizing points are also solution of the …

We present a new feedback precision-adjustment rule for use with a smoothing technique
and standard unconstrained minimization algorithms in the solution of finite minimax …

We describe an extension of the classical cutting plane algorithm to tackle the unconstrained
minimization of a nonconvex, not necessarily differentiable function of several variables. The …

Consider a min-max problem in the form of min x ε X max 1≤ i≤ m {fi (x)}. It is well-known
that the non-differentiability of the max function F (x)≡ max 1≤ i≤ m {fi (x)} presents …

We consider the problem of optimally covering plane domains by a given number of circles.
The mathematical modeling of this problem leads to a min–max–min formulation which, in …

In this paper we present a derivative-free optimization algorithm for finite minimax problems.
The algorithm calculates an approximate gradient for each of the active functions of the finite …

We introduce a new approach to minimizing a function defined as the pointwise maximum
over finitely many convex real functions (next referred to as the “component functions”), with …

The derivative-free trust-region algorithm proposed by Conn et al.(SIAM J Optim 20: 387–
415, 2009) is adapted to the problem of minimizing a composite function\varPhi (x)= f (x)+ h …

In this paper we propose a new derivative-free algorithm for linearly constrained finite
minimax problems. Due to the nonsmoothness of this class of problems, standard derivative …

We introduce an iterative method for solving linearly constrained optimization problems,
whose nonsmooth nonconvex objective function is defined as the pointwise maximum of …