Optimization Theory and Applications

Dear Colleagues,

Optimization algorithms lie at the core of many contemporary tools used in science and engineering. They represent the engine behind design automation in electrical and mechanical engineering, protein-folding simulations, drug design, machine learning, scheduling and timetable design, traffic management, resource allocation, decision making, model predictive control, geophysical-parameter estimation, portfolio management, asset-price modelling, etc.

Nature can be a great source of inspiration for designing optimization algorithms. In the past, algorithms were devised so that they mimicked the annealing of metals, the motion of objects in gravitational fields, evolution, animal behavior, and many more inventions of mother nature.

The theoretical analysis of optimization algorithms is important not only because it confirms the appropriateness of an algorithm. It also provides insight into the algorithm's limitations and hints for future research. In this sense, not only are positive results significant but counterexamples that point out the algorithm's weaknesses are also important.

You are cordially invited to submit papers related to all aspects of optimization, both theoretical and applicational. This involves (but is not limited to) linear, quadratic, convex, nonconvex, nonlinear, and integer programming; combinatorial optimization; robust optimization; stochastic programming; quasi-Newton methods; interior point methods; successive quadratic programming; derivative-free methods; approximation algorithms; and evolutionary algorithms.

Prof. Dr. Árpád Bűrmen
Prof. Dr. Tadej Tuma
Guest Editors

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