We investigate risk-averse stochastic optimization problems with a risk-shaping constraint in the form of a stochastic-order relation. Both univariate and multivariate orders are considered. We extend ...

Conventional quantum algorithms are not feasible for solving combinatorial optimization problems (COPs) with constraints in the operation time of quantum computers. To address this issue ...

On Friday the 25th of May 2018, M.Sc. Jeremias Berg will defend his doctoral thesis on Solving Optimization Problems via Maximum Satisfiablity in Kumpula. The thesis is a part of the research done at ...

Annealing processors are more energy efficient and quicker at solving mathematical optimization problems than PCs. Researchers at Tokyo University of Science have now developed a new approach to ...

This paper describes mathematical and software developments needed for the effective solution of differential algebraic equations of index 1 in the integrated computing environment MATLAB and the ...

The researchers say that the innovations in math and algorithms they have developed are as critical as the machine itself in solving optimization problems. The novel type of algorithm being used in ...

It’s widely known that quantum computers are well suited for solving optimization problems, an application which is a front-runner for showing performance improvements over classical computation ...