We take advantage of recent advances in optimization methods and computer hardware to identify globally optimal solutions of product line design problems that are too large for complete enumeration.
Advances in optimization techniques can be used to enhance the performance of turbines in various applications.
On the electrocatalytic side, there have been advances in optimization of known rhenium motifs sometimes in conjunction with silicon photoelectrodes giving enhanced catalytic current and stability.
Among ongoing advances in optimization methods and tools, many applications including mathematical modeling with constraints in different areas remain challenges in industrial design environments.
There is an increased use of problem representations (i.e. superstructures in synthesis problems; networks in route problems; graphs; ordered graphs in various systems representations) following on significant advances in optimization technologies that hold capabilities to solve, robustly, large-scale problems.
And also unlike the conventional approach, the standard GP model is solved by recent advances in optimization tools (Boyd and Michael 2009).
With the advances in optimization algorithms and computing power, research has increased greatly since the 1980s (Sequeira et al. 2002; Chacón et al. 2004; Barragán et al. 2005; Gunnerud and Foss 2010; Knudsen and Foss 2013; Tavallali et al. 2013).
For example, cost-benefit analysis has long dominated studies of resource allocation in DRRG (Jonkman et al. 2004; Mechler 2005; Rose et al. 2007; Li 2012; Kull et al. 2013; Liel and Deierlein 2013), and many recent advances in optimization theory are rarely introduced or attempted.
This stochastic nature requires advances in optimization methods, and a number of researches are already providing useful approaches, such as in parameter estimation in stochastic biochemical reactions [ 58] or in the optimization of stochastic gene network models [ 73].
Variational methods pose the problem of computing relevant posterior distributions as an optimization problem, allowing us to build on recent advances in optimization theory to develop fast inference tools.
While, all the aforementioned algorithms are only sub-optimal, the centralized optimal algorithm [2] Monotonic optimization-bAsed poweR controL (MARL), proposed by Qian et al., is based on recent advances in global optimization, including monotonic optimization and fractional programming.
