Many statistical methods rely on numerical optimization to estimate a model’s parameters. Unfortunately, conventional algorithms sometimes fail. Even when they do converge, there is no assurance that they have found the global, rather than a local, optimum. We test a new optimization algorithm, simulated annealing, on four econometric problems and compare it to three common conventional algorithms. Not only can simulated annealing find the global optimum, it is also less likely to fail on difficult functions because it is a very robust algorithm. The promise of simulated annealing is demonstrated on the four econometric problems.

Sandia National Laboratories applied a systems approach to identifying innovative biomedical technologies with the potential to reduce U.S. health care delivery costs while maintaining care quality. This study was conducted by implementing both top-down and bottom-up strategies. The top-down approach used prosperity gaming methodology to identify future health care delivery needs. This effort provided roadmaps for the development and integration of technology to meet perceived care delivery requirements. The bottom-up approach identified and ranked interventional therapies employed in existing care delivery systems for a host of health-related conditions. Economic analysis formed the basis for development of care pathway interaction models for two of the most pervasive, chronic disease/disability conditions: coronary artery disease (CAD) and benign prostatic hypertrophy (BPH). Societal cost-benefit relationships based on these analyses were used to evaluate the effect of emerging techn...