Simulated annealing is a computational algorithm for optimization. It mimics the physical process of thermal annealing in which a metal is heated and then slowly cooled to settle into a highly ordered crystal structure. For common metals, the lowest energy state is already known. But the method is useful for other problems where the best state is not known and exhaustively searching all possible states is impractical. The method is applied by modeling the problem as a physical system with structure, energy, and temperature. This Python module implements simulated annealing so that it can be easily applied to a variety of problems. An example program is include to perform simulated annealing of the traveling salesman problem.
JCGO (pronounced as "j-c-go") translates (converts) programs written in Java into platform-independent C code that can be compiled (by third-party tools) into highly-optimized native code for the target platform. JCGO is a powerful solution that enables your desktop, server-side, embedded, mobile, and wireless Java applications to take full advantage of the underlying hardware. In addition, JCGO makes your programs, when compiled to native code, as hard to reverse engineer as if they were written in C/C++. The JCGO translator uses some optimization algorithms that allow, together with optimizations performed by a C compiler, the resulting executable code to reach better performance compared with the traditional Java implementations (based on the Just-In-Time technology). The produced executable does not contain nor require a Java Virtual Machine to execute, so its resource requirements are smaller than that required by a typical Java VM. This also simplifies the process of deployment and distribution of an application.