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This is an issue for program correctness when objects are tied to non-memory resources, whose release is an externally visible program behavior, such as closing a network connection, releasing a device or closing a file. One garbage collection technique which provides determinism in this regard is reference counting.
Stop-and-copy garbage collection in a Lisp architecture: [1] Memory is divided into working and free memory; new objects are allocated in the former. When it is full (depicted), garbage collection is performed: All data structures still in use are located by pointer tracing and copied into consecutive locations in free memory.
The method SwingUtilities.isEventDispatchThread() or EventQueue.isDispatchThread() can be called to determine if the current thread is the event dispatching thread. The code supplied by means of the invokeLater and invokeAndWait to the EDT must be as quick as possible to prevent freezing.
The actual logic is contained in event-handler routines. These routines handle the events to which the main program will respond. For example, a single left-button mouse-click on a command button in a GUI program may trigger a routine that will open another window, save data to a database or exit the application.
Robot in a wooden maze. A maze-solving algorithm is an automated method for solving a maze.The random mouse, wall follower, Pledge, and Trémaux's algorithms are designed to be used inside the maze by a traveler with no prior knowledge of the maze, whereas the dead-end filling and shortest path algorithms are designed to be used by a person or computer program that can see the whole maze at once.
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The program can then jump to the called subroutine. Producing such code instead of a standard call sequence is called tail-call elimination or tail-call optimization. Tail-call elimination allows procedure calls in tail position to be implemented as efficiently as goto statements, thus allowing efficient structured programming.
The IBM ILOG CPLEX Optimizer solves integer programming problems, very large [3] linear programming problems using either primal or dual variants of the simplex method or the barrier interior point method, convex and non-convex quadratic programming problems, and convex quadratically constrained problems (solved via second-order cone programming, or SOCP).