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Many optimization algorithms have been developed to solve unconstrained problems This section summarizes only a few that support the material presented in this book The reader is referred to [201, 262, 263, 317, 806] for more detail

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Local search methods follow the same basic structure as given in Algorithm A1 A starting point, x(0), is selected, and its quality evaluated Then, iteratively a search direction is determined and a move is made in that direction Algorithm A1 General Local Search Algorithm Find starting point x(0) S; t = 0; repeat Evaluate f (x(t)); Calculate a search direction, q(t); Calculate step length (t); Set x(t + 1) to x(t) + (t)q(t); t = t + 1; until stopping condition is true; Return x(t) as the solution;

Click the last line of code and double-click stopDrag from the Movie Clip Control menu Your code should now look like this:

Search directions and step lengths can be determined using steepest gradient descent, conjugate gradients, or Newton methods (amongst many others)

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Beam Search Beam search (BS) is a classical tree local search method [650] The search method allows the extension of partial, or approximate, solutions in a number of ways as determined by tree branches Each step of the algorithm extends a partial solution from the set B (referred to as the beam) in at most nc possible ways A newly constructed solution is stored in the set of complete solutions, Bc , if that solution represents a complete solution If not, the new partial solution is added to the set Bp of partial solutions At the end of each iteration, a new beam, B, is created by selecting nb solutions from Bp , where nb is the beam width BS uses a lower bound on the objective function value to select partial solutions from Bp The lower bound speci es the minimum objective function value for any complete solution that can be constructed from a partial solution Partial solutions are usually extended using a deterministic greedy policy, which is based on a weighting function that assigns weights to all the possible extensions

Osprey Video Division of the ViewCast Corporation (the Osprey-220 and Osprey-500) at: wwwviewcastcom Winnov (the Videum-II) at: wwwwinnovcom Pinnacle at: wwwpinnaclesyscom Antex at: wwwantexcom

Tabu Search Tabu search (TS) is an iterative neighborhood search algorithm [319, 362], where the neighborhood changes dynamically TS enhances local search by actively avoiding points in the search space already visited By avoiding already visited points, loops in search trajectories are avoided and local optima can be escaped The main feature

on(press){ startDrag(""); on(release) } stopDrag();

of TS is the use of an explicit memory A simple TS usually implements two forms of memory: A frequency-based memory, which maintains information about how often a search point has been visited (or how often a move has been made) during a speci ed time interval A recency-based memory, which maintains information about how recently a search point has been visited (or how recently a move has been made) Recency is based on the iteration at which the event occurred If, for example, the frequency count of a search point exceeds a given threshold, then that point is classi ed as being tabu for the next cycle of iterations Positions speci ed in the tabu list are excluded from the neighborhood of candidate positions that can be visited from the current position Positions remain in the tabu list for a speci ed time period The following may be used to terminate TS: the neighborhood is empty, ie all possible neighboring points have already been visited, or when the number of iterations since the last improvement is larger than a speci ed threshold

While it is not necessary for audio and video to be on the same device, there are a few advantages to this:

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