A reference implementation in Fortran 77 (and with a Fortran 90 interface).In February 2011, some of the authors of the original L-BFGS-B code posted a major update (version 3.0). The L-BFGS-B variant also exists as ACM TOMS algorithm 778.Notable non open source implementations include: SciPy's optimization module's minimize method also includes an option to use L-BFGS-B.R's optim general-purpose optimizer routine uses the L-BFGS-B method.ALGLIB implements L-BFGS in C++ and C# as well as a separate box/linearly constrained version, BLEIC.Notable open source implementations include: It has been shown that O-LBFGS has a global almost sure convergence while the online approximation of BFGS (O-BFGS) is not necessarily convergent. Similar to stochastic gradient descent, this can be used to reduce the computational complexity by evaluating the error function and gradient on a randomly drawn subset of the overall dataset in each iteration. present an online approximation to both BFGS and L-BFGS. After an L-BFGS step, the method allows some variables to change sign, and repeats the process. The algorithm's target problem is to minimize f ( x ) term becomes a smooth linear term which can be handled by L-BFGS. It is a popular algorithm for parameter estimation in machine learning. Limited-memory BFGS ( L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory.
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