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Keep the gradient flowing

Policy Gradients Part 1: The REINFORCE Estimator On the Link Between Optimization and Polynomials, Part 6. Optimization Nuggets: Stochastic Polyak Step-size, Part 2 Optimization Nuggets: Stochastic Polyak Step-size On the Convergence of the Unadjusted Langevin Algorithm The Russian Roulette: An Unbiased Estimator of the Limit Notes on the Frank-Wolfe Algorithm, Part III: backtracking line-search On the Link Between Optimization and Polynomials, Part 5 Optimization Nuggets: Implicit Bias of Gradient-based Methods Optimization Nuggets: Exponential Convergence of SGD On the Link Between Optimization and Polynomials, Part 4 On the Link Between Optimization and Polynomials, Part 3 On the Link Between Optimization and Polynomials, Part 2 On the Link Between Polynomials and Optimization, Part 1 How to Evaluate the Logistic Loss and not NaN trying Notes on the Frank-Wolfe Algorithm, Part II: A Primal-dual Analysis Three Operator Splitting Notes on the Frank-Wolfe Algorithm, Part I Optimization inequalities cheatsheet A fully asynchronous variant of the SAGA algorithm Hyperparameter optimization with approximate gradient Lightning v0.1 scikit-learn-contrib, an umbrella for scikit-learn related projects. SAGA algorithm in the lightning library On the consistency of ordinal regression methods Holdout cross-validation generator IPython/Jupyter notebook gallery PyData Paris - April 2015 Data-driven hemodynamic response function estimation Plot memory usage as a function of time
Refine module, proof of concept
Fabian Pedregosa · 2009-07-09 · via Keep the gradient flowing

The 0.6.5 release of SymPy is taking longer than expected because some bugs in the testing framework, so my query module is not merged into trunk (yet). In the meantime, I am implementing a refine module (very little code is available yet). The refine module implements a refine() function (better names accepted) that would work in a very similar way as Mathematica's Refine (http://documents.wolfram.com/mathematica/functions/Refine). It tries to simplify an expression based on it's assumptions. For example: [cc lang="python"] >>> refine(abs(x), Assume(x, positive=True)) x >>> refine(abs(x), Assume(x, negative=True)) -x [/cc] Initial code is in my git repo, branch queries.