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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
Queries and performance
Fabian Pedregosa · 2009-06-23 · via Keep the gradient flowing

After some hacking on the queries module, I finally got it right without the limitations of past versions. You can check it out from my repo http://fa.bianp.net/git/sympy.git, branch master. It now relies even more on logic.inference.satisfiable(), which is just an implementation of the DPLL algorithm. Bad news is that (my implementation of ) dpll_satisfiable() is SLOW, so inevitably queries are SLOW. But everything is not lost, since the algorithm is quite fast, and in fact other variants of the algorithm (MiniSAT) perform 6600x times faster than my implementation on medium-sized problems (60 variables, 170 clauses). So this looks like something smells bad on the programming side ... However, I spent the day profiling the function (link to source code used for profiling) without much success