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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
Fabian Pedregosa · 2009-08-17 · via Keep the gradient flowing

This commit introduced a new module in sympy: the refine module. The purpose of this module is to simplify expressions when they are bound to assumptions. For example, if you know that x>0, then you can simplify abs(x) to x. This code was traditionally embedded into the core, but now this will be part of an external module (sympy.refine) upon which the core has no dependencies. In a not very original move, I named the main function in this module refine(). It's syntax is very straightforward: first argument is an expression and second argument are assumptions. Some examples (from isympy): In [1]: refine(1+abs(x), Assume(x, Q.positive)) Out[1]: 1 + x In [2]: refine(exp(I*x*pi), Assume(x, Q.odd)) Out[2]: -1 In [3]: refine(exp(I*x*pi), Assume(x, Q.even)) Out[3]: 1 Right now the module lacks some rules, but the design (very similar to the query module) will make adding these rules an easy task.