惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

美团技术团队
人人都是产品经理
人人都是产品经理
月光博客
月光博客
V
V2EX
WordPress大学
WordPress大学
酷 壳 – CoolShell
酷 壳 – CoolShell
Last Week in AI
Last Week in AI
博客园 - 三生石上(FineUI控件)
小众软件
小众软件
Hugging Face - Blog
Hugging Face - Blog
V
Visual Studio Blog
宝玉的分享
宝玉的分享
雷峰网
雷峰网
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - Franky
博客园 - 聂微东
博客园 - 司徒正美
博客园 - 【当耐特】
爱范儿
爱范儿
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
大猫的无限游戏
大猫的无限游戏
博客园 - 叶小钗
阮一峰的网络日志
阮一峰的网络日志

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
The boolean satisfiability problem
Fabian Pedregosa · 2009-06-15 · via Keep the gradient flowing

Most annoying problem in my implementation of the query system is that it will not solve implications if the implicates are far away from each other. For instance, if the graph of known facts is something like this

Integer ----> Rational --> Real --> Complex
  ^  ^
  |  |
  |   -------
  |         |
Prime      Even
  ^
  |
  |
MersennePrime

Then it will not know how to handle the query: Is x complex assuming it is a Mersenne prime ?. This is because the vertices MersennePrime and Complex are far away from each other and the query function does not load the complete graph of known facts, but rather a small subgraph centered on the assumed facts ... This was done so for efficiency reasons, because in the initial implementation I feared that the graph of known facts could become huge and thus making it unfeasible to search into. But things have changed now. Known facts is not huge at all, roughly having over 20 vertices, so it is feasible to build the complete graph the first time query() is called and store it for future uses. And, most important, we have implemented fast algorithms for the problem of boolean satisfiability (DPLL under sympy.logic.algorithms.dpll), so all is ready to implement these ideas in the following days. Interestingly, there seems to me many open source libraries for solving this problem. One that caught my attention early is MiniSAT, a nice little program written in C++ which is really fast