Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
While Transformers have enabled tremendous progress in various application settings, such architectures still trail behind traditional symbolic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks. This is accomplished by training an encoder-decoder Transformer model to predict the search dynamics of the $A^*$ se
Random coordinate descent - Wikipedia
Randomized (Block) Coordinate Descent Method is an optimization algorithm popularized by Nesterov (2010) and Richtárik and Takáč (2011). The first analysis of this method, when applied to the problem of minimizing a smooth convex function, was performed by Nesterov (2010).[1] In Nesterov's analysis the method needs to be applied to a quadratic perturbation of the original function with an unknown
Modeling Language GNU MathProg
1 Introduction Linear programming problem Model objects Structure of model description GNU MathProg is a modeling language intended for describing linear mathematical programming models.1 Model descriptions written in the GNU MathProg language consist of a set of statements and data blocks constructed by the user from the language elements described in this document. In a process called translatio
Metaheuristic - Wikipedia
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity.[1][2] Metaheuristics
List of metaphor-based metaheuristics - Wikipedia
1.1.1 Simulated annealing (Kirkpatrick et al., 1983)
Particle Swarm Optimization / ソフトウェア / IBA Laboratory
Particle Swarm Optimization(PSO)とは このシミュレータはParticle Swarm Optimizationとはどのようなものかを知ろうというソフトです．その概観は以下のとおりです．PSOは1995年にJames Kennedyという人によって提案されたアルゴリズムです．ここでのPSOは3次元空間を数多くの個体が動き回り，それぞれの関数の一番低いところを探すように作られています．それぞれの個体は位置ベクトル，速度ベクトルと，その個体が最良の適合度を獲得した場所だけを記憶しています．そして個体全体の中における最良の適合値の場所の情報もぞれぞれの個体が共有することが出来ます． すべての個体が世代を経ることにより獲得された最も優れた位置とそれぞれの個体が世代を経ることにより獲得された最も優れた位置の方向へそれぞれの個体が引っ張られると言うと分かりやすいでしょうか
Particle swarm optimization - Wikipedia
In computational science, particle swarm optimization (PSO)[1] is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae ove
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GitHub - facebookresearch/searchformer: Official codebase for the paper "Beyond A* Better Planning with Transformers via Search Dynamics Bootstrapping".
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