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 TakÃ¡Ä (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)ã¨ã¯ ã“ã®ã‚·ãƒŸãƒ¥ãƒ¬ãƒ¼ã‚¿ã¯Particle Swarm Optimizationã¨ã¯ã©ã®ã‚ˆã†ãªã‚‚ã®ã‹ã‚’知ã‚ã†ã¨ã„ã†ã‚½ãƒ•ãƒˆã§ã™ï¼Žãã®æ¦‚観ã¯ä»¥ä¸‹ã®ã¨ãŠã‚Šã§ã™ï¼ŽPSOã¯1995å¹´ã«James Kennedyã¨ã„ã†äººã«ã‚ˆã£ã¦æ案ã•ã‚ŒãŸã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã§ã™ï¼Žã“ã“ã§ã®PSOã¯3次元空間を数多ãã®å€‹ä½“ãŒå‹•ã回り，ãã‚Œãžã‚Œã®é–¢æ•°ã®ä¸€ç•ªä½Žã„ã¨ã“ã‚を探ã™ã‚ˆã†ã«ä½œã‚‰ã‚Œã¦ã„ã¾ã™ï¼Žãã‚Œãžã‚Œã®å€‹ä½“ã¯ä½ç½®ãƒ™ã‚¯ãƒˆãƒ«ï¼Œé€Ÿåº¦ãƒ™ã‚¯ãƒˆãƒ«ã¨ï¼Œãã®å€‹ä½“ãŒæœ€è‰¯ã®é©åˆåº¦ã‚’ç²å¾—ã—ãŸå ´æ‰€ã ã‘を記憶ã—ã¦ã„ã¾ã™ï¼Žãã—ã¦å€‹ä½“全体ã®ä¸ã«ãŠã‘る最良ã®é©åˆå€¤ã®å ´æ‰€ã®æƒ…å ±ã‚‚ãžã‚Œãžã‚Œã®å€‹ä½“ãŒå…±æœ‰ã™ã‚‹ã“ã¨ãŒå‡ºæ¥ã¾ã™ï¼Ž ã™ã¹ã¦ã®å€‹ä½“ãŒä¸–代を経るã“ã¨ã«ã‚ˆã‚Šç²å¾—ã•ã‚ŒãŸæœ€ã‚‚優れãŸä½ç½®ã¨ãã‚Œãžã‚Œã®å€‹ä½“ãŒä¸–代を経るã“ã¨ã«ã‚ˆã‚Šç²å¾—ã•ã‚ŒãŸæœ€ã‚‚優れãŸä½ç½®ã®æ–¹å‘ã¸ãã‚Œãžã‚Œã®å€‹ä½“ãŒå¼•ã£å¼µã‚‰ã‚Œã‚‹ã¨è¨€ã†ã¨åˆ†ã‹ã‚Šã‚„ã™ã„ã§ã—ょã†ã‹
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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