EcoSearch: A Constant-Delay Best-First Search Algorithm for Program Synthesis
- URL: http://arxiv.org/abs/2412.17330v1
- Date: Mon, 23 Dec 2024 06:48:47 GMT
- Title: EcoSearch: A Constant-Delay Best-First Search Algorithm for Program Synthesis
- Authors: Théo Matricon, Nathanaël Fijalkow, Guillaume Lagarde,
- Abstract summary: We present a new best-first search algorithm called EcoSearch, which is the first constant-delay algorithm for pre-generation cost function.
We observe that EcoSearch outperforms its predecessors on two classic domains.
- Score: 4.523850593225294
- License:
- Abstract: Many approaches to program synthesis perform a combinatorial search within a large space of programs to find one that satisfies a given specification. To tame the search space blowup, previous works introduced probabilistic and neural approaches to guide this combinatorial search by inducing heuristic cost functions. Best-first search algorithms ensure to search in the exact order induced by the cost function, significantly reducing the portion of the program space to be explored. We present a new best-first search algorithm called EcoSearch, which is the first constant-delay algorithm for pre-generation cost function: the amount of compute required between outputting two programs is constant, and in particular does not increase over time. This key property yields important speedups: we observe that EcoSearch outperforms its predecessors on two classic domains.
Related papers
- A Three-Stage Algorithm for the Closest String Problem on Artificial and Real Gene Sequences [39.58317527488534]
Closest String Problem is an NP-hard problem that aims to find a string that has the minimum distance from all sequences that belong to the given set of strings.
In this paper, we introduce a three-stage algorithm that comprises the following process: first, we apply a novel alphabet pruning method to reduce the search space for effectively finding promising search regions.
Second, a variant of beam search to find a solution is employed. This method utilizes a newly developed guiding function based on an expected distance score of partial solutions.
arXiv Detail & Related papers (2024-07-17T21:26:27Z) - Program Synthesis with Best-First Bottom-Up Search [14.146892127555217]
We show that current state-of-the-art cost-guided BUS algorithms suffer from a common problem: they can lose useful information.
We introduce a novel best-first bottom-up search algorithm, which we call Bee Search, that does not suffer information loss and is able to perform cost-guided bottom-up synthesis in a best-first manner.
arXiv Detail & Related papers (2023-10-06T15:44:47Z) - HUSP-SP: Faster Utility Mining on Sequence Data [48.0426095077918]
High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity.
We design a compact structure called sequence projection (seqPro) and propose an efficient algorithm, namely discovering high-utility sequential patterns with the seqPro structure (HUSP-SP)
Experimental results on both synthetic and real-life datasets show that HUSP-SP can significantly outperform the state-of-the-art algorithms in terms of running time, memory usage, search space pruning efficiency, and scalability.
arXiv Detail & Related papers (2022-12-29T10:56:17Z) - Efficient Non-Parametric Optimizer Search for Diverse Tasks [93.64739408827604]
We present the first efficient scalable and general framework that can directly search on the tasks of interest.
Inspired by the innate tree structure of the underlying math expressions, we re-arrange the spaces into a super-tree.
We adopt an adaptation of the Monte Carlo method to tree search, equipped with rejection sampling and equivalent- form detection.
arXiv Detail & Related papers (2022-09-27T17:51:31Z) - CrossBeam: Learning to Search in Bottom-Up Program Synthesis [51.37514793318815]
We propose training a neural model to learn a hands-on search policy for bottom-up synthesis.
Our approach, called CrossBeam, uses the neural model to choose how to combine previously-explored programs into new programs.
We observe that CrossBeam learns to search efficiently, exploring much smaller portions of the program space compared to the state-of-the-art.
arXiv Detail & Related papers (2022-03-20T04:41:05Z) - Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
This paper investigates the classical integer least-squares problem which estimates signals integer from linear models.
The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning.
We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
arXiv Detail & Related papers (2021-01-07T08:00:02Z) - Searching for a Search Method: Benchmarking Search Algorithms for
Generating NLP Adversarial Examples [10.993342896547691]
We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks.
We perform a fine-grained analysis of three elements relevant to search: search algorithm, search space, and search budget.
arXiv Detail & Related papers (2020-09-09T17:04:42Z) - BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration [72.88493072196094]
We present a new synthesis approach that leverages learning to guide a bottom-up search over programs.
In particular, we train a model to prioritize compositions of intermediate values during search conditioned on a set of input-output examples.
We show that the combination of learning and bottom-up search is remarkably effective, even with simple supervised learning approaches.
arXiv Detail & Related papers (2020-07-28T17:46:18Z) - Best-First Beam Search [78.71330480725668]
We show that the standard implementation of beam search can be made up to 10x faster in practice.
We propose a memory-reduced variant of Best-First Beam Search, which has a similar beneficial search bias in terms of downstream performance.
arXiv Detail & Related papers (2020-07-08T05:56:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.