Dynamic programming by polymorphic semiring algebraic shortcut fusion

TL;DR

This paper introduces an algebraic formalism based on semiring polymorphism for systematically deriving, optimizing, and reusing dynamic programming algorithms across various combinatorial problems, ensuring correctness and efficiency.

Contribution

It presents a rigorous algebraic framework for deriving and optimizing DP algorithms using semiring polymorphism, enabling systematic correctness and reusability.

Findings

Formalism applies to diverse problems like Viterbi decoding and probabilistic inference.

Efficient, correct algorithms derived through shortcut fusion.

Demonstrated effectiveness in signal processing, bioinformatics, and reliability engineering.

Abstract

Dynamic programming (DP) is an algorithmic design paradigm for the efficient, exact solution of otherwise intractable, combinatorial problems. However, DP algorithm design is often presented in an ad-hoc manner. It is sometimes difficult to justify algorithm correctness. To address this issue, this paper presents a rigorous algebraic formalism for systematically deriving DP algorithms, based on semiring polymorphism. We start with a specification, construct an algorithm to compute the required solution which is self-evidently correct because it exhaustively generates and evaluates all possible solutions meeting the specification. We then derive, through the use of shortcut fusion, an implementation of this algorithm which is both efficient and correct. We also demonstrate how, with the use of semiring lifting, the specification can be augmented with combinatorial constraints, showing how these constraints can be fused with the algorithm. We furthermore demonstrate how existing DP algorithms for a given combinatorial problem can be abstracted from their original context and re-purposed. This approach can be applied to the full scope of combinatorial problems expressible in terms of semirings. This includes, for example: optimal probability and Viterbi decoding, probabilistic marginalization, logical inference, fuzzy sets, differentiable softmax, relational and provenance queries. The approach, building on ideas from the existing literature on constructive algorithmics, exploits generic properties of polymorphic functions, tupling and formal sums and algebraic simplifications arising from constraint algebras. We demonstrate the effectiveness of this formalism for some example applications arising in signal processing, bioinformatics and reliability engineering. Python software implementing these algorithms can be downloaded from: http://www.maxlittle.net/software/dppolyalg.zip.