Dynamic programming is an important algorithm design technique. It is used for solving problems whose solutions involve recursively solving subproblems that share subsubproblems. While a straightforward recursive program solves common subsubproblems repeatedly and often takes exponential time, a dynamic programming algorithm solves every subsubproblem just once, saves the result, reuses it when the subsubproblem is encountered again, and takes polynomial time. This paper describes a systematic method for transforming programs written as straightforward recursions into programs that use dynamic programming. The method extends the original program to cache all possibly computed values, incrementalizes the extended program with respect to an input increment to use and maintain all cached results, prunes out cached results that are not used in the incremental computation, and uses the resulting incremental program to form an optimized new program. Incrementalization statically exploits semantics of both control structures and data structures and maintains as invariants equalities characterizing cached results. The principle underlying incrementalization is general for achieving drastic program speedups. Compared with previous methods that perform memoization or tabulation, the method based on incrementalization is more powerful and systematic. It has been implemented and applied to numerous problems and succeeded on all of them. 1

Abstract. Dynamic programming is a classical programming technique, applicable in a wide variety of domains such as stochastic systems analysis, operations research, combinatorics of discrete structures, flow problems, parsing of ambiguous languages, and biosequence analysis. Little methodology has hitherto been available to guide the design of such algorithms. The matrix recurrences that typically describe a dynamic programming algorithm are difficult to construct, error-prone to implement, and, in nontrivial applications, almost impossible to debug completely. This article introduces a discipline designed to alleviate this problem. We describe an algebraic style of dynamic programming over sequence data. We define its formal framework, based on a combination of grammars and algebras, and including a formalization of Bellman’s Principle. We suggest a language used for algorithm design on a convenient level of abstraction. We outline three ways of implementing this language, including an embedding in a lazy functional language. The workings of the

Given are a directed edge-labelled graph G with a distinguished node n0, and a regular expression P which may contain variables. We wish to compute all substitutions φ (of symbols for variables), together with all nodes n such that all paths n0 → n are in φ(P). We derive an algorithm for this problem using relational algebra, and show how it may be implemented in Prolog. The motivation for the problem derives from a declarative framework for specifying compiler optimisations. 1 Bob Paige and IFIP WG 2.1 Bob Paige was a long-standing member of IFIP Working Group 2.1 on Algorithmic Languages and Calculi. In recent years, the main aim of this group has been to investigate the derivation of algorithms from specifications by program transformation. Already in the mid-eighties, Bob was way ahead of the pack: instead of applying transformational techniques to well-worn examples, he was applying his theories of program transformation to new problems, and discovering new algorithms [16, 48, 52]. The secret of his success lay partly in his insistence on the study of general algorithm design strategies (in particular

Abstract. Dynamic programming is a classic programming technique, applicable in a wide variety of domains, like stochastic systems analysis, operations research, combinatorics of discrete structures, flow problems, parsing with ambiguous grammars, or biosequence analysis. Yet, no methodology is available for designing such algorithms. The matrix recurrences that typically describe a dynamic programming algorithm are difficult to construct, error-prone to implement, and almost impossible to debug. This article introduces an algebraic style of dynamic programming over sequence data. We define the formal framework including a formalization of Bellman’s principle, specify an executable specification language, and show how algorithm design decisions and tuning for efficiency can be described on a convenient level of abstraction.

Database performance is directly linked to the allocation of the resources used by the Database Management System (DBMS). The complex relationships between numerous DBMS resources make problem diagnosis and performance tuning complex and time-consuming tasks. Costly Database Administrators (DBAs) are currently needed to initially tune a DBMS for performance and then to retune the DBMS as the database grows and workloads change. Automatic diagnosis and resource management removes the need for DBAs, greatly reducing the cost of ownership for the DBMS. An automated system also allows the DBMS to respond more quickly to changes in the workload as performance can be monitored 24 hours a day. An automated diagnosis and resource management system allows the DBMS to improve performance for both static and dynamic workloads. One of the key issues in automatic resource management is the capability of the system to diagnose resource problems. Diagnosis of the resource allocation problem is the first step in the process of tuning the resources. In this dissertation, we propose an automatic

The main contribution of this thesis is a study of the dynamic programming and greedy strategies for solving combinatorial optimization problems. The study is carried out in the context of a calculus of relations, and generalises previous work by using a loop operator in the imperative programming style for generating feasible solutions, rather than the fold and unfold operators of the functional programming style. The relationship between fold operators and loop operators is explored, and it is shown how to convert from the former to the latter. This fresh approach provides additional insights into the relationship between dynamic programming and greedy algorithms, and helps to unify previously distinct approaches to solving combinatorial optimization problems. Some of the solutions discovered are new and solve problems which had previously proved difficult. The material is illustrated with a selection of problems and solutions that is a mixture of old and new. Another contribution is the invention of a new calculus, called the graph calculus, which is a useful tool for reasoning in the relational calculus and other non-relational calculi. The graph

The derivation of certain algorithms can be seen as a hybrid form of dynamic programming and the greedy paradigm. We present a generic theorem about such algorithms, and show how it can be applied to the derivation of an algorithm for data compression. 1 Introduction Dynamic programming is a technique for solving optimisation problems. A typical dynamic programming algorithm proceeds by decomposing the input in all possible ways, recursively solving the subproblems, and combining optimal solutions to subproblems into an optimal solution for the whole problem. The greedy paradigm is also a technique for solving optimisation problems and differs from dynamic programming in that only one decomposition of the input is considered. Such a decomposition is usually chosen to maximise some objective function, and this explains the term `greedy'. In our earlier work, we have characterised the use of dynamic programming and the greedy paradigm, using the categorical calculus of relations to der...

Abstract. Dynamic programming is a classic programming technique, applicable in a wide variety of domains, like stochastic systems analysis, operations research, combinatorics of discrete structures, flow problems, parsing ambiguous languages, or biosequence analysis. Yet, heretofore no methodology was available guiding the design of such algorithms. The matrix recurrences that typically describe a dynamic programming algorithm are difficult to construct, error-prone to implement, and almost impossible to debug. This article introduces an algebraic style of dynamic programming over sequence data. We define its formal framework including a formalization of Bellman’s principle. We suggest a language for algorithm design on a convenient level of abstraction. We outline three ways of implementation, including an embedding in a lazy functional language. The workings of the new method are illustrated by a series of examples from diverse areas of computer science.

Given the inorder and preorder traversal of a binary tree whose labels are all distinct, one can reconstruct the tree. This article examines two existing algorithms for rebuilding the tree in a functional framework, using existing theory on function inversion. We also present a new, although complicated, algorithm by trying another possibility not explored before.

On the first hand, this report studies the class of Greedy Algorithms in order to find an as systematic as possible strategy that could be applied to the specification of some problems to lead to a correct program solving that problem. On the other hand, the standard formalisms underlying the Greedy Algorithms (matroid, greedoid and matroid embedding) which are dependent on the particular type set are generalized to a formalism independent of any data type based on an algebraic specification setting.