The Knapsack Problem with Neighbour Constraints

We study a constrained version of the knapsack problem in which dependencies between items are given by the adjacencies of a graph. In the 1-neighbour knapsack problem, an item can be selected only if at least one of its neighbours is also selected. In the all-neighbours knapsack problem, an item can be selected only if all its neighbours are also selected. We give approximation algorithms and hardness results when the nodes have both uniform and arbitrary weight and profit functions, and when the dependency graph is directed and undirected.Comment: Full version of IWOCA 2011 pape

LAD models, trees and an analog of the fundamental theorem of arithmetic

International audienceMotivated by applications of Logical Analysis Data (LAD) in medical contexts, original discrete optimization problems are proposed. When a patient arrives with a presumption of a disease, he is submitted to a sequence of tests. From one patient to another, the tests allowing to detect the disease may vary. A subset of tests whose results detect the disease in a given part of the population is called a pattern, which has its own prevalence in the population. If there is only a limited number of tests that can be done, which ones must be selected in order to maximize the number of spotted patients ? Or, if each test has a cost, in which order the tests have to be done, in order to minimize the cost ? It is the kind of questions that are investigated in this paper. For various special cases, polynomial algorithms are proposed, especially when the hypergraph whose vertices are the tests and whose edges are the patterns is a tree graph. One of these questions involves a criterion which is not a number but a sequence of numbers. The objective is then to find the best sequence for the lexicographic order. To solve this question, a new product on finite sequences is defined, namely the maximum shuffle product, which maps two sequences to their shuffle that is maximal for the lexicographic order. Surprisingly, this product leads to a theorem similar to the fundamental theorem of arithmetic: every sequence can be written uniquely as the product of prime sequences, with the suitable definition of prime sequences

Approximate and Exact Merging of Knapsack Constraints with Cover Inequalities

This paper presents both approximate and exact merged knapsack cover inequalities, a class of cutting planes for knapsack and multiple knapsack integer programs. These inequalities combine the information from knapsack constraints and cover inequalities. Approximate merged knapsack cover inequalities can be generated through a O(n log n) algorithm, where n is the number of variables. This class of inequalities can be strengthened to an exact version with a pseudo-polynomial time algorithm. Computational experiments demonstrate an average improvement of approximately 8% in solution time and 5% in the number of ticks from CPLEX when approximate merged knapsack cover inequalities are implemented as preprocessing cuts to solve some benchmark multiple knapsack problems. Furthermore, exact merged knapsack cover inequalities improve the solution time and number of ticks of some random multiple knapsack instances by 15% and 5%, respectively

Subset sum problems with digraph constraints

We introduce and study optimization problems which are related to the well-known Subset Sum problem. In each new problem, a node-weighted digraph is given and one has to select a subset of vertices whose total weight does not exceed a given budget. Some additional constraints called digraph constraints and maximality need to be satisfied. The digraph constraint imposes that a node must belong to the solution if at least one of its predecessors is in the solution. An alternative of this constraint says that a node must belong to the solution if all its predecessors are in the solution. The maximality constraint ensures that no superset of a feasible solution is also feasible. The combination of these constraints provides four problems. We study their complexity and present some approximation results according to the type of input digraph, such as directed acyclic graphs and oriented trees

Attention and Sensor Planning in Autonomous Robotic Visual Search

This thesis is concerned with the incorporation of saliency in visual search and the development of sensor planning strategies for visual search. The saliency model is a mixture of two schemes that extracts visual clues regarding the structure of the environment and object specific features. The sensor planning methods, namely Greedy Search with Constraint (GSC), Extended Greedy Search (EGS) and Dynamic Look Ahead Search (DLAS) are approximations to the optimal solution for the problem of object search, as extensions to the work of Yiming Ye. Experiments were conducted to evaluate the proposed methods. They show that by using saliency in search a performance improvement up to 75% is attainable in terms of number of actions taken to complete the search. As for the planning strategies, the GSC algorithm achieved the highest detection rate and the best efficiency in terms of cost it incurs to explore every percentage of an environment

The application of the in-tree knapsack problem to routing prefix caches

Modern routers use specialized hardware, such as Ternary Content Addressable Memory (TCAM), to solve the Longest Prefix Matching Problem (LPMP) quickly. Due to the fact that TCAM is a non-standard type of memory and inherently parallel, there are concerns about its cost and power consumption. This problem is exacerbated by the growth in routing tables, which demands ever larger TCAMs. To reduce the size of the TCAMs in a distributed forwarding environment, a batch caching model is proposed and analyzed. The problem of determining which routing prefixes to store in the TCAMs reduces to the In-tree Knapsack Problem (ITKP) for unit weight vertices in this model. Several algorithms are analysed for solving the ITKP, both in the general case and when the problem is restricted to unit weight vertices. Additionally, a variant problem is proposed and analyzed, which exploits the caching model to provide better solutions. This thesis concludes with discussion of open problems and future experimental work

Approximation algorithms for stochastic scheduling problems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. [109]-113).In this dissertation we study a broad class of stochastic scheduling problems characterized by the presence of hard deadline constraints. The input to such a problem is a set of jobs, each with an associated value, processing time, and deadline. We would like to schedule these jobs on a set of machines over time. In our stochastic setting, the processing time of each job is random, known in advance only as a probability distribution (and we make no assumptions about the structure of this distribution). Only after a job completes do we know its actual "instantiated" processing time with certainty. Each machine can process only a singe job at a time, and each job must be assigned to only one machine for processing. After a job starts processing we require that it must be allowed to complete - it cannot be canceled or "preempted" (put on hold and resumed later). Our goal is to devise a scheduling policy that maximizes the expected value of jobs that are scheduled by their deadlines. A scheduling policy observes the state of our machines over time, and any time a machine becomes available for use, it selects a new job to execute on that machine. Scheduling policies can be classified as adaptive or non-adaptive based on whether or not they utilize information learned from the instantiation of processing times of previously-completed jobs in their future scheduling decisions. A novel aspect of our work lies in studying the benefit one can obtain through adaptivity, as we show that for all of our stochastic scheduling problems, adaptivity can only allow us to improve the expected value obtained by an optimal policy by at most a small constant factor. All of the problems we consider are at least NP-hard since they contain the deterministic 0/1 knapsack problem as a special case. We therefore seek to develop approximation algorithms: algorithms that run in polynomial time and compute a policy whose expected value is provably close to that of an optimal adaptive(cont.) policy. For all the problems we consider, we can approximate the expected value obtained by an optimal adaptive policy to within a small constant factor (which depends on the problem under consideration, but is always less than 10). A small handful of our results are pseudo-approximation algorithms, delivering an approximately optimal policy that is feasible with respect to a slightly expanded set of deadlines. Our algorithms utilize a wide variety of techniques, ranging from fairly well-established methods like randomized rounding to more novel techniques such as those we use to bound the expected value obtained by an optimal adaptive policy. In the scheduling literature to date and also in practice, the "deadline" of a job refers to the time by which a job must be completed. We introduce a new model, called the start deadline model, in which the deadline of a job instead governs the time by which we must start the job. While there is no difference between this model and the standard "completion deadline" model in a deterministic setting, we show that for our stochastic problems, one can generally obtain much stronger approximation results with much simpler analyses in the start deadline model. The simplest problem variant we consider is the so-called stochastic knapsack problem, where all jobs share a common deadline and we schedule them on a single machine. The most general variant we consider involves scheduling jobs with individual deadlines on a set of "unrelated" parallel machines, where the value of a job and its processing time distribution can vary depending on the machine to which it is assigned.(cont.) We also discuss algorithms based on dynamic programming for stochastic scheduling problems and their relatives in a discrete-time setting (where processing times are small integers), and we show how to use a new technique from signal processing called zero-delay convolution to improve the running time of dynamic programming algorithms for some of these problems.by Brian Christopher Dean.Ph.D

Partially ordered knapsack and applications to scheduling

In the partially ordered knapsack problem (POK) we are given a set N of items and a partial order ≺P on N. Each item has a size and an associated weight. The objective is to pack a set N′ ⊆ N of maximum weight in a knapsack of bounded size. N′ should be precedence-closed, i.e., be a valid prefix of ≺P. POK is a natural generalization, for which very little is known, of the classical Knapsack problem. In this paper we present both positive and negative results. We give an FPTAS for the important case of a two-dimensional partial order, a class of partial orders which is a substantial generalization of the series-parallel class, and we identify the first non-trivial special case for which a polynomial-time algorithm exists. Our results have implications for approximation algorithms for scheduling precedence-constrained jobs on a single machine to minimize the sum of weighted completion times, a problem closely related to POK. © 2006

Partially-Ordered Knapsack and Applications to Scheduling

In the partially-ordered knapsack problem (POK) we are given a set N of items and a partial order on N. Each item has a size and an associated weight. The objective is to pack a set N # N of maximum weight in a knapsack of bounded size. N # should be precedence-closed, i.e., be a valid prefix of . POK is a natural generalization, for which very little is known, of the classical Knapsack problem. In this paper we present both positive and negative results

Partially-Ordered Knapsack and Applications to Scheduling

In the partially-ordered knapsack problem (POK) we are given a set N of items and a partial order ≺P on N. Each item has a size and an associated weight. The objective is to pack a set N ′ ⊆ N of maximum weight in a knapsack of bounded size. N ′ should be precedence-closed, i.e., be a valid prefix of ≺P. POK is a natural generalization, for which very little is known, of the classical Knapsack problem. In this paper we present both positive and negative results. We give an FPTAS for the important case of a 2-dimensional partial order, a class of partial orders which is a substantial generalization of the series-parallel class, and we identify the first non-trivial special case for which a polynomial-time algorithm exists. We also characterize cases where the natural linear relaxation for POK is useful for approximation but we demonstrate its limitations as well. Our results have implications for approximation algorithms for scheduling precedence-constrained jobs on a single machine to minimize the sum of weighted completion times, a problem closely related to POK