Linear & Quadratic Knapsack Optimisation Problem
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15.083 Lecture 21: Approximation Algorithsm I
15.083J Integer Programming and Combinatorial Optimization Fall 2009 Approximation Algorithms I The knapsack problem • Input: nonnegative numbers p1; : : : ; pn; a1; : : : ; an; b. n X max pj xj j=1 n X s.t. aj xj ≤ b j=1 n x 2 Z+ Additive performance guarantees Theorem 1. There is a polynomial-time algorithm A for the knapsack problem such that A(I) ≥ OP T (I) − K for all instances I (1) for some constant K if and only if P = NP. Proof: • Let A be a polynomial-time algorithm satisfying (1). • Let I = (p1; : : : ; pn; a1; : : : ; an; b) be an instance of the knapsack problem. 0 0 0 • Let I = (p1 := (K + 1)p1; : : : ; pn := (K + 1)pn; a1; : : : ; an; b) be a new instance. • Clearly, x∗ is optimal for I iff it is optimal for I0. • If we apply A to I0 we obtain a solution x0 such that p0x∗ − p0x0 ≤ K: • Hence, 1 K px∗ − px0 = (p0x∗ − p0x0) ≤ < 1: K + 1 K + 1 • Since px0 and px∗ are integer, it follows that px0 = px∗, that is x0 is optimal for I. • The other direction is trivial. • Note that this technique applies to any combinatorial optimization problem with linear ob jective function. 1 Approximation algorithms • There are few (known) NP-hard problems for which we can find in polynomial time solutions whose value is close to that of an optimal solution in an absolute sense. (Example: edge coloring.) • In general, an approximation algorithm for an optimization Π produces, in polynomial time, a feasible solution whose objective function value is within a guaranteed factor of that of an optimal solution. -
4. Convex Optimization Problems
Convex Optimization — Boyd & Vandenberghe 4. Convex optimization problems optimization problem in standard form • convex optimization problems • quasiconvex optimization • linear optimization • quadratic optimization • geometric programming • generalized inequality constraints • semidefinite programming • vector optimization • 4–1 Optimization problem in standard form minimize f0(x) subject to f (x) 0, i =1,...,m i ≤ hi(x)=0, i =1,...,p x Rn is the optimization variable • ∈ f : Rn R is the objective or cost function • 0 → f : Rn R, i =1,...,m, are the inequality constraint functions • i → h : Rn R are the equality constraint functions • i → optimal value: p⋆ = inf f (x) f (x) 0, i =1,...,m, h (x)=0, i =1,...,p { 0 | i ≤ i } p⋆ = if problem is infeasible (no x satisfies the constraints) • ∞ p⋆ = if problem is unbounded below • −∞ Convex optimization problems 4–2 Optimal and locally optimal points x is feasible if x dom f and it satisfies the constraints ∈ 0 ⋆ a feasible x is optimal if f0(x)= p ; Xopt is the set of optimal points x is locally optimal if there is an R> 0 such that x is optimal for minimize (over z) f0(z) subject to fi(z) 0, i =1,...,m, hi(z)=0, i =1,...,p z x≤ R k − k2 ≤ examples (with n =1, m = p =0) f (x)=1/x, dom f = R : p⋆ =0, no optimal point • 0 0 ++ f (x)= log x, dom f = R : p⋆ = • 0 − 0 ++ −∞ f (x)= x log x, dom f = R : p⋆ = 1/e, x =1/e is optimal • 0 0 ++ − f (x)= x3 3x, p⋆ = , local optimum at x =1 • 0 − −∞ Convex optimization problems 4–3 Implicit constraints the standard form optimization problem has an implicit -
Metaheuristics1
METAHEURISTICS1 Kenneth Sörensen University of Antwerp, Belgium Fred Glover University of Colorado and OptTek Systems, Inc., USA 1 Definition A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms (Sörensen and Glover, To appear). Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, and ant colony optimization, although many more exist. A problem-specific implementation of a heuristic optimization algorithm according to the guidelines expressed in a metaheuristic framework is also referred to as a metaheuristic. The term was coined by Glover (1986) and combines the Greek prefix meta- (metá, beyond in the sense of high-level) with heuristic (from the Greek heuriskein or euriskein, to search). Metaheuristic algorithms, i.e., optimization methods designed according to the strategies laid out in a metaheuristic framework, are — as the name suggests — always heuristic in nature. This fact distinguishes them from exact methods, that do come with a proof that the optimal solution will be found in a finite (although often prohibitively large) amount of time. Metaheuristics are therefore developed specifically to find a solution that is “good enough” in a computing time that is “small enough”. As a result, they are not subject to combinatorial explosion – the phenomenon where the computing time required to find the optimal solution of NP- hard problems increases as an exponential function of the problem size. Metaheuristics have been demonstrated by the scientific community to be a viable, and often superior, alternative to more traditional (exact) methods of mixed- integer optimization such as branch and bound and dynamic programming. -
Quadratic Multiple Knapsack Problem with Setups and a Solution Approach
Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 – 6, 2012 Quadratic Multiple Knapsack Problem with Setups and a Solution Approach Nilay Dogan, Kerem Bilgiçer and Tugba Saraç Industrial Engineering Department Eskisehir Osmangazi University Meselik 26480, Eskisehir Turkey Abstract In this study, the quadratic multiple knapsack problem that include setup costs is considered. In this problem, if any item is assigned to a knapsack, a setup cost is incurred for the class which it belongs. The objective is to assign each item to at most one of the knapsacks such that none of the capacity constraints are violated and the total profit is maximized. QMKP with setups is NP-hard. Therefore, a GA based solution algorithm is proposed to solve it. The performance of the developed algorithm compared with the instances taken from the literature. Keywords Quadratic Multiple Knapsack Problem with setups, Genetic algorithm, Combinatorial Optimization. 1. Introduction The knapsack problem (KP) is one of the well-known combinatorial optimization problems. There are different types of knapsack problems in the literature. A comprehensive survey which can be considered as a through introduction to knapsack problems and their variants was published by Kellerer et al. (2004). The classical KP seeks to select, from a finite set of items, the subset, which maximizes a linear function of the items chosen, subject to a single inequality constraint. In many real life applications it is important that the profit of a packing also should reflect how well the given items fit together. One formulation of such interdependence is the quadratic knapsack problem. -
IEOR 269, Spring 2010 Integer Programming and Combinatorial Optimization
IEOR 269, Spring 2010 Integer Programming and Combinatorial Optimization Professor Dorit S. Hochbaum Contents 1 Introduction 1 2 Formulation of some ILP 2 2.1 0-1 knapsack problem . 2 2.2 Assignment problem . 2 3 Non-linear Objective functions 4 3.1 Production problem with set-up costs . 4 3.2 Piecewise linear cost function . 5 3.3 Piecewise linear convex cost function . 6 3.4 Disjunctive constraints . 7 4 Some famous combinatorial problems 7 4.1 Max clique problem . 7 4.2 SAT (satisfiability) . 7 4.3 Vertex cover problem . 7 5 General optimization 8 6 Neighborhood 8 6.1 Exact neighborhood . 8 7 Complexity of algorithms 9 7.1 Finding the maximum element . 9 7.2 0-1 knapsack . 9 7.3 Linear systems . 10 7.4 Linear Programming . 11 8 Some interesting IP formulations 12 8.1 The fixed cost plant location problem . 12 8.2 Minimum/maximum spanning tree (MST) . 12 9 The Minimum Spanning Tree (MST) Problem 13 i IEOR269 notes, Prof. Hochbaum, 2010 ii 10 General Matching Problem 14 10.1 Maximum Matching Problem in Bipartite Graphs . 14 10.2 Maximum Matching Problem in Non-Bipartite Graphs . 15 10.3 Constraint Matrix Analysis for Matching Problems . 16 11 Traveling Salesperson Problem (TSP) 17 11.1 IP Formulation for TSP . 17 12 Discussion of LP-Formulation for MST 18 13 Branch-and-Bound 20 13.1 The Branch-and-Bound technique . 20 13.2 Other Branch-and-Bound techniques . 22 14 Basic graph definitions 23 15 Complexity analysis 24 15.1 Measuring quality of an algorithm . -
Approximate Algorithms for the Knapsack Problem on Parallel Computers
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector INFORMATION AND COMPUTATION 91, 155-171 (1991) Approximate Algorithms for the Knapsack Problem on Parallel Computers P. S. GOPALAKRISHNAN IBM T. J. Watson Research Center, P. 0. Box 218, Yorktown Heights, New York 10598 I. V. RAMAKRISHNAN* Department of Computer Science, State University of New York, Stony Brook, New York 11794 AND L. N. KANAL Department of Computer Science, University of Maryland, College Park, Maryland 20742 Computingan optimal solution to the knapsack problem is known to be NP-hard. Consequently, fast parallel algorithms for finding such a solution without using an exponential number of processors appear unlikely. An attractive alter- native is to compute an approximate solution to this problem rapidly using a polynomial number of processors. In this paper, we present an efficient parallel algorithm for hnding approximate solutions to the O-l knapsack problem. Our algorithm takes an E, 0 < E < 1, as a parameter and computes a solution such that the ratio of its deviation from the optimal solution is at most a fraction E of the optimal solution. For a problem instance having n items, this computation uses O(n51*/&3’2) processors and requires O(log3 n + log* n log( l/s)) time. The upper bound on the processor requirement of our algorithm is established by reducing it to a problem on weighted bipartite graphs. This processor complexity is a signiti- cant improvement over that of other known parallel algorithms for this problem. 0 1991 Academic Press, Inc 1. -
Bin Completion Algorithms for Multicontainer Packing, Knapsack, and Covering Problems
Journal of Artificial Intelligence Research 28 (2007) 393-429 Submitted 6/06; published 3/07 Bin Completion Algorithms for Multicontainer Packing, Knapsack, and Covering Problems Alex S. Fukunaga [email protected] Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena, CA 91108 USA Richard E. Korf [email protected] Computer Science Department University of California, Los Angeles Los Angeles, CA 90095 Abstract Many combinatorial optimization problems such as the bin packing and multiple knap- sack problems involve assigning a set of discrete objects to multiple containers. These prob- lems can be used to model task and resource allocation problems in multi-agent systems and distributed systms, and can also be found as subproblems of scheduling problems. We propose bin completion, a branch-and-bound strategy for one-dimensional, multicontainer packing problems. Bin completion combines a bin-oriented search space with a powerful dominance criterion that enables us to prune much of the space. The performance of the basic bin completion framework can be enhanced by using a number of extensions, in- cluding nogood-based pruning techniques that allow further exploitation of the dominance criterion. Bin completion is applied to four problems: multiple knapsack, bin covering, min-cost covering, and bin packing. We show that our bin completion algorithms yield new, state-of-the-art results for the multiple knapsack, bin covering, and min-cost cov- ering problems, outperforming previous algorithms by several orders of magnitude with respect to runtime on some classes of hard, random problem instances. For the bin pack- ing problem, we demonstrate significant improvements compared to most previous results, but show that bin completion is not competitive with current state-of-the-art cutting-stock based approaches. -
Solving Packing Problems with Few Small Items Using Rainbow Matchings
Solving Packing Problems with Few Small Items Using Rainbow Matchings Max Bannach Institute for Theoretical Computer Science, Universität zu Lübeck, Lübeck, Germany [email protected] Sebastian Berndt Institute for IT Security, Universität zu Lübeck, Lübeck, Germany [email protected] Marten Maack Department of Computer Science, Universität Kiel, Kiel, Germany [email protected] Matthias Mnich Institut für Algorithmen und Komplexität, TU Hamburg, Hamburg, Germany [email protected] Alexandra Lassota Department of Computer Science, Universität Kiel, Kiel, Germany [email protected] Malin Rau Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France [email protected] Malte Skambath Department of Computer Science, Universität Kiel, Kiel, Germany [email protected] Abstract An important area of combinatorial optimization is the study of packing and covering problems, such as Bin Packing, Multiple Knapsack, and Bin Covering. Those problems have been studied extensively from the viewpoint of approximation algorithms, but their parameterized complexity has only been investigated barely. For problem instances containing no “small” items, classical matching algorithms yield optimal solutions in polynomial time. In this paper we approach them by their distance from triviality, measuring the problem complexity by the number k of small items. Our main results are fixed-parameter algorithms for vector versions of Bin Packing, Multiple Knapsack, and Bin Covering parameterized by k. The algorithms are randomized with one-sided error and run in time 4k · k! · nO(1). To achieve this, we introduce a colored matching problem to which we reduce all these packing problems. The colored matching problem is natural in itself and we expect it to be useful for other applications. -
CS-258 Solving Hard Problems in Combinatorial Optimization Pascal
CS-258 Solving Hard Problems in Combinatorial Optimization Pascal Van Hentenryck Course Overview Study of combinatorial optimization from a practitioner standpoint • the main techniques and tools • several representative problems What is expected from you: • 7 team assignments (2 persons team) • Short write-up on the assignments • Final presentation of the results What you should not do in this class? • read papers/books/web/... on combinatorial optimization • Read the next slide during class Course Overview January 31: Knapsack * Assignment: Coloring February 7: LS + CP + LP + IP February 14: Linear Programming * Assignment: Supply Chain February 28: Integer Programming * Assignment: Green Zone March 7: Travelling Salesman Problem * Assignment: TSP March 14: Advanced Mathematical Programming March 21: Constraint Programming * Assignment: Nuclear Reaction April 4: Constraint-Based Scheduling April 11: Approximation Algorithms (Claire) * Assignment: Operation Hurricane April 19: Vehicle Routing April 25: Advanced Constraint Programming * Assignment: Special Ops. Knapsack Problem n c x max ∑ i i i = 1 subject to: n ∑ wi xi ≤ l i = 1 xi ∈ [0, 1] NP-Complete Problems Decision problems Polynomial-time verifiability • We can verify if a guess is a solution in polynomial time Hardest problem in NP (NP-complete) • If I solve one NP-complete problem, I solve them all Approaches Exponential Algorithms (sacrificing time) • dynamic programming • branch and bound • branch and cut • constraint programming Approximation Algorithms (sacrificing quality) -
Introduction to Integer Programming – Integer Programming Models
15.053/8 March 14, 2013 Introduction to Integer Programming – Integer programming models 1 Quotes of the Day “Somebody who thinks logically is a nice contrast to the real world.” -- The Law of Thumb “Take some more tea,” the March Hare said to Alice, very earnestly. “I’ve had nothing yet,” Alice replied in an offended tone, “so I can’t take more.” “You mean you can’t take less,” said the Hatter. “It’s very easy to take more than nothing.” -- Lewis Carroll in Alice in Wonderland 2 Combinatorial optimization problems INPUT: A description of the data for an instance of the problem FEASIBLE SOLUTIONS: there is a way of determining from the input whether a given solution x’ (assignment of values to decision variables) is feasible. Typically in combinatorial optimization problems there is a finite number of possible solutions. OBJECTIVE FUNCTION: For each feasible solution x’ there is an associated objective f(x’). Minimization problem. Find a feasible solution x* that minimizes f( ) among all feasible solutions. 3 Example 1: Traveling Salesman Problem INPUT: a set N of n points in the plane FEASIBLE SOLUTION: a tour that passes through each point exactly once. OBJECTIVE: minimize the length of the tour. 4 Example 2: Balanced Partition INPUT: A set of positive integers a1, …, an FEASIBLE SOLUTION: a partition of {1, 2, … n} into two disjoint sets S and T. – S ∩ T = ∅, S∪T = {1, … , n} OBJECTIVE : minimize | ∑i∈S ai - ∑i∈T ai | Example: 7, 10, 13, 17, 20, 22 These numbers sum to 89 The best split is {10, 13, 22} and {7, 17, 20}. -
1 Load Balancing / Multiprocessor Scheduling
CS 598CSC: Approximation Algorithms Lecture date: February 4, 2009 Instructor: Chandra Chekuri Scribe: Rachit Agarwal Fixed minor errors in Spring 2018. In the last lecture, we studied the KNAPSACK problem. The KNAPSACK problem is an NP- hard problem but does admit a pseudo-polynomial time algorithm and can be solved efficiently if the input size is small. We used this pseudo-polynomial time algorithm to obtain an FPTAS for KNAPSACK. In this lecture, we study another class of problems, known as strongly NP-hard problems. Definition 1 (Strongly NP-hard Problems) An NPO problem π is said to be strongly NP-hard if it is NP-hard even if the inputs are polynomially bounded in combinatorial size of the problem 1. Many NP-hard problems are in fact strongly NP-hard. If a problem Π is strongly NP-hard, then Π does not admit a pseudo-polynomial time algorithm. For more discussion on this, refer to [1, Section 8.3] We study two such problems in this lecture, MULTIPROCESSOR SCHEDULING and BIN PACKING. 1 Load Balancing / MultiProcessor Scheduling A central problem in scheduling theory is to design a schedule such that the finishing time of the last jobs (also called makespan) is minimized. This problem is often referred to as the LOAD BAL- ANCING, the MINIMUM MAKESPAN SCHEDULING or MULTIPROCESSOR SCHEDULING problem. 1.1 Problem Description In the MULTIPROCESSOR SCHEDULING problem, we are given m identical machines M1;:::;Mm and n jobs J1;J2;:::;Jn. Job Ji has a processing time pi ≥ 0 and the goal is to assign jobs to the machines so as to minimize the maximum load2. -
Integer Optimization Methods for Machine Learning by Allison an Chang Sc.B
Integer Optimization Methods for Machine Learning by Allison An Chang Sc.B. Applied Mathematics, Brown University (2007) Submitted to the Sloan School of Management in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Operations Research at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2012 c Massachusetts Institute of Technology 2012. All rights reserved. Author............................................. ................. Sloan School of Management May 18, 2012 Certified by......................................... ................. Dimitris Bertsimas Boeing Leaders for Global Operations Professor Co-Director, Operations Research Center Thesis Supervisor Certified by......................................... ................. Cynthia Rudin Assistant Professor of Statistics Thesis Supervisor Accepted by......................................... ................ Patrick Jaillet Dugald C. Jackson Professor Department of Electrical Engineering and Computer Science Co-Director, Operations Research Center 2 Integer Optimization Methods for Machine Learning by Allison An Chang Submitted to the Sloan School of Management on May 18, 2012, in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Operations Research Abstract In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks in diverse domains such as infor- mation retrieval, recommender systems, natural language processing, bioinformatics, and preventative maintenance. The primary advantage of using MIO for ranking is that it allows for direct optimization of ranking quality measures, as opposed to current state-of-the-art algorithms that use heuristic loss functions. We demonstrate using a number of datasets that our approach can outperform other ranking methods. The second part of the thesis focuses on reverse-engineering ranking models.