Types of Algorithms
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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. -
An Iterated Greedy Metaheuristic for the Blocking Job Shop Scheduling Problem
Dipartimento di Ingegneria Via della Vasca Navale, 79 00146 Roma, Italy An Iterated Greedy Metaheuristic for the Blocking Job Shop Scheduling Problem Marco Pranzo1, Dario Pacciarelli2 RT-DIA-208-2013 Novembre 2013 (1) Universit`adegli Studi Roma Tre, Via della Vasca Navale, 79 00146 Roma, Italy (2) Universit`adegli Studi di Siena Via Roma 56 53100 Siena, Italy. ABSTRACT In this paper we consider a job shop scheduling problem with blocking (zero buffer) con- straints. Blocking constraints model the absence of buffers, whereas in the traditional job shop scheduling model buffers have infinite capacity. There are two known variants of this problem, namely the blocking job shop scheduling (BJSS) with swap allowed and the BJSS with no-swap. A swap is needed when there is a cycle of two or more jobs each waiting for the machine occupied by the next job in the cycle. Such a cycle represents a deadlock in no-swap BJSS, while with a swap all the jobs in the cycle move simulta- neously to their subsequent machine. This scheduling problem is receiving an increasing interest in the recent literature, and we propose an Iterated Greedy (IG) algorithm to solve both variants of the problem. The IG is a metaheuristic based on the repetition of a destruction phase, which removes part of the solution, and a construction phase, in which a new solution is obtained by applying an underlying greedy algorithm starting from the partial solution. Comparison with recent published results shows that the iterated greedy outperforms other state-of-the-art algorithms on benchmark instances. -
Lecture 4 Dynamic Programming
1/17 Lecture 4 Dynamic Programming Last update: Jan 19, 2021 References: Algorithms, Jeff Erickson, Chapter 3. Algorithms, Gopal Pandurangan, Chapter 6. Dynamic Programming 2/17 Backtracking is incredible powerful in solving all kinds of hard prob- lems, but it can often be very slow; usually exponential. Example: Fibonacci numbers is defined as recurrence: 0 if n = 0 Fn =8 1 if n = 1 > Fn 1 + Fn 2 otherwise < ¡ ¡ > A direct translation in:to recursive program to compute Fibonacci number is RecFib(n): if n=0 return 0 if n=1 return 1 return RecFib(n-1) + RecFib(n-2) Fibonacci Number 3/17 The recursive program has horrible time complexity. How bad? Let's try to compute. Denote T(n) as the time complexity of computing RecFib(n). Based on the recursion, we have the recurrence: T(n) = T(n 1) + T(n 2) + 1; T(0) = T(1) = 1 ¡ ¡ Solving this recurrence, we get p5 + 1 T(n) = O(n); = 1.618 2 So the RecFib(n) program runs at exponential time complexity. RecFib Recursion Tree 4/17 Intuitively, why RecFib() runs exponentially slow. Problem: redun- dant computation! How about memorize the intermediate computa- tion result to avoid recomputation? Fib: Memoization 5/17 To optimize the performance of RecFib, we can memorize the inter- mediate Fn into some kind of cache, and look it up when we need it again. MemFib(n): if n = 0 n = 1 retujrjn n if F[n] is undefined F[n] MemFib(n-1)+MemFib(n-2) retur n F[n] How much does it improve upon RecFib()? Assuming accessing F[n] takes constant time, then at most n additions will be performed (we never recompute). -
GREEDY ALGORITHMS Greedy Algorithms Solve Problems by Making the Choice That Seems Best at the Particular Moment
GREEDY ALGORITHMS Greedy algorithms solve problems by making the choice that seems best at the particular moment. Many optimization problems can be solved using a greedy algorithm. Some problems have no efficient solution, but a greedy algorithm may provide a solution that is close to optimal. A greedy algorithm works if a problem exhibits the following two properties: 1. Greedy choice property: A globally optimal solution can be arrived at by making a locally optimal solution. In other words, an optimal solution can be obtained by making "greedy" choices. 2. optimal substructure. Optimal solutions contain optimal sub solutions. In other Words, solutions to sub problems of an optimal solution are optimal. Difference Between Greedy and Dynamic Programming The most difference between greedy algorithms and dynamic programming is that we don’t solve every optimal sub-problem with greedy algorithms. In some cases, greedy algorithms can be used to produce sub-optimal solutions. That is, solutions which aren't necessarily optimal, but are perhaps very dose. In dynamic programming, we make a choice at each step, but the choice may depend on the solutions to subproblems. In a greedy algorithm, we make whatever choice seems best at 'the moment and then solve the sub-problems arising after the choice is made. The choice made by a greedy algorithm may depend on choices so far, but it cannot depend on any future choices or on the solutions to sub- problems. Thus, unlike dynamic programming, which solves the sub-problems bottom up, a greedy strategy usually progresses in a top-down fashion, making one greedy choice after another, interactively reducing each given problem instance to a smaller one. -
Exhaustive Recursion and Backtracking
CS106B Handout #19 J Zelenski Feb 1, 2008 Exhaustive recursion and backtracking In some recursive functions, such as binary search or reversing a file, each recursive call makes just one recursive call. The "tree" of calls forms a linear line from the initial call down to the base case. In such cases, the performance of the overall algorithm is dependent on how deep the function stack gets, which is determined by how quickly we progress to the base case. For reverse file, the stack depth is equal to the size of the input file, since we move one closer to the empty file base case at each level. For binary search, it more quickly bottoms out by dividing the remaining input in half at each level of the recursion. Both of these can be done relatively efficiently. Now consider a recursive function such as subsets or permutation that makes not just one recursive call, but several. The tree of function calls has multiple branches at each level, which in turn have further branches, and so on down to the base case. Because of the multiplicative factors being carried down the tree, the number of calls can grow dramatically as the recursion goes deeper. Thus, these exhaustive recursion algorithms have the potential to be very expensive. Often the different recursive calls made at each level represent a decision point, where we have choices such as what letter to choose next or what turn to make when reading a map. Might there be situations where we can save some time by focusing on the most promising options, without committing to exploring them all? In some contexts, we have no choice but to exhaustively examine all possibilities, such as when trying to find some globally optimal result, But what if we are interested in finding any solution, whichever one that works out first? At each decision point, we can choose one of the available options, and sally forth, hoping it works out. -
Dynamic Programming Via Static Incrementalization 1 Introduction
Dynamic Programming via Static Incrementalization Yanhong A. Liu and Scott D. Stoller Abstract Dynamic programming is an imp ortant 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 rep eatedly and of- ten takes exp onential time, a dynamic programming algorithm solves every subsubproblem just once, saves the result, reuses it when the subsubproblem is encountered again, and takes p oly- nomial time. This pap er describ es a systematic metho d for transforming programs written as straightforward recursions into programs that use dynamic programming. The metho d extends the original program to cache all p ossibly computed values, incrementalizes the extended pro- gram with resp ect 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 in- cremental program to form an optimized new program. Incrementalization statically exploits semantics of b oth control structures and data structures and maintains as invariants equalities characterizing cached results. The principle underlying incrementalization is general for achiev- ing drastic program sp eedups. Compared with previous metho ds that p erform memoization or tabulation, the metho d based on incrementalization is more powerful and systematic. It has b een implemented and applied to numerous problems and succeeded on all of them. 1 Intro duction Dynamic programming is an imp ortant technique for designing ecient algorithms [2, 44 , 13 ]. It is used for problems whose solutions involve recursively solving subproblems that overlap. -
Greedy Estimation of Distributed Algorithm to Solve Bounded Knapsack Problem
Shweta Gupta et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4313-4316 Greedy Estimation of Distributed Algorithm to Solve Bounded knapsack Problem Shweta Gupta#1, Devesh Batra#2, Pragya Verma#3 #1Indian Institute of Technology (IIT) Roorkee, India #2Stanford University CA-94305, USA #3IEEE, USA Abstract— This paper develops a new approach to find called probabilistic model-building genetic algorithms, solution to the Bounded Knapsack problem (BKP).The are stochastic optimization methods that guide the search Knapsack problem is a combinatorial optimization problem for the optimum by building probabilistic models of where the aim is to maximize the profits of objects in a favourable candidate solutions. The main difference knapsack without exceeding its capacity. BKP is a between EDAs and evolutionary algorithms is that generalization of 0/1 knapsack problem in which multiple instances of distinct items but a single knapsack is taken. Real evolutionary algorithms produces new candidate solutions life applications of this problem include cryptography, using an implicit distribution defined by variation finance, etc. This paper proposes an estimation of distribution operators like crossover, mutation, whereas EDAs use algorithm (EDA) using greedy operator approach to find an explicit probability distribution model such as Bayesian solution to the BKP. EDA is stochastic optimization technique network, a multivariate normal distribution etc. EDAs can that explores the space of potential solutions by modelling be used to solve optimization problems like other probabilistic models of promising candidate solutions. conventional evolutionary algorithms. The rest of the paper is organized as follows: next section Index Terms— Bounded Knapsack, Greedy Algorithm, deals with the related work followed by the section III Estimation of Distribution Algorithm, Combinatorial Problem, which contains theoretical procedure of EDA. -
Backtrack Parsing Context-Free Grammar Context-Free Grammar
Context-free Grammar Problems with Regular Context-free Grammar Language and Is English a regular language? Bad question! We do not even know what English is! Two eggs and bacon make(s) a big breakfast Backtrack Parsing Can you slide me the salt? He didn't ought to do that But—No! Martin Kay I put the wine you brought in the fridge I put the wine you brought for Sandy in the fridge Should we bring the wine you put in the fridge out Stanford University now? and University of the Saarland You said you thought nobody had the right to claim that they were above the law Martin Kay Context-free Grammar 1 Martin Kay Context-free Grammar 2 Problems with Regular Problems with Regular Language Language You said you thought nobody had the right to claim [You said you thought [nobody had the right [to claim that they were above the law that [they were above the law]]]] Martin Kay Context-free Grammar 3 Martin Kay Context-free Grammar 4 Problems with Regular Context-free Grammar Language Nonterminal symbols ~ grammatical categories Is English mophology a regular language? Bad question! We do not even know what English Terminal Symbols ~ words morphology is! They sell collectables of all sorts Productions ~ (unordered) (rewriting) rules This concerns unredecontaminatability Distinguished Symbol This really is an untiable knot. But—Probably! (Not sure about Swahili, though) Not all that important • Terminals and nonterminals are disjoint • Distinguished symbol Martin Kay Context-free Grammar 5 Martin Kay Context-free Grammar 6 Context-free Grammar Context-free -
Automatic Code Generation Using Dynamic Programming Techniques
! Automatic Code Generation using Dynamic Programming Techniques MASTERARBEIT zur Erlangung des akademischen Grades Diplom-Ingenieur im Masterstudium INFORMATIK Eingereicht von: Igor Böhm, 0155477 Angefertigt am: Institut für System Software Betreuung: o.Univ.-Prof.Dipl.-Ing. Dr. Dr.h.c. Hanspeter Mössenböck Linz, Oktober 2007 Abstract Building compiler back ends from declarative specifications that map tree structured intermediate representations onto target machine code is the topic of this thesis. Although many tools and approaches have been devised to tackle the problem of automated code generation, there is still room for improvement. In this context we present Hburg, an implementation of a code generator generator that emits compiler back ends from concise tree pattern specifications written in our code generator description language. The language features attribute grammar style specifications and allows for great flexibility with respect to the placement of semantic actions. Our main contribution is to show that these language features can be integrated into automatically generated code generators that perform optimal instruction selection based on tree pattern matching combined with dynamic program- ming. In order to substantiate claims about the usefulness of our language we provide two complete examples that demonstrate how to specify code generators for Risc and Cisc architectures. Kurzfassung Diese Diplomarbeit beschreibt Hburg, ein Werkzeug das aus einer Spezi- fikation des abstrakten Syntaxbaums eines Programms und der Spezifika- tion der gewuns¨ chten Zielmaschine automatisch einen Codegenerator fur¨ diese Maschine erzeugt. Abbildungen zwischen abstrakten Syntaxb¨aumen und einer Zielmaschine werden durch Baummuster definiert. Fur¨ diesen Zweck haben wir eine deklarative Beschreibungssprache entwickelt, die es erm¨oglicht den Baummustern Attribute beizugeben, wodurch diese gleich- sam parametrisiert werden k¨onnen. -
Greedy Algorithms Greedy Strategy: Make a Locally Optimal Choice, Or Simply, What Appears Best at the Moment R
Overview Kruskal Dijkstra Human Compression Overview Kruskal Dijkstra Human Compression Overview One of the strategies used to solve optimization problems CSE 548: (Design and) Analysis of Algorithms Multiple solutions exist; pick one of low (or least) cost Greedy Algorithms Greedy strategy: make a locally optimal choice, or simply, what appears best at the moment R. Sekar Often, locally optimality 6) global optimality So, use with a great deal of care Always need to prove optimality If it is unpredictable, why use it? It simplifies the task! 1 / 35 2 / 35 Overview Kruskal Dijkstra Human Compression Overview Kruskal Dijkstra Human Compression Making change When does a Greedy algorithm work? Given coins of denominations 25cj, 10cj, 5cj and 1cj, make change for x cents (0 < x < 100) using minimum number of coins. Greedy choice property Greedy solution The greedy (i.e., locally optimal) choice is always consistent with makeChange(x) some (globally) optimal solution if (x = 0) return What does this mean for the coin change problem? Let y be the largest denomination that satisfies y ≤ x Optimal substructure Issue bx=yc coins of denomination y The optimal solution contains optimal solutions to subproblems. makeChange(x mod y) Implies that a greedy algorithm can invoke itself recursively after Show that it is optimal making a greedy choice. Is it optimal for arbitrary denominations? 3 / 35 4 / 35 Chapter 5 Overview Kruskal Dijkstra Human Compression Overview Kruskal Dijkstra Human Compression Knapsack Problem Greedy algorithmsFractional Knapsack A sack that can hold a maximum of x lbs Greedy choice property Proof by contradiction: Start with the assumption that there is an You have a choice of items you can pack in the sack optimal solution that does not include the greedy choice, and show a A game like chess can be won only by thinking ahead: a player who is focused entirely on Maximize the combined “value” of items in the sack contradiction. -
Types of Algorithms
Types of Algorithms Material adapted – courtesy of Prof. Dave Matuszek at UPENN Algorithm classification Algorithms that use a similar problem-solving approach can be grouped together This classification scheme is neither exhaustive nor disjoint The purpose is not to be able to classify an algorithm as one type or another, but to highlight the various ways in which a problem can be attacked 2 1 A short list of categories Algorithm types we will consider include: Simple recursive algorithms Divide and conquer algorithms Dynamic programming algorithms Greedy algorithms Brute force algorithms Randomized algorithms Note: we may even classify algorithms based on the type of problem they are trying to solve – example sorting algorithms, searching algorithms etc. 3 Simple recursive algorithms I A simple recursive algorithm: Solves the base cases directly Recurs with a simpler subproblem Does some extra work to convert the solution to the simpler subproblem into a solution to the given problem We call these “simple” because several of the other algorithm types are inherently recursive 4 2 Example recursive algorithms To count the number of elements in a list: If the list is empty, return zero; otherwise, Step past the first element, and count the remaining elements in the list Add one to the result To test if a value occurs in a list: If the list is empty, return false; otherwise, If the first thing in the list is the given value, return true; otherwise Step past the first element, and test whether the value occurs in the remainder of the list 5 Backtracking algorithms Backtracking algorithms are based on a depth-first* recursive search A backtracking algorithm: Tests to see if a solution has been found, and if so, returns it; otherwise For each choice that can be made at this point, Make that choice Recur If the recursion returns a solution, return it If no choices remain, return failure *We will cover depth-first search very soon (once we get to tree-traversal and trees/graphs). -
1 Introduction 2 Dijkstra's Algorithm
15-451/651: Design & Analysis of Algorithms September 3, 2019 Lecture #3: Dynamic Programming II last changed: August 30, 2019 In this lecture we continue our discussion of dynamic programming, focusing on using it for a variety of path-finding problems in graphs. Topics in this lecture include: • The Bellman-Ford algorithm for single-source (or single-sink) shortest paths. • Matrix-product algorithms for all-pairs shortest paths. • Algorithms for all-pairs shortest paths, including Floyd-Warshall and Johnson. • Dynamic programming for the Travelling Salesperson Problem (TSP). 1 Introduction As a reminder of basic terminology: a graph is a set of nodes or vertices, with edges between some of the nodes. We will use V to denote the set of vertices and E to denote the set of edges. If there is an edge between two vertices, we call them neighbors. The degree of a vertex is the number of neighbors it has. Unless otherwise specified, we will not allow self-loops or multi-edges (multiple edges between the same pair of nodes). As is standard with discussing graphs, we will use n = jV j, and m = jEj, and we will let V = f1; : : : ; ng. The above describes an undirected graph. In a directed graph, each edge now has a direction (and as we said earlier, we will sometimes call the edges in a directed graph arcs). For each node, we can now talk about out-neighbors (and out-degree) and in-neighbors (and in-degree). In a directed graph you may have both an edge from u to v and an edge from v to u.