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• Disk Search / Sort Algorithm

  - by AlgoMan

  Given a Range of numbers say 1 to 10,000, Input is in random order. Constraint: At any point only 1000 numbers can be loaded to memory. Assumption: Assuming unique numbers. I propose the following efficient , "When-Required-sort Algorithm". We write the numbers into files which are designated to hold particular range of numbers. For example, File1 will have 0 - 999 , File2 will have 1000 - 1999 and so on in random order. If a particular number which is say "2535" is being searched for then we know that the number is in the file3 (Binary search over range to find the file). Then file3 is loaded to memory and sorted using say Quick sort (which is optimized to add insertion sort when the array size is small ) and then we search the number in this sorted array using Binary search. And when search is done we write back the sorted file. So in long run all the numbers will be sorted. Please comment on this proposal.

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• Help with Algorithm chinese auction

  - by sam munkes

  Hi, i am designing a Chinese auction website. Tickets ($5, $10 & $20) are sold either individually, or via packages to receive discounts. There are various Ticket packages for example: 5-$5 tickets = receive 10% off 5-$10 tickets = receive 10% off 5-$20 tickets = receive 10% off 5-$5 tickets + 5-$10 tickets + 5-$20 tickets = receive 15% off When users add tickets to their cart, i need to figure out the cheapest package(s) to give them. the trick is that if a user adds 4-$5 tickets + 5-$10 tickets + 5-$20 tickets, it should still give him package #3 since that would be the cheapest for him. Any help in figuring out a algorithm to solve this, or any tips would be greatly appreciate it. thanks

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• Algorithm to price bulk discounts

  - by sam munkes

  Hi, i am designing a Chinese auction website. Tickets ($5, $10 & $20) are sold either individually, or via packages to receive discounts. There are various Ticket packages for example: 5-$5 tickets = receive 10% off 5-$10 tickets = receive 10% off 5-$20 tickets = receive 10% off 5-$5 tickets + 5-$10 tickets + 5-$20 tickets = receive 15% off When users add tickets to their cart, i need to figure out the cheapest package(s) to give them. the trick is that if a user adds 4-$5 tickets + 5-$10 tickets + 5-$20 tickets, it should still give him package #4 since that would be the cheapest for him. Any help in figuring out a algorithm to solve this, or any tips would be greatly appreciate it. thanks

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• Creating a "crossover" function for a genetic algorithm to improve network paths

  - by Dave

  Hi, I'm trying to develop a genetic algorithm that will find the most efficient way to connect a given number of nodes at specified locations. All the nodes on the network must be able to connect to the server node and there must be no cycles within the network. It's basically a tree. I have a function that can measure the "fitness" of any given network layout. What's stopping me is that I can't think of a crossover function that would take 2 network structures (parents) and somehow mix them to create offspring that would meet the above conditions. Any ideas? Clarification: The nodes each have a fixed x,y coordiante position. Only the routes between them can be altered.

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• Suggestion on algorithm to distribute objects of different value

  - by Unknown

  Hello, I have the following problem: Given N objects of different values (N < 30, and the values are multiple of a "k" constant, i.e. k, 2k, 3k, 4k, 6k, 8k, 12k, 16k, 24k and 32k), I need an algorithm that will distribute all items to M players (M <= 6) in such a way that the total value of the objects each player gets is as even as possible (in other words, I want to distribute all objects to all players in the fairest way possible). I don't need (pseudo)code to solve this (also, this is not a homework :) ), but I'll appreciate any ideas or links to algorithms that could solve this. Thanks!

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• help with number calculation algorithm [hw]

  - by sa125

  Hi - I'm working on a hw problem that asks me this: given a finite set of numbers, and a target number, find if the set can be used to calculate the target number using basic math operations (add, sub, mult, div) and using each number in the set exactly once (so I need to exhaust the set). This has to be done with recursion. So, for example, if I have the set {1, 2, 3, 4} and target 10, then I could get to it by using ((3 * 4) - 2)/1 = 10. I'm trying to phrase the algorithm in pseudo-code, but so far haven't gotten too far. I'm thinking graphs are the way to go, but would definitely appreciate help on this. thanks.

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• algorithm for Virtual Machine(VM) Consolidation in Cloud

  - by devansh dalal

  PROBLEM: We have N physical machines(PMs) each with ram Ri, cpu Ci and a set of currently scheduled VMs each with ram requirement ri and ci respectively Moving(Migrating) any VM from one PM to other has a cost associated which depends on its ram ri. A PM with no VMs is shut down to save power. Our target is to minimize the weighted sum of (N,migration cost) by migrating some VMs i.e. minimize the number of working PMs as well as not to degrade the service level due to excessive migrations. My Approach: Brute Force approach is choosing the minimum loaded PM and try to fit its VMs to other PMs by First Fit Decreasing algorithm or we can select the victim PMs and target PMs based on their loading level and shut down victims if possible by moving their VMs to targets. I tried this Greedy approach on the Data of Baadal(IIT-D cloud) but It isn't giving promising results. I have also tried to study the Ant colony optimization for dynamic VM consolidating but was unable to understand very much. I used the links. http://dumas.ccsd.cnrs.fr/docs/00/72/52/15/PDF/Esnault.pdf http://hal.archives-ouvertes.fr/docs/00/72/38/56/PDF/RR-8032.pdf Would anyone please explain the solution or suggest any new approach for better performance soon. Thanks in advance.

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• Efficient algorithm for Virtual Machine(VM) Consolidation in Cloud

  - by devansh dalal

  PROBLEM: We have N physical machines(PMs) each with ram Ri, cpu Ci and a set of currently scheduled VMs each with ram requirement ri and ci respectively Moving(Migrating) any VM from one PM to other has a cost associated which depends on its ram ri. A PM with no VMs is shut down to save power. Our target is to minimize the weighted sum of (N,migration cost) by migrating some VMs i.e. minimize the number of working PMs as well as not to degrade the service level due to excessive migrations. My Approach: Brute Force approach is choosing the minimum loaded PM and try to fit its VMs to other PMs by First Fit Decreasing algorithm or we can select the victim PMs and target PMs based on their loading level and shut down victims if possible by moving their VMs to targets. I tried this Greedy approach on the Data of Baadal(IIT-D cloud) but It isn't giving promising results. I have also tried to study the Ant colony optimization for dynamic VM consolidating but was unable to understand very much. I used the links. http://dumas.ccsd.cnrs.fr/docs/00/72/52/15/PDF/Esnault.pdf http://hal.archives-ouvertes.fr/docs/00/72/38/56/PDF/RR-8032.pdf Would anyone please clarify the solution or suggest any new approach/resources for better performance. I am basically searching for the algorithms not the physical optimizations and I also know that many commercial organizations have provided these solution but I just wanted to know more the underlying algorithms. Thanks in advance.

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• Understanding and Implementing a Force based graph layout algorithm

  - by zcourts

  I'm trying to implement a force base graph layout algorithm, based on http://en.wikipedia.org/wiki/Force-based_algorithms_(graph_drawing) My first attempt didn't work so I looked at http://blog.ivank.net/force-based-graph-drawing-in-javascript.html and https://github.com/dhotson/springy I changed my implementation based on what I thought I understood from those two but I haven't managed to get it right and I'm hoping someone can help? JavaScript isn't my strong point so be gentle... If you're wondering why write my own. In reality I have no real reason to write my own I'm just trying to understand how the algorithm is implemented. Especially in my first link, that demo is brilliant. This is what I've come up with //support function.bind - https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Function/bind#Compatibility if (!Function.prototype.bind) { Function.prototype.bind = function (oThis) { if (typeof this !== "function") { // closest thing possible to the ECMAScript 5 internal IsCallable function throw new TypeError("Function.prototype.bind - what is trying to be bound is not callable"); } var aArgs = Array.prototype.slice.call(arguments, 1), fToBind = this, fNOP = function () {}, fBound = function () { return fToBind.apply(this instanceof fNOP ? this : oThis || window, aArgs.concat(Array.prototype.slice.call(arguments))); }; fNOP.prototype = this.prototype; fBound.prototype = new fNOP(); return fBound; }; } (function() { var lastTime = 0; var vendors = ['ms', 'moz', 'webkit', 'o']; for(var x = 0; x < vendors.length && !window.requestAnimationFrame; ++x) { window.requestAnimationFrame = window[vendors[x]+'RequestAnimationFrame']; window.cancelAnimationFrame = window[vendors[x]+'CancelAnimationFrame'] || window[vendors[x]+'CancelRequestAnimationFrame']; } if (!window.requestAnimationFrame) window.requestAnimationFrame = function(callback, element) { var currTime = new Date().getTime(); var timeToCall = Math.max(0, 16 - (currTime - lastTime)); var id = window.setTimeout(function() { callback(currTime + timeToCall); }, timeToCall); lastTime = currTime + timeToCall; return id; }; if (!window.cancelAnimationFrame) window.cancelAnimationFrame = function(id) { clearTimeout(id); }; }()); function Graph(o){ this.options=o; this.vertices={}; this.edges={};//form {vertexID:{edgeID:edge}} } /** *Adds an edge to the graph. If the verticies in this edge are not already in the *graph then they are added */ Graph.prototype.addEdge=function(e){ //if vertex1 and vertex2 doesn't exist in this.vertices add them if(typeof(this.vertices[e.vertex1])==='undefined') this.vertices[e.vertex1]=new Vertex(e.vertex1); if(typeof(this.vertices[e.vertex2])==='undefined') this.vertices[e.vertex2]=new Vertex(e.vertex2); //add the edge if(typeof(this.edges[e.vertex1])==='undefined') this.edges[e.vertex1]={}; this.edges[e.vertex1][e.id]=e; } /** * Add a vertex to the graph. If a vertex with the same ID already exists then * the existing vertex's .data property is replaced with the @param v.data */ Graph.prototype.addVertex=function(v){ if(typeof(this.vertices[v.id])==='undefined') this.vertices[v.id]=v; else this.vertices[v.id].data=v.data; } function Vertex(id,data){ this.id=id; this.data=data?data:{}; //initialize to data.[x|y|z] or generate random number for each this.x = this.data.x?this.data.x:-100 + Math.random()*200; this.y = this.data.y?this.data.y:-100 + Math.random()*200; this.z = this.data.y?this.data.y:-100 + Math.random()*200; //set initial velocity to 0 this.velocity = new Point(0, 0, 0); this.mass=this.data.mass?this.data.mass:Math.random(); this.force=new Point(0,0,0); } function Edge(vertex1ID,vertex2ID){ vertex1ID=vertex1ID?vertex1ID:Math.random() vertex2ID=vertex2ID?vertex2ID:Math.random() this.id=vertex1ID+"->"+vertex2ID; this.vertex1=vertex1ID; this.vertex2=vertex2ID; } function Point(x, y, z) { this.x = x; this.y = y; this.z = z; } Point.prototype.plus=function(p){ this.x +=p.x this.y +=p.y this.z +=p.z } function ForceLayout(o){ this.repulsion = o.repulsion?o.repulsion:200; this.attraction = o.attraction?o.attraction:0.06; this.damping = o.damping?o.damping:0.9; this.graph = o.graph?o.graph:new Graph(); this.total_kinetic_energy =0; this.animationID=-1; } ForceLayout.prototype.draw=function(){ //vertex velocities initialized to (0,0,0) when a vertex is created //vertex positions initialized to random position when created cc=0; do{ this.total_kinetic_energy =0; //for each vertex for(var i in this.graph.vertices){ var thisNode=this.graph.vertices[i]; // running sum of total force on this particular node var netForce=new Point(0,0,0) //for each other node for(var j in this.graph.vertices){ if(thisNode!=this.graph.vertices[j]){ //net-force := net-force + Coulomb_repulsion( this_node, other_node ) netForce.plus(this.CoulombRepulsion( thisNode,this.graph.vertices[j])) } } //for each spring connected to this node for(var k in this.graph.edges[thisNode.id]){ //(this node, node its connected to) //pass id of this node and the node its connected to so hookesattraction //can update the force on both vertices and return that force to be //added to the net force this.HookesAttraction(thisNode.id, this.graph.edges[thisNode.id][k].vertex2 ) } // without damping, it moves forever // this_node.velocity := (this_node.velocity + timestep * net-force) * damping thisNode.velocity.x=(thisNode.velocity.x+thisNode.force.x)*this.damping; thisNode.velocity.y=(thisNode.velocity.y+thisNode.force.y)*this.damping; thisNode.velocity.z=(thisNode.velocity.z+thisNode.force.z)*this.damping; //this_node.position := this_node.position + timestep * this_node.velocity thisNode.x=thisNode.velocity.x; thisNode.y=thisNode.velocity.y; thisNode.z=thisNode.velocity.z; //normalize x,y,z??? //total_kinetic_energy := total_kinetic_energy + this_node.mass * (this_node.velocity)^2 this.total_kinetic_energy +=thisNode.mass*((thisNode.velocity.x+thisNode.velocity.y+thisNode.velocity.z)* (thisNode.velocity.x+thisNode.velocity.y+thisNode.velocity.z)) } cc+=1; }while(this.total_kinetic_energy >0.5) console.log(cc,this.total_kinetic_energy,this.graph) this.cancelAnimation(); } ForceLayout.prototype.HookesAttraction=function(v1ID,v2ID){ var a=this.graph.vertices[v1ID] var b=this.graph.vertices[v2ID] var force=new Point(this.attraction*(b.x - a.x),this.attraction*(b.y - a.y),this.attraction*(b.z - a.z)) // hook's attraction a.force.x += force.x; a.force.y += force.y; a.force.z += force.z; b.force.x += this.attraction*(a.x - b.x); b.force.y += this.attraction*(a.y - b.y); b.force.z += this.attraction*(a.z - b.z); return force; } ForceLayout.prototype.CoulombRepulsion=function(vertex1,vertex2){ //http://en.wikipedia.org/wiki/Coulomb's_law // distance squared = ((x1-x2)*(x1-x2)) + ((y1-y2)*(y1-y2)) + ((z1-z2)*(z1-z2)) var distanceSquared = ( (vertex1.x-vertex2.x)*(vertex1.x-vertex2.x)+ (vertex1.y-vertex2.y)*(vertex1.y-vertex2.y)+ (vertex1.z-vertex2.z)*(vertex1.z-vertex2.z) ); if(distanceSquared==0) distanceSquared = 0.001; var coul = this.repulsion / distanceSquared; return new Point(coul * (vertex1.x-vertex2.x),coul * (vertex1.y-vertex2.y), coul * (vertex1.z-vertex2.z)); } ForceLayout.prototype.animate=function(){ if(this.animating) this.animationID=requestAnimationFrame(this.animate.bind(this)); this.draw(); } ForceLayout.prototype.cancelAnimation=function(){ cancelAnimationFrame(this.animationID); this.animating=false; } ForceLayout.prototype.redraw=function(){ this.animating=true; this.animate(); } $(document).ready(function(){ var g= new Graph(); for(var i=0;i<=100;i++){ var v1=new Vertex(Math.random(), {}) var v2=new Vertex(Math.random(), {}) var e1= new Edge(v1.id,v2.id); g.addEdge(e1); } console.log(g); var l=new ForceLayout({ graph:g }); l.redraw(); });

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• Algorithm for dynamic combinations

  - by sOltan

  My code has a list called INPUTS, that contains a dynamic number of lists, let's call them A, B, C, .. N. These lists contain a dynamic number of Events I would like to call a function with each combination of Events. To illustrate with an example: INPUTS: A(0,1,2), B(0,1), C(0,1,2,3) I need to call my function this many times for each combination (the input count is dynamic, in this example it is three parameter, but it can be more or less) function(A[0],B[0],C[0]) function(A[0],B[1],C[0]) function(A[0],B[0],C[1]) function(A[0],B[1],C[1]) function(A[0],B[0],C[2]) function(A[0],B[1],C[2]) function(A[0],B[0],C[3]) function(A[0],B[1],C[3]) function(A[1],B[0],C[0]) function(A[1],B[1],C[0]) function(A[1],B[0],C[1]) function(A[1],B[1],C[1]) function(A[1],B[0],C[2]) function(A[1],B[1],C[2]) function(A[1],B[0],C[3]) function(A[1],B[1],C[3]) function(A[2],B[0],C[0]) function(A[2],B[1],C[0]) function(A[2],B[0],C[1]) function(A[2],B[1],C[1]) function(A[2],B[0],C[2]) function(A[2],B[1],C[2]) function(A[2],B[0],C[3]) function(A[2],B[1],C[3]) This is what I have thought of so far: My approach so far is to build a list of combinations. The element combination is itself a list of "index" to the input arrays A, B and C. For our example: my list iCOMBINATIONS contains the following iCOMBO lists (0,0,0) (0,1,0) (0,0,1) (0,1,1) (0,0,2) (0,1,2) (0,0,3) (0,1,3) (1,0,0) (1,1,0) (1,0,1) (1,1,1) (1,0,2) (1,1,2) (1,0,3) (1,1,3) (2,0,0) (2,1,0) (2,0,1) (2,1,1) (2,0,2) (2,1,2) (2,0,3) (2,1,3) Then I would do this: foreach( iCOMBO in iCOMBINATIONS) { foreach ( P in INPUTS ) { COMBO.Clear() foreach ( i in iCOMBO ) { COMBO.Add( P[ iCOMBO[i] ] ) } function( COMBO ) --- (instead of passing the events separately) } } But I need to find a way to build the list iCOMBINATIONS for any given number of INPUTS and their events. Any ideas? Is there actually a better algorithm than this? any pseudo code to help me with will be great. C# (or VB) Thank You

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• how to tackle this combinatorial algorithm problem

  - by Andrew Bullock

  I have N people who must each take T exams. Each exam takes "some" time, e.g. 30 min (no such thing as finishing early). Exams must be performed in front of an examiner. I need to schedule each person to take each exam in front of an examiner within an overall time period, using the minimum number of examiners for the minimum amount of time (i.e. no examiners idle) There are the following restrictions: No person can be in 2 places at once each person must take each exam once noone should be examined by the same examiner twice I realise that an optimal solution is probably NP-Complete, and that I'm probably best off using a genetic algorithm to obtain a best estimate (similar to this? http://stackoverflow.com/questions/184195/seating-plan-software-recommendations-does-such-a-beast-even-exist). I'm comfortable with how genetic algorithms work, what i'm struggling with is how to model the problem programatically such that i CAN manipulate the parameters genetically.. If each exam took the same amount of time, then i'd divide the time period up into these lengths, and simply create a matrix of time slots vs examiners and drop the candidates in. However because the times of each test are not necessarily the same, i'm a bit lost on how to approach this. currently im doing this: make a list of all "tests" which need to take place, between every candidate and exam start with as many examiners as there are tests repeatedly loop over all examiners, for each one: find an unscheduled test which is eligible for the examiner (based on the restrictions) continue until all tests that can be scheduled, are if there are any unscheduled tests, increment the number of examiners and start again. i'm looking for better suggestions on how to approach this, as it feels rather crude currently.

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• Merge method in MergeSort Algorithm .

  - by Tony

  I've seen many mergeSort implementations .Here is the version in Data Structures and Algorithms in Java (2nd Edition) by Robert Lafore : private void recMergeSort(long[] workSpace, int lowerBound,int upperBound) { if(lowerBound == upperBound) // if range is 1, return; // no use sorting else { // find midpoint int mid = (lowerBound+upperBound) / 2; // sort low half recMergeSort(workSpace, lowerBound, mid); // sort high half recMergeSort(workSpace, mid+1, upperBound); // merge them merge(workSpace, lowerBound, mid+1, upperBound); } // end else } // end recMergeSort() private void merge(long[] workSpace, int lowPtr, int highPtr, int upperBound) { int j = 0; // workspace index int lowerBound = lowPtr; int mid = highPtr-1; int n = upperBound-lowerBound+1; // # of items while(lowPtr <= mid && highPtr <= upperBound) if( theArray[lowPtr] < theArray[highPtr] ) workSpace[j++] = theArray[lowPtr++]; else workSpace[j++] = theArray[highPtr++]; while(lowPtr <= mid) workSpace[j++] = theArray[lowPtr++]; while(highPtr <= upperBound) workSpace[j++] = theArray[highPtr++]; for(j=0; j<n; j++) theArray[lowerBound+j] = workSpace[j]; } // end merge() One interesting thing about merge method is that , almost all the implementations didn't pass the lowerBound parameter to merge method . lowerBound is calculated in the merge . This is strange , since lowerPtr = mid + 1 ; lowerBound = lowerPtr -1 ; that means lowerBound = mid ; Why the author didn't pass mid to merge like merge(workSpace, lowerBound,mid, mid+1, upperBound); ? I think there must be a reason , otherwise I can't understand why an algorithm older than half a center ,and have all coincident in the such little detail.

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• Sparse parameter selection using Genetic Algorithm

  - by bgbg

  Hello, I'm facing a parameter selection problem, which I would like to solve using Genetic Algorithm (GA). I'm supposed to select not more than 4 parameters out of 3000 possible ones. Using the binary chromosome representation seems like a natural choice. The evaluation function punishes too many "selected" attributes and if the number of attributes is acceptable, it then evaluates the selection. The problem is that in these sparse conditions the GA can hardly improve the population. Neither the average fitness cost, nor the fitness of the "worst" individual improves over the generations. All I see is slight (even tiny) improvement in the score of the best individual, which, I suppose, is a result of random sampling. Encoding the problem using indices of the parameters doesn't work either. This is most probably, due to the fact that the chromosomes are directional, while the selection problem isn't (i.e. chromosomes [1, 2, 3, 4]; [4, 3, 2, 1]; [3, 2, 4, 1] etc. are identical) What problem representation would you suggest? P.S If this matters, I use PyEvolve.

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• Algorithm to match list of regular expressions

  - by DSII

  I have two algorithmic questions for a project I am working on. I have thought about these, and have some suspicions, but I would love to hear the community's input as well. Suppose I have a string, and a list of N regular expressions (actually they are wildcard patterns representing a subset of full regex functionality). I want to know whether the string matches at least one of the regular expressions in the list. Is there a data structure that can allow me to match the string against the list of regular expressions in sublinear (presumably logarithmic) time? This is an extension of the previous problem. Suppose I have the same situation: a string and a list of N regular expressions, only now each of the regular expressions is paired with an offset within the string at which the match must begin (or, if you prefer, each of the regular expressions must match a substring of the given string beginning at the given offset). To give an example, suppose I had the string: This is a test string and the regex patterns and offsets: (a) his.* at offset 0 (b) his.* at offset 1 The algorithm should return true. Although regex (a) does not match the string beginning at offset 0, regex (b) does match the substring beginning at offset 1 ("his is a test string"). Is there a data structure that can allow me to solve this problem in sublinear time? One possibly useful piece of information is that often, many of the offsets in the list of regular expressions are the same (i.e. often we are matching the substring at offset X many times). This may be useful to leverage the solution to problem #1 above. Thank you very much in advance for any suggestions you may have!

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• How to gain accurate results with Painter's algorithm?

  - by pimvdb

  A while ago I asked how to determine when a face is overlapping another. The advice was to use a Z-buffer. However, I cannot use a Z-buffer in my current project and hence I would like to use the Painter's algorithm. I have no good clue as to when a surface is behind or in front of another, though. I've tried numerous methods but they all fail in edge cases, or they fail even in general cases. This is a list of sorting methods I've tried so far: Distance to midpoint of each face Average distance to each vertex of each face Average z value of each vertex Higest z value of vertices of each face and draw those first Lowest z value of vertices of each face and draw those last The problem is that a face might have a closer distance but is still further away. All these methods seem unreliable. Edit: For example, in the following image the surface with the blue point as midpoint is painted over the surface with the red point as midpoint, because the blue point is closer. However, this is because the surface of the red point is larger and the midpoint is further away. The surface with the red point should be painted over the blue one, because it is closer, whilst the midpoint distance says the opposite. What exactly is used in the Painter's algorithm to determine the order in which objects should be drawn?

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• Triangulation A* (TA*) pathfinding algorithm

  - by hyn

  I need help understanding the Triangle A* (TA*) algorithm that is described by Demyen in his paper Efficient Triangulation-Based Pathfinding, on pages 76-81. He describes how to adapt the regular A* algorithm for triangulation, to search for other possibly more optimal paths, even after the final node is reached/expanded. Regular A* stops when the final node is expanded, but this is not always the best path when used in a triangulated graph. This is exactly the problem I'm having. The problem is illustrated on page 78, Figure 5.4: I understand how to calculate the g and h values presented in the paper (page 80). And I think the search stop condition is: if (currentNode.fCost > shortestDistanceFound) { // stop break; } where currentNode is the search node popped from the open list (priority queue), which has the lowest f-score. shortestDistanceFound is the actual distance of the shortest path found so far. But how do I exclude the previously found paths from future searches? Because if I do the search again, it will obviously find the same path. Do I reset the closed list? I need to modify something, but I don't know what it is I need to change. The paper lacks pseudocode, so that would be helpful.

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• need explanation on amortization in algorithm

  - by Pradeep

  I am a learning algorithm analysis and came across a analysis tool for understanding the running time of an algorithm with widely varying performance which is called as amortization. The autor quotes " An array with upper bound of n elements, with a fixed bound N, on it size. Operation clear takes O(n) time, since we should dereference all the elements in the array in order to really empty it. " The above statement is clear and valid. Now consider the next content: "Now consider a series of n operations on an initially empty array. if we take the worst case viewpoint, the running time is O(n^2), since the worst case of a sigle clear operation in the series is O(n) and there may be as many as O(n) clear operations in the series." From the above statement how is the time complexity O(n^2)? I did not understand the logic behind it. if 'n' operations are performed how is it O(n ^2)? Please explain what the autor is trying to convey..

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• How does flocking algorithm work?

  - by Chan

  I read and understand the basic of flocking algorithm. Basically, we need to have 3 behaviors: 1. Cohesion 2. Separation 3. Alignment From my understanding, it's like a state machine. Every time we do an update (then draw), we check all the constraints on both three behaviors. And each behavior returns a Vector3 which is the "correct" orientation that an object should transform to. So my initial idea was /// <summary> /// Objects stick together /// </summary> /// <returns></returns> private Vector3 Cohesion() { Vector3 result = new Vector3(0.0f, 0.0f, 0.0f); return result; } /// <summary> /// Object align /// </summary> /// <returns></returns> private Vector3 Align() { Vector3 result = new Vector3(0.0f, 0.0f, 0.0f); return result; } /// <summary> /// Object separates from each others /// </summary> /// <returns></returns> private Vector3 Separate() { Vector3 result = new Vector3(0.0f, 0.0f, 0.0f); return result; } Then I search online for pseudocode but many of them involve velocity and acceleration plus other stuffs. This part confused me. In my game, all objects move at constant speed, and they have one leader. So can anyone share me an idea how to start on implement this flocking algorithm? Also, did I understand it correctly? (I'm using XNA 4.0)

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• Algorithm to generate multifaced cube?

  - by OnePie

  Are there any elegant soloution to generate a simple-six sided cube, where each cube is made out of more than one face? The method I have used ended up a horrible and complicated mess of logic that is imopssible to follow and most likely to maintain. The algorithm should not generate reduntant vertices, and should output the indice list for the mesh as well. The reason I need this is that the cubes vertices will be deformed depending on various factors, meaning that a simple six-faced cube will nto do.

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• How to utilize miniMax algorithm in Checkers game

  - by engineer

  I am sorry...as there are too many articles about it.But I can't simple get this. I am confused in the implementation of AI. I have generated all possible moves of computer's type pieces. Now I can't decide the flow. Whether I need to start a loop for the possible moves of each piece and assign score to it.... or something else is to be done. Kindly tell me the proper flow/algorithm for this. Thanks

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• Ideas for attack damage algorithm (language irrelevant)

  - by Dillon

  I am working on a game and I need ideas for the damage that will be done to the enemy when your player attacks. The total amount of health that the enemy has is called enemyHealth, and has a value of 1000. You start off with a weapon that does 40 points of damage (may be changed.) The player has an attack stat that you can increase, called playerAttack. This value starts off at 1, and has a possible max value of 100 after you level it up many times and make it farther into the game. The amount of damage that the weapon does is cut and dry, and subtracts 40 points from the total 1000 points of health every time the enemy is hit. But what the playerAttack does is add to that value with a percentage. Here is the algorithm I have now. (I've taken out all of the gui, classes, etc. and given the variables very forward names) double totalDamage = weaponDamage + (weaponDamage*(playerAttack*.05)) enemyHealth -= (int)totalDamage; This seemed to work great for the most part. So I statrted testing some values... //enemyHealth ALWAYS starts at 1000 weaponDamage = 50; playerAttack = 30; If I set these values, the amount of damage done on the enemy is 125. Seemed like a good number, so I wanted to see what would happen if the players attack was maxed out, but with the weakest starting weapon. weaponDamage = 50; playerAttack = 100; the totalDamage ends up being 300, which would kill an enemy in just a few hits. Even with your attack that high, I wouldn't want the weakest weapon to be able to kill the enemy that fast. I thought about adding defense, but I feel the game will lose consistency and become unbalanced in the long run. Possibly a well designed algorithm for a weapon decrease modifier would work for lower level weapons or something like that. Just need a break from trying to figure out the best way to go about this, and maybe someone that has experience with games and keeping the leveling consistent could give me some ideas/pointers.

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• Algorithm to map an area [on hold]

  - by user37843

  I want to create a crawler that starts in a room and from that room to move North,East,West and South until there aren't any new rooms to visit. I don't want to have duplicates and the output format per line to be something like this: current room, neighbour 1, neighbour 2 ... and in the end to apply BFS algorithm to find the shortest path between 2 rooms. Can anyone offer me some suggestion what to use? Thanks

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• Is Work Stealing always the most appropriate user-level thread scheduling algorithm?

  - by Il-Bhima

  I've been investigating different scheduling algorithms for a thread pool I am implementing. Due to the nature of the problem I am solving I can assume that the tasks being run in parallel are independent and do not spawn any new tasks. The tasks can be of varying sizes. I went immediately for the most popular scheduling algorithm "work stealing" using lock-free deques for the local job queues, and I am relatively happy with this approach. However I'm wondering whether there are any common cases where work-stealing is not the best approach. For this particular problem I have a good estimate of the size of each individual task. Work-stealing does not make use of this information and I'm wondering if there is any scheduler which will give better load-balancing than work-stealing with this information (obviously with the same efficiency). NB. This question ties up with a previous question.

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• Understanding Process Scheduling in Oracle Solaris

  - by rickramsey

  The process scheduler in the Oracle Solaris kernel allocates CPU resources to processes. By default, the scheduler tries to give every process relatively equal access to the available CPUs. However, you might want to specify that certain processes be given more resources than others. That's where classes come in. A process class defines a scheduling policy for a set of processes. These three resources will help you understand and manage it process classes: Blog: Overview of Process Scheduling Classes in the Oracle Solaris Kernel by Brian Bream Timesharing, interactive, fair-share scheduler, fixed priority, system, and real time. What are these? Scheduling classes in the Solaris kernel. Brian Bream describes them and how the kernel manages them through context switching. Blog: Process Scheduling at the Thread Level by Brian Bream The Fair Share Scheduler allows you to dispatch processes not just to a particular CPU, but to CPU threads. Brian Bream explains how to use and provides examples. Docs: Overview of the Fair Share Scheduler by Oracle Solaris Documentation Team This official Oracle Solaris documentation set provides the nitty-gritty details for setting up classes and managing your processes. Covers: Introduction to the Scheduler CPU Share Definition CPU Shares and Process State CPU Share Versus Utilization CPU Share Examples FSS Configuration FSS and Processor Sets Combining FSS With Other Scheduling Classes Setting the Scheduling Class for the System Scheduling Class on a System with Zones Installed Commands Used With FSS -Rick Follow me on: Blog | Facebook | Twitter | Personal Twitter | YouTube | The Great Peruvian Novel

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• Algorithm for querying linearly through a non-linear list of questions

  - by JoshLeaves

  For a multiplayers trivia game, I need to supply my users with a new quizz in a desired subject (Science, Maths, Litt. and such) at the start of every game. I've generated about 5K quizzes for each subject and filled my database with them. So my 'Quizzes' database looks like this: |ID |Subject |Question +-----+------------+---------------------------------- | 23 |Science | What's water? | 42 |Maths | What's 2+2? | 99 |Litt. | Who wrote "Pride and Prejudice"? | 123 |Litt. | Who wrote "On The Road"? | 146 |Maths | What's 2*2? | 599 |Science | You know what's cool? |1042 |Maths | What's the Fibonacci Sequence? |1056 |Maths | What's 42? And so on... (Much more detailed/complex but I'll keep the exemple simple) As you can see, due to technical constraints (MongoDB), my IDs are not linear but I can use them as an increasing suite. So far, my algorithm to ensure two users get a new quizz when they play together is the following: // Take the last played quizzes by P1 and P2 var q_one = player_one.getLastPlayedQuizz('Maths'); var q_two = player_two.getLastPlayedQuizz('Maths'); // If both of them never played in the subject, return first quizz in the list if ((q_one == NULL) && (q_two == NULL)) return QuizzDB.findOne({subject: 'Maths'}); // If one of them never played, play the next quizz for the other player // This quizz is found by asking for the first quizz in the desired subject where // the ID is greater than the last played quizz's ID (if the last played quizz ID // is 42, this will return 146 following the above example database) if (q_one == NULL) return QuizzDB.findOne({subject: 'Maths', ID > q_two}); if (q_two == NULL) return QuizzDB.findOne({subject: 'Maths', ID > q_one}); // And if both of them have a lastPlayedQuizz, we return the next quizz for the // player whose lastPlayedQuizz got the higher ID if (q_one > q_two) return QuizzDB.findOne({subject: 'Maths', ID > q_one}); else return QuizzDB.findOne({subject: 'Maths', ID > q_two}); Now here comes the real problem: Once I get to the end of my database (let's say, P1's last played quizz in 'Maths' is 1056, P2's is 146 and P3 is 1042), following my algorithm, P1's ID is the highest so I ask for the next question in 'Maths' where ID is superior to 1056. There is nothing, so I roll back to the beginning of my quizz list (with a random skipper to avoid having the first question always show up). P1 and P2's last played will then be 42 and they will start fresh from the beginning of the list. However, if P1 (42) plays against P3 (1042), the resulting ID will be 1056...which P1 already played two games ago. Basically, players who just "rolled back" to the beginning of the list will be brought back to the end of the list by players who still haven't rolled back. The rollback WILL happen in the end, but it'll take time and there'll be a "bottleneck" at the beginning and at the end. Thus my question: What would be the best algorith to avoid this bottleneck and ensure players don't get stuck endlessly on the same quizzes? Also bear in mind that I've got some technical constraints: I can't get a random question in a subject (ie: no "QuizzDB.findOne({subject: 'Maths'}).skip(random());"). It's cool to skip on one to twenty records, but the MongoDB documentation warns against skipping too many documents. I would like to avoid building an array of every quizz played by each player and find the next non-played in the database with a $nin. Thanks for your help

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