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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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  • Voxel terrain rendering with marching cubes

    - by JavaJosh94
    I was working on making procedurally generated terrain using normal cubish voxels (like minecraft) But then I read about marching cubes and decided to convert to using those. I managed to create a working marching cubes class and cycle through the densities and everything in it seemed to be working so I went on to work on actual terrain generation. I'm using XNA (C#) and a ported libnoise library to generate noise for the terrain generator. But instead of rendering smooth terrain I get a 64x64 chunk (I specified 64 but can change it) of seemingly random marching cubes using different triangles. This is the code I'm using to generate a "chunk": public MarchingCube[, ,] getTerrainChunk(int size, float dMultiplyer, int stepsize) { MarchingCube[, ,] temp = new MarchingCube[size / stepsize, size / stepsize, size / stepsize]; for (int x = 0; x < size; x += stepsize) { for (int y = 0; y <size; y += stepsize) { for (int z = 0; z < size; z += stepsize) { float[] densities = {(float)terrain.GetValue(x, y, z)*dMultiplyer, (float)terrain.GetValue(x, y+stepsize, z)*dMultiplyer, (float)terrain.GetValue(x+stepsize, y+stepsize, z)*dMultiplyer, (float)terrain.GetValue(x+stepsize, y, z)*dMultiplyer, (float)terrain.GetValue(x,y,z+stepsize)*dMultiplyer,(float)terrain.GetValue(x,y+stepsize,z+stepsize)*dMultiplyer,(float)terrain.GetValue(x+stepsize,y+stepsize,z+stepsize)*dMultiplyer,(float)terrain.GetValue(x+stepsize,y,z+stepsize)*dMultiplyer }; Vector3[] corners = { new Vector3(x,y,z), new Vector3(x,y+stepsize,z),new Vector3(x+stepsize,y+stepsize,z),new Vector3(x+stepsize,y,z), new Vector3(x,y,z+stepsize), new Vector3(x,y+stepsize,z+stepsize), new Vector3(x+stepsize,y+stepsize,z+stepsize), new Vector3(x+stepsize,y,z+stepsize)}; if (x == 0 && y == 0 && z == 0) { temp[x / stepsize, y / stepsize, z / stepsize] = new MarchingCube(densities, corners, device); } temp[x / stepsize, y / stepsize, z / stepsize] = new MarchingCube(densities, corners); } } } (terrain is a Perlin Noise generated using libnoise) I'm sure there's probably an easy solution to this but I've been drawing a blank for the past hour. I'm just wondering if the problem is how I'm reading in the data from the noise or if I may be generating the noise wrong? Or maybe the noise is just not good for this kind of generation? If I'm reading it wrong does anyone know the right way? the answers on google were somewhat ambiguous but I'm going to keep searching. Thanks in advance!

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  • Finding cubes in frustum

    - by salmonmoose
    Working with an infinite set of cubes, is there a way of detecting which cubes exist within a frustum? Most frustum culling seems to work along the lines of running through all objects and seeing if they intersect - this is ok with a finite set of objects, or something like Octrees. I'm currently finding all cubes within the frustum's bounding box - but that's far more than I really need. I could then test these all against it, but I was wondering if I could skip a step.

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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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  • 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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  • 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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  • Search algorithm (with a sort algorithm already implemented)

    - by msr
    Hello, Im doing a Java application and Im facing some doubts in which concerns performance. I have a PriorityQueue which guarantees me the element removed is the one with greater priority. That PriorityQueue has instances of class Event (which implements Comparable interface). Each Event is associated with a Entity. The size of that priorityqueue could be huge and very frequently I will have to remove events associated to an entity. Right now Im using an iterator to run all the priorityqueue. However Im finding it heavy and I wonder if there are better alternatives to search and remove events associated with an entity "xpto". Any suggestions? Thanks!

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  • Help:Graph contest problem: maybe a modified Dijkstra or another alternative algorithm

    - by newba
    Hi you all, I'm trying to do this contest exercise about graphs: XPTO is an intrepid adventurer (a little too temerarious for his own good) who boasts about exploring every corner of the universe, no matter how inhospitable. In fact, he doesn't visit the planets where people can easily live in, he prefers those where only a madman would go with a very good reason (several millions of credits for instance). His latest exploit is trying to survive in Proxima III. The problem is that Proxima III suffers from storms of highly corrosive acids that destroy everything, including spacesuits that were especially designed to withstand corrosion. Our intrepid explorer was caught in a rectangular area in the middle of one of these storms. Fortunately, he has an instrument that is capable of measuring the exact concentration of acid on each sector and how much damage it does to his spacesuit. Now, he only needs to find out if he can escape the storm. Problem The problem consists of finding an escape route that will allow XPTOto escape the noxious storm. You are given the initial energy of the spacesuit, the size of the rectangular area and the damage that the spacesuit will suffer while standing in each sector. Your task is to find the exit sector, the number of steps necessary to reach it and the amount of energy his suit will have when he leaves the rectangular area. The escape route chosen should be the safest one (i.e., the one where his spacesuit will be the least damaged). Notice that Rodericus will perish if the energy of his suit reaches zero. In case there are more than one possible solutions, choose the one that uses the least number of steps. If there are at least two sectors with the same number of steps (X1, Y1) and (X2, Y2) then choose the first if X1 < X2 or if X1 = X2 and Y1 < Y2. Constraints 0 < E = 30000 the suit's starting energy 0 = W = 500 the rectangle's width 0 = H = 500 rectangle's height 0 < X < W the starting X position 0 < Y < H the starting Y position 0 = D = 10000 the damage sustained in each sector Input The first number given is the number of test cases. Each case will consist of a line with the integers E, X and Y. The following line will have the integers W and H. The following lines will hold the matrix containing the damage D the spacesuit will suffer whilst in the corresponding sector. Notice that, as is often the case for computer geeks, (1,1) corresponds to the upper left corner. Output If there is a solution, the output will be the remaining energy, the exit sector's X and Y coordinates and the number of steps of the route that will lead Rodericus to safety. In case there is no solution, the phrase Goodbye cruel world! will be written. Sample Input 3 40 3 3 7 8 12 11 12 11 3 12 12 12 11 11 12 2 1 13 11 11 12 2 13 2 14 10 11 13 3 2 1 12 10 11 13 13 11 12 13 12 12 11 13 11 13 12 13 12 12 11 11 11 11 13 13 10 10 13 11 12 8 3 4 7 6 4 3 3 2 2 3 2 2 5 2 2 2 3 3 2 1 2 2 3 2 2 4 3 3 2 2 4 1 3 1 4 3 2 3 1 2 2 3 3 0 3 4 10 3 4 7 6 3 3 1 2 2 1 0 2 2 2 4 2 2 5 2 2 1 3 0 2 2 2 2 1 3 3 4 2 3 4 4 3 1 1 3 1 2 2 4 2 2 1 Sample Output 12 5 1 8 Goodbye cruel world! 5 1 4 2 Basically, I think we have to do a modified Dijkstra, in which the distance between nodes is the suit's energy (and we have to subtract it instead of suming up like is normal with distances) and the steps are the ....steps made along the path. The pos with the bester binomial (Energy,num_Steps) is our "way out". Important : XPTO obviously can't move in diagonals, so we have to cut out this cases. I have many ideas, but I have such a problem implementing them... Could someone please help me thinking about this with some code or, at least, ideas? Am I totally wrong?

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  • What problems have you solved using genetic algorithms/genetic programming?

    - by knorv
    Genetic algorithms (GA) and genetic programming (GP) are interesting areas of research. I'd like to know about specific problems you - the SO reader - have solved using GA/GP and what libraries/frameworks you used if you didn't roll your own. Questions: What problems have you used GA/GP to solve? What libraries/frameworks did you use? I'm looking for first-hand experiences, so please do not answer unless you have that.

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  • Best Fit Scheduling Algorithm

    - by Teegijee
    I'm writing a scheduling program with a difficult programming problem. There are several events, each with multiple meeting times. I need to find an arrangement of meeting times such that each schedule contains any given event exactly once, using one of each event's multiple meeting times. Obviously I could use brute force, but that's rarely the best solution. I'm guessing this is a relatively basic computer science problem, which I'll learn about once I am able to start taking computer science classes. In the meantime, I'd prefer any links where I could read up on this, or even just a name I could Google.

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