Older blog entries for sness (starting at number 5013)

What We Learned in 2012 · An A List Apart Article

What We Learned in 2012 · An A List Apart Article: "Design systems, not screens

More than half of U.S. laptop owners now also own a smartphone, and nearly a quarter of them own a tablet too (source). And, of course, with the holiday season past us, the number of users who own a device in all three categories will jump higher still. Users move between devices so fluidly, and in patterns that we often can’t predict. Now apps are starting to connect to other devices to control, synchronize or extend an experience.

I think we’re going to see more cross-channel design thinking in 2013 to address simultaneous multi-device usage, and frequent device hopping in a single workflow. Continuity between platforms will be important, but we don’t need to make the experience the same between devices. The user experience will morph with each context. We’ll need to design systems, not screens, to solve cross-channel experience design problems."

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Syndicated 2013-01-27 17:47:00 from sness

What We Learned in 2012 · An A List Apart Article

What We Learned in 2012 · An A List Apart Article: "For me 2012 was a year of experimentation. I learned that the more certain you are about something, or the longer you’ve been doing things one way, the more important it is to abandon your assumptions and try the complete opposite. The more embedded your assumptions are, the less you notice them—so this is not easy.

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Syndicated 2013-01-27 17:46:00 from sness

Optimal substructure - Wikipedia, the free encyclopedia

Optimal substructure - Wikipedia, the free encyclopedia: "In computer science, a problem is said to have optimal substructure if an optimal solution can be constructed efficiently from optimal solutions of its subproblems. This property is used to determine the usefulness of dynamic programming and greedy algorithms for a problem.[1]"

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Syndicated 2013-01-27 08:48:00 from sness

Dynamic programming - Wikipedia, the free encyclopedia

Dynamic programming - Wikipedia, the free encyclopedia: "Optimal substructure means that the solution to a given optimization problem can be obtained by the combination of optimal solutions to its subproblems. Consequently, the first step towards devising a dynamic programming solution is to check whether the problem exhibits such optimal substructure. Such optimal substructures are usually described by means of recursion. For example, given a graph G=(V,E), the shortest path p from a vertex u to a vertex v exhibits optimal substructure: take any intermediate vertex w on this shortest path p. If p is truly the shortest path, then the path p1 from u to w and p2 from w to v are indeed the shortest paths between the corresponding vertices (by the simple cut-and-paste argument described in Introduction to Algorithms). Hence, one can easily formulate the solution for finding shortest paths in a recursive manner, which is what the Bellman-Ford algorithm or the Floyd-Warshall algorithm does.
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Syndicated 2013-01-27 08:48:00 from sness

Python - Dijkstra's Algorithm - Stack Overflow

Python - Dijkstra's Algorithm - Stack Overflow: "
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down vote
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As mentioned above, you can use an instance of an object.

This author has a pretty convincing python implementation of Dijkstras in python.

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Syndicated 2013-01-27 01:21:00 from sness

Dijkstra's algorithm - Wikipedia, the free encyclopedia

Dijkstra's algorithm - Wikipedia, the free encyclopedia: "For the current node, consider all of its unvisited neighbors and calculate their tentative distances. For example, if the current node A is marked with a distance of 6, and the edge connecting it with a neighbor B has length 2, then the distance to B (through A) will be 6+2=8. If this distance is less than the previously recorded tentative distance of B, then overwrite that distance. Even though a neighbor has been examined, it is not marked as "visited" at this time, and it remains in the unvisited set."

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Syndicated 2013-01-27 01:21:00 from sness

Dijkstra's algorithm for shortest paths « Python recipes « ActiveState Code

Dijkstra's algorithm for shortest paths « Python recipes « ActiveState Code: "

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Dijkstra(G,s) finds all shortest paths from s to each other vertex in the graph, and shortestPath(G,s,t) uses Dijkstra to find the shortest path from s to t. Uses the priorityDictionary data structure (Recipe 117228) to keep track of estimated distances to each vertex.

Python, 87 lines"

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Syndicated 2013-01-27 01:20:00 from sness

Lecture 10 - Dynamic Programming - YouTube

Lecture 10 - Dynamic Programming - YouTube: "