Clusterpath: An Algorithm for Clustering using Convex
... return table of all optimal α and λ values. Surprisingly, the `2 path is not always agglomerative, and in this case to reach the optimal solution requires restarting clusters = {{1}, ..., {n}}. The clusters will rejoin in the next call to S OLVE -L2 if necessary. This takes more time but ensures tha ...
... return table of all optimal α and λ values. Surprisingly, the `2 path is not always agglomerative, and in this case to reach the optimal solution requires restarting clusters = {{1}, ..., {n}}. The clusters will rejoin in the next call to S OLVE -L2 if necessary. This takes more time but ensures tha ...
here
... enabling them to make decisions without human intervention. Full autonomy has two clear benefits over pre-programming and human remote control. First, in contrast to sensors with pre-programmed motion paths, autonomous sensors are better able to adapt to their environment, and react to a priori unkn ...
... enabling them to make decisions without human intervention. Full autonomy has two clear benefits over pre-programming and human remote control. First, in contrast to sensors with pre-programmed motion paths, autonomous sensors are better able to adapt to their environment, and react to a priori unkn ...
Laplace transforms of probability distributions
... h̃′1 (k) = H̃1′ (k)/K̃(k) to zero that lie beyond the absolute minimum of the power spectrum of h′1 (y), estimated by some local averaging of |h̃′1 (k)|2 . This procedure is justified, because smoothness arguments suggest that |h̃′1 (k)|2 → 0 as |k| → ∞. The numerical value of the minimum power give ...
... h̃′1 (k) = H̃1′ (k)/K̃(k) to zero that lie beyond the absolute minimum of the power spectrum of h′1 (y), estimated by some local averaging of |h̃′1 (k)|2 . This procedure is justified, because smoothness arguments suggest that |h̃′1 (k)|2 → 0 as |k| → ∞. The numerical value of the minimum power give ...
Routing
... ◦ Claim 2. Dj is, for each j, the shortest distance between j and 1, using paths whose nodes all belong to P (except, possibly, j) • Given the above two properties ◦ When algorithm stops, the shortest path lengths must be equal to Dj , for all j → That is, algorithm finds the shortest path as desire ...
... ◦ Claim 2. Dj is, for each j, the shortest distance between j and 1, using paths whose nodes all belong to P (except, possibly, j) • Given the above two properties ◦ When algorithm stops, the shortest path lengths must be equal to Dj , for all j → That is, algorithm finds the shortest path as desire ...
Range-Efficient Counting of Distinct Elements in a Massive Data
... present a novel algorithm for range-efficient computation of F0 of a data stream that provides the current best time and space bounds. It is well known [AMS99] that exact computation of the F0 of a data stream requires space linear in the size of the input in the worst case. In fact, even deterministi ...
... present a novel algorithm for range-efficient computation of F0 of a data stream that provides the current best time and space bounds. It is well known [AMS99] that exact computation of the F0 of a data stream requires space linear in the size of the input in the worst case. In fact, even deterministi ...
Time-Memory Trade-Off for Lattice Enumeration in a Ball
... for SVP and γ-CVP in 3n+o(n) . Contrary to Kannan enumeration, the running time is better but we need exponential memory. We also show that we can have a time/memory tradeoff with polynomial memory which is however worse than Kannan algorithm. ...
... for SVP and γ-CVP in 3n+o(n) . Contrary to Kannan enumeration, the running time is better but we need exponential memory. We also show that we can have a time/memory tradeoff with polynomial memory which is however worse than Kannan algorithm. ...
Document
... An edge {i,j} is ambivalent if there is a minimum weight perfect matching that contains it and another that does not If minimum not unique, at least one edge is ambivalent Assign weights to all edges except {i,j} Let aij be the largest weight for which {i,j} participates in some minimum weight perfe ...
... An edge {i,j} is ambivalent if there is a minimum weight perfect matching that contains it and another that does not If minimum not unique, at least one edge is ambivalent Assign weights to all edges except {i,j} Let aij be the largest weight for which {i,j} participates in some minimum weight perfe ...