Translation of "n terms of" to Japanese language:


  Dictionary English-Japanese

N terms of - translation : Terms - translation :

  Examples (External sources, not reviewed)

lower bound on N in terms of k, let's have an upper bound on k in terms of N.
つまり ルートからのあらゆる経路の長さの最小値は
Now, we have two n terms.
f1 x g1 y とf1 x g1 y dy dxのように
Each N plus 1 corresponds to each of these terms, so there are N of these.
Nこあります つまり N 1をN回 足すことになります
And we have n terms like that.
これらの項を足していけば
Russian(n, n) for lots of different values of n.
しかし結果は期待外れでした
Plus, and we have n terms, plus fn of x gn prime of y, and then all of these terms are multiplied by dy dx.
このすべての項は dy dx によって乗算されます 今 何かここで面白そうだね
So it's Θ(n²). It's order of n², as well as order of n³.
(log n)7は9n(log n)²より優位 n² ³は(log n)7より優位
So the running time of Dijkstra in terms of n and M and here's a sketch of the algorithm again.
ノードについて最短の距離を調べ
It's n times the factorial of n 1.
この部分は再帰的関数の呼び出しです
And if you include just the second order terms, that is, the terms that are a product of, you know, two of these terms, x1 times x1 and so on, then, for the case of n equals
つまり これらの2つの 積の項
Is it n, n 1 over n, n over n 1, n divided by n 1, n 1 divided by n, or is it n squared 1 over n 1?
それとも n² 1 1でしょうか ここでnは標本集合におけるデータ点の 個数であることに注意してください
Number of terms
項数
What's the running time of Dijkstra in terms of the number of nodes (n) and edges (m) when heaps are used?
ダイクストラ法での実行時間はどうなるでしょう ここで言うヒープとは
I'm in awe of their power in terms of imagination, in terms of technology, in terms of concept.
そしてコンセプトといった点に 畏怖しています しかしそれ以上に
Is Fibonacci of N always lt or N 1?
1は2よりも小さく2も3以下ですね
Θ(n²), O(n²), and O(n³).
ビッグ オー記法は上界なのでO(n²)が有効です
So usually anything which is more than N times logarythmic terms, you'd think of that as a dense graph.
それは密なグラフ しかし 再び 人々 がこれでもう少しずさんなです
N by N, kind of like we have up here.
でも普通は次元は暗黙的にしておく
You may have noticed that I wrote 1 times N times factorial of N minus 1 instead of just N times factorial of N minus 1.
N factorial N 1)と同じことですね 何かに1を掛けても値は変わらないので 正しいですが遅くなります
In terms of invention,
プロジェクトのお話を させて頂きます
Is the depth that we get this way log n, n, or 2 n, and are the number of leaves at the bottom log n, n, or 2 n?
リーフの数はlog n n 2ⁿのどれでしょうか
Okay. So basically (log n)⁷ beats 9n(log n)² and n² ³ beats (log n)⁷ and 9n(log n)² beats n² ³.
9n(log n)²はn² ³より優位なので
It solely uses the terms over here. In particular, we have the ΣXi², we have N.
N²はNから簡単に求められ ΣXi²ですべての総和が出ます
Now, if K is n 2 that's n times n 2 where n².
n 2のn倍はnの2乗 Θ(n²)になります
So I subscript n by n is the n by n identity matrix.
n掛けるnの単位行列 というのは おのおのの次元nごとに
So altogether we're talking about n n² or n³.
ダイクストラ法をより密度の高いグラフに適用した時の n³ log nよりよい値です
n times n minus one times n minus two.
何回行ったのかを把握する変数だけでなく
And 8 n we can think of a just Θ( n).
前に出てきた2n 2 nという式ならΘ(n)です
If we divide through by n, we get 2 n n and divided by n is actually n.
すると2 nです
In terms of their discourse, in terms of their sophistication of knowledge of the world,
世界に対しての知識は 私から見ると
So instead of just adding N plus 1 N times, we could say that this is just N times N plus 1.
または N N 1 です つまり 合計の2倍が N N 1 と等しいとなります
If f(n) is in little o(g(n)), that's kind of like saying f(n) is strictly less than g(n).
漸近的には成長率は低くなります
So that reduces compute time from n squared, where n is number of particles, to n log n, which is much easier.
n log nにする これはもっとずっと簡単な計算だ もう一つのクラスの手法として
They have needs which have to be met efficiently in terms of energy, in terms of cost, in terms of quality.
エネルギー効率が良くて 価格が手頃で 質が良いものだということだ けして質の悪い物ではない
So the log of n, log base 2 of n is, you type the number N into your calculator, okay?
割る2 を打つ そしてその
With the very long list that were wanting to tap values of, you might as well just sort the whole thing. n, well what happens with n? n where look we're comparing n log n to n( n) which is n³ ². n³ ² is asymptotically larger than n log n so we're still better off just sorting the whole list.
nの平方根の場合はどうでしょう この場合はn log nを nのn倍 つまりnの3 2乗と比較します この値は漸近的にn log nより大きいので
Bryant's level, in terms of points per game. Also, in terms of variability.
彼はほとんどKobe Bryanと同じ程度に一貫している その期間を通して
n
n
N
N
北緯South, the direction
N
N
N
Nunit description in lists
N
Nunit description in lists
N
N
? N? ?
哲希のチャランポラン どーにかなんねえかな

 

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