Translation of "n terms of" to Japanese language:
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 | 北 |
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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