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The complete bipartite graph $K_{r,s}$ has vertex classes of sizes $r$ and $s$ and contains all $rs$ edges between the classes.

If a square bipartite adjacency matrix $B$ has nonzero determinant, some product in its Leibniz expansion is nonzero. Its permutation selects one edge at each vertex and therefore gives a perfect matching.

Coefficient extraction from $(1-x{)}^n(1+x{)}^n=(1-x^2{)}^n$ gives

If square matrices $A$ and $B$ commute, thenThe usual proof works because commutativity allows words with the same numbers of $A$ and $B$ factors to be collected.

Partition the vertices into two sets of comparable size. If $np\to \infty$, the first set contains a triangle with probability tending to one. Conditional on any triangle chosen using only internal edges, the probability that none of its vertices has a neighbour in the second set is at mostHence $G(n,p)$ contains a triangle with an attached leaf with probability tending to one.

If $X$ counts triangles in $G(n,p)$, thenandThus $np\to \infty$ implies $\mathrm{var}X/(\mathbb{E}X{)}^2\to 0$, and the second moment method gives $\mathbb{P}(X>0)\to 1$.

If $X_t$ counts copies of the complete graph $K_t$ in $G(n,p)$, thenConsequently $p{n}^{2/(t-1)}\to 0$ implies $\mathbb{P}(X_t>0)\to 0$ by the first moment method.

If $p<1/4$, choose $p<\delta <1/4$. Binary codes of relative minimum distance above $2\delta$ have positive asymptotic rate by a greedy packing bound, and nearest-codeword decoding fails only when more than $\delta n$ channel errors occur. That probability tends to zero.

At crossover probability $p=1/2$, the two rows of the transition matrix of a binary symmetric channel coincide. Its output is then independent of its input and its capacity is zero.

An error is undetected by codeword-membership checking when the received word is a different valid codeword. For a linear code this occurs exactly when the nonzero channel-error vector is itself a codeword.

A Roche lobe is the region around one component of a circular binary inside the critical effective-potential surface through the inner Lagrange point.

An equivalence relation says which set elements should count as the same for a particular purpose. In modular arithmetic, for example, distinct integers with the same remainder are equivalent. The relation gathers interchangeable set elements into equivalence classes; the quotient set then treats each whole equivalence class as one object.

Formally, an equivalence relation on a set $X$ is a binary relation $R\subseteq X\times X$, written $x\sim y$, satisfying three conditions for all $x,y,z\in X$:These conditions make the equivalence classes a partition of a set: each set element belongs to exactly one class. ^[ref]

For a worked example, take the finite set $X=\{0,1,\dots ,8\}$ and restrict integer congruence modulo three to $X$:Reflexivity follows because $x-x=0$. The symmetric relation property follows by changing the sign of a difference, and transitivity follows by adding $x-y$ and $y-z$: divisibility by three is preserved in both steps. Thus $0\sim 3$ and $3\sim 6$ force $0\sim 6$. The three equivalence classes are $\{0,3,6\}$, $\{1,4,7\}$ and $\{2,5,8\}$. Their members alternate along the number line; membership in an equivalence class need not mean being close together.

The transitivity requirement rules out a tempting alternative. On $\{0,1,2\}$, define nearness by the absolute value condition $|x-y|\le 1$. This binary relation is reflexive and symmetric, but $0$ is near $1$ and $1$ is near $2$ while $0$ is not near $2$. Its neighborhoods $\{0,1\}$, $\{0,1,2\}$ and $\{1,2\}$ overlap without being equal. They are not equivalence classes or a partition of a set.

A relation $R$ is transitive when $xRy$ and $yRz$ imply $xRz$.

A relation $R$ is symmetric when $xRy$ implies $yRx$.

A relation $R$ on $X$ is reflexive when $xRx$ for every $x\in X$.

The Neyman-Pearson lemma says that an optimal region accepting $P$ has the likelihood-ratio formwith possible randomization on the boundary.

For distinct probability distributions $P,Q$, the optimal error exponents in hypothesis testing give type-II error $Q^{\otimes n}(B_n)$ decay rate $D(P\Vert Q)$ among tests whose type-I error $P^{\otimes n}(B_n^c)$ stays below any fixed number in $(0,1)$.

A dyadic rational has the form $m/2^k$. Its terminating binary expansion has an alternative expansion ending in infinitely many $1$ digits.

The Hamming distance between two equal-length words is the number of coordinates in which they differ.

A Nash equilibrium is a strategy profile in which every player's strategy is a best response to the others. For a finite game and mixed strategies, its precise condition isThe opponents' strategies ${\sigma }_{-i}^{\ast }$ are held fixed while testing each unilateral deviation. Equality is allowed: a player may have other equally good best responses. Because expected payoff is a linear function of the player's own mixture, it suffices to test every pure strategy. Equivalently, every choice in the support of a mixed strategy must attain the largest pure-choice expected payoff, and no unused choice may exceed it.

The two worked examples illustrate different uses of this condition. Matching pennies has no pure strategy Nash equilibrium, but its unique mixed strategy Nash equilibrium removes each player's vulnerability to predictable play. The asymmetric coordination example has three Nash equilibria, and its mixed one fails Pareto efficiency. A Nash equilibrium tests individual incentives, whereas Pareto efficiency compares feasible outcomes that may require several players to change together. In the prisoner's dilemma, for example, mutual defection is a Nash equilibrium even though mutual cooperation gives both players a larger payoff because $R>P$.

Existence in a finite game is guaranteed by Nash's theorem. The theorem does not select one Nash equilibrium, promise Pareto efficiency, or assert convergence of a learning process. Nash's ^[ref] used the Kakutani fixed-point theorem; his ^[ref] gave a proof using the Brouwer fixed-point theorem. The connection to a fixed point captures the idea of choices that remain mutually optimal when considered together.