Let . Choose a basis of the intersection of vector subspaces, extend it to a basis of , and independently extend it to a basis of . The combined list
spans the sum of vector subspaces . It is also linearly independent: a vanishing linear combination says that a combination of the belongs to , and its expression in the chosen basis of forces all its coefficients to vanish; the coefficients of the then vanish in the same way. This proves the dimension formula for a sum of subspaces
Parametrize the two linear subspaces as
Putting a vector of in first forces its fourth coordinate to vanish; the remaining defining equation is then automatic. Thus
and the dimension formula gives .
The linear functional
vanishes on each displayed generator of and . Its kernel is three-dimensional because , so the inclusion between two three-dimensional subspaces is equality.
Let the torsion submodule of the module over a ring be
This is a submodule. Indeed, if and with , then because the scalar ring is an integral domain, and ; scalar multiples and additive inverses are handled similarly.
Take the quotient module and let be the quotient R-module homomorphism. It is torsion-free: if and , then , so for some . Since , this puts in and therefore .
Now let be torsion-free and let be an -module homomorphism. If and for , then , so torsion-freeness gives . Hence , and the universal property of a quotient module gives a unique homomorphism
with . Thus is the maximal torsion-free quotient of .
For , all component functions are continuously differentiable and the Jacobian matrix is
At a point , the first component cannot be differentiable: along the first coordinate axis its difference quotient contains , which is unbounded as . Therefore is continuously differentiable exactly at the points with .
At a point with , the determinant of the derivative is
The inverse function theorem therefore supplies neighbourhoods on which has a continuously differentiable, hence differentiable, inverse. Concretely, if , the local inverse is the restriction of
which is continuously differentiable near the image whenever .
Suppose for contradiction that the continuous image of a connected space is disconnected. Then there are disjoint nonempty sets , open in the subspace topology on , with . By continuity, and are disjoint open subsets of ; by surjectivity both are nonempty, and their union is . This is a separation of a topological space of , contradicting that is a connected space. Hence is connected.
First recall from the least-upper-bound property that every real interval is connected. For completeness, if were a separation with , let be the supremum of the points for which remains in . Relative openness rules out both and : in the first case one can move slightly to the right while staying in , and in the second one finds points of slightly to the left of , contrary to the definition of . Thus no separation exists.
Part (a) now makes a connected subset of the real line. Such a set is an interval: if it contained but omitted , its intersections with and would separate it. Since contains and , it consequently contains every number between them. Therefore for each such there is an with . This derives the intermediate value theorem from connectedness.
For an axisymmetric separated solution of the Laplace equation whose angular factor is the Legendre polynomial , the radial ordinary differential equation has the two powers and . Thus
for constants .
Write . Since , only the spherical harmonics of degrees zero and two occur. The degree-zero radial coefficient satisfying its two Dirichlet boundary conditions is . For degree two, write ; then
so and . By linearity and uniqueness of this boundary value problem,
Direct substitution at verifies both boundary values.
The free-space Green function of the Laplacian for the Poisson equation gives the electric potential
For , its electric multipole expansion begins with
Define the total electric charge and electric dipole moment by
Then
The analogous solution for the magnetic vector potential is
Its apparent coefficient is . For each component, the supplied identity and the divergence-free vector field condition give
The divergence theorem turns this into a surface integral outside the compact support of the current density, where . Hence , so the term vanishes.
The velocity field is incompressible because
With the convention and , a stream function is
The vorticity is vertical:
For a steady inviscid incompressible flow, the vorticity equation is . Here the right side is zero because the flow is independent of , while the left side is because velocity is tangent to the level sets of . Thus the equation is satisfied.
The streamlines are the level curves . The lines and , together with the boundary, are separatrices; each of the four cells contains closed nested curves around a centre at , , , or .
Let be the number of heads. The likelihood function for the Bernoulli distribution sample is proportional to . A uniform distribution prior on is the distribution, so Beta-binomial conjugacy gives the posterior distribution
The quadratic loss, also called squared-error loss, for reporting an action when the parameter is is
For any action , decompose the posterior expected quadratic loss around the posterior mean:
The unique minimizer is therefore the Bayes estimator under squared error loss. Using the mean of the posterior Beta distribution gives
Under repeated sampling at parameter value , has the binomial distribution with expected value . Consequently
so its bias of an estimator is .
The Lagrange sufficiency theorem for maximizing subject to and states that if a feasible point and multipliers satisfy complementary slackness and globally maximizes the Lagrangian function in constrained optimization
then globally maximizes on the feasible set.
For the equality constraint, take
The Lagrange multiplier equations give
so positivity and the constraint imply
For this multiplier, is a strictly concave function on the positive octant and its stationary point is its unique global maximum. The sufficiency theorem therefore proves that the constrained maximum is
For , use . The same point and multiplier satisfy feasibility, stationarity, , and complementary slackness, and they again globally maximize the Lagrangian. Hence the answer is unchanged. Equivalently, any point with strict inequality can be radially scaled outward, increasing all three positive coordinates and therefore increasing .
If is an invertible matrix, then
Thus and are similar matrices, and similar matrices have the same characteristic polynomial.
For all but finitely many , the matrix is invertible. Part (a), applied to and , then gives
Both sides are polynomials in the two variables . Since they agree for infinitely many values of , their coefficients as polynomials in agree. Setting yields
even when is singular.
Take
Then and , whereas . Hence their minimal polynomials are
For every nonnegative integer ,
Consequently, if , then
By the divisibility characterization of the minimal polynomial,
Interchanging and also gives . Therefore the Minimal polynomials of AB and BA can differ only in the exponent of the factor , and that exponent differs by at most one.
If is diagonalizable, its minimal polynomial has no repeated roots. The preceding divisibility says that every nonzero Jordan block of has size one and every zero-eigenvalue block has size at most two. Squaring preserves the one-dimensional nonzero blocks and sends every size-at-most-two nilpotent block to zero. Thus the Jordan normal form of is diagonal, so
Because is irreducible, the ideal is maximal in the polynomial ring . Hence
is a field extension of . Let . Evaluation in the quotient gives , and the factor theorem in gives
for some . This is the standard adjoining a root of an irreducible polynomial construction.
Proceed by induction on . A nonconstant polynomial over a field has an irreducible factor . Part (a) gives an extension containing a root of , hence of . The factor theorem gives
where is monic of degree . Apply the induction hypothesis over and compose the resulting field extensions. The final field contains roots and
Equivalently, every polynomial has a splitting field.
Put . Every extension of has characteristic , so the iterated Frobenius endomorphism satisfies
The root set is . It contains and . If , then
so it is closed under subtraction and multiplication. If is nonzero, then
Thus is closed under multiplicative inverses as well, and is therefore a field. This is the polynomial characterization of a finite field; in fact it is the finite field with elements, since has degree and no repeated roots.
Two norms and on one vector space are Lipschitz equivalent when constants exist such that
Choose a basis of the finite-dimensional vector space and let be the maximum absolute coordinate. For any norm , the triangle inequality gives
so is continuous in the coordinate topology. On the compact set , the positive continuous function attains a positive minimum and a finite maximum . By absolute homogeneity,
This proves the equivalence of norms in finite dimensions: every norm is equivalent to the maximum norm, and transitivity of these inequalities proves that any two norms on a finite-dimensional vector space are Lipschitz equivalent.
Suppose
If is a Cauchy sequence in , then
Conversely, . Hence the sequence is Cauchy for one norm exactly when it is Cauchy for the other.
For each positive integer , let have its first coordinates equal to one and all later coordinates zero. This is an element of the l-p sequence space for every finite , and
No constant can bound by a fixed multiple of for all . Thus and are not Lipschitz equivalent.
For , the same finitely supported sequence gives
One of the two inequalities required for equivalent norms therefore fails. Hence
Let be tangent vectors to the two smooth curves at . complex differentiability at a point gives
When , its real derivative is multiplication by a nonzero complex number, which is a rotation followed by a positive scaling. It therefore preserves the angle between and , so is conformal at . The condition is essential: has and sends rays making angle at zero to rays making angle .
For ,
If both satisfy , or both satisfy , the alternative is impossible unless the points lie on the omitted unit circle. Hence is one-to-one on each region. Also has no zero there, so the restrictions are conformal. Solving shows that exactly when both roots lie on the unit circle; otherwise one root is inside and the other outside. Thus each region has image
Taking the reciprocal of the interior restriction and filling its removable value at zero gives
The reciprocal sends onto
and . Therefore is the required one-to-one conformal map from the unit disc.
A smooth surface in is a subset locally parametrized by a smooth map of two variables whose derivative has rank two and which is a homeomorphism onto its image.
Writing , the first surface equation becomes
It is the torus of major radius and minor radius , with smooth parametrization
Since , this is an embedding. The Gaussian curvature of a torus is
For the second surface, use the orthogonal transformation
Its equation becomes the same torus equation . Orthogonal transformations are Euclidean isometries and preserve Gaussian curvature. Curvature vanishes where , equivalently . In the original coordinates the zero-curvature points are therefore exactly
The two Euler-Lagrange equations are
Differentiating along an extremal and using these equations gives . Hence the Beltrami identity makes constant when has no explicit dependence on . If omits or , that variable is a cyclic coordinate and the corresponding momentum or is an additional first integral; continuous symmetries give the analogous conserved quantities through Noether's theorem.
Put . Since is cyclic,
for a constant . The Beltrami integral is
Therefore
If , then and
For , define
the hinted identity gives . Hence . Because as , the prescribed limit selects the minus sign:
As , the logarithm tends to , so every such solution satisfies .
Multiply the Gegenbauer differential equation by
It becomes the Sturm-Liouville form
For two polynomial solutions of degrees , multiply their equations crosswise, subtract, and integrate over . The boundary term vanishes because and . Since the eigenvalues are distinct,
Thus , , and the displayed function is the weight.
Every interior root is simple: a solution and its derivative cannot both vanish at an ordinary point of a second-order linear differential equation unless the solution is identically zero. Let be all interior roots and put . If , then has degree below , so orthogonality gives
But has no zero and constant sign on , whence the integrand has one strict sign except at finitely many points. Its integral cannot vanish, a contradiction. Therefore : all roots are real, simple, and lie in .
The total angular momentum operator is
Using the cyclic orbital angular momentum commutation relations,
while ; hence . In Cartesian coordinates,
Because and it annihilates and every radial function,
Replacing by gives eigenvalue .
The six-dimensional space splits into the five trace-free quadratic spherical harmonics with and one radial state with . A simultaneous eigenbasis for and is
where the eigenvalue of was used. Every displayed state is a complex linear combination of the original six energy eigenstates, so it is still an energy eigenstate at the same level.
For a moving circuit, Faraday's law states that the electromotive force
equals , where is the magnetic flux through any spanning surface. Choose the upward normal. The horizontal square has
so Ohm's law gives the induced current
with positive current taken counterclockwise from above.
On the edge , . The magnetic force on a current-carrying wire is , hence
The ratio of the horizontal to vertical magnitudes is , so the directed force makes angle with the vertical, with the sign fixing the horizontal side.
The horizontal forces on opposite edges cancel, while all four vertical contributions add:
This is an electromagnetic drag force opposing the motion, as required by Lenz's law. Including gravity gives
At the terminal velocity, , and the constant downward speed has magnitude
The linear least-squares problem is to choose minimizing the Euclidean norm of the residual:
For a QR decomposition , orthogonality of gives . If has full column rank and is in standard form with invertible upper-triangular , the minimizer solves .
Applying the Gram-Schmidt process to the columns of the given matrix yields
Indeed, is an orthogonal matrix and . Moreover,
Back substitution in the leading triangular system gives
so the unique least-squares solution is
The unused final transformed residual component is , so the minimum residual norm is .
Until the walk first reaches , it is the simple random walk on a path of length with reflection at the endpoint . If is the expected hitting time of from the vertex at distance from , then
Solving this second-difference equation gives . Therefore
A complete excursion from along any one arm and back to has mean length . This follows from the same path recurrence, or from Kac's lemma: the graph has edges, the stationary distribution of simple random walk assigns mass to , so its mean return time is .
Let be the mean time to hit starting at . On each visit to , the first step reaches with probability ; with probability it begins a wrong-arm excursion of mean duration , after which the problem restarts. Thus
The walk first takes mean time to reach , so the requested time from is
Let be the vertex steps from toward , and let be the mean time to reach from . Part (b) gives . For , first-step analysis at gives
because a step to must be followed by a fresh passage of mean back to before trying again. Hence
By the Strong Markov property, the expected time from to is the sum of these successive passage times:
Adding the initial mean time from to gives

Articles by others on the same topic (0)

There are currently no matching articles.