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In [[numerical linear algebra]], the [[conjugate gradient method]] is an [[iterative method]] for numerically solving the [[System of linear equations|linear system]]
 
:<math>\boldsymbol{Ax}=\boldsymbol{b}</math>
 
where <math>\boldsymbol{A}</math> is [[Symmetric matrix|symmetric]] [[Positive-definite matrix|positive-definite]]. The conjugate gradient method can be derived from several different perspectives, including specialization of the [[conjugate direction method]] for [[Optimization (mathematics)|optimization]], and variation of the [[Arnoldi iteration|Arnoldi]]/[[Lanczos iteration|Lanczos]] iteration for [[eigenvalue]] problems.
 
The intent of this article is to document the important steps in these derivations.
 
==Derivation from the conjugate direction method==
{{Expand section|date=April 2010}}
The conjugate gradient method can be seen as a special case of the conjugate direction method applied to minimization of the quadratic function
 
:<math>f(\boldsymbol{x})=\boldsymbol{x}^\mathrm{T}\boldsymbol{A}\boldsymbol{x}-2\boldsymbol{b}^\mathrm{T}\boldsymbol{x}\text{.}</math>
 
===The conjugate direction method===
In the conjugate direction method for minimizing
 
:<math>f(\boldsymbol{x})=\boldsymbol{x}^\mathrm{T}\boldsymbol{A}\boldsymbol{x}-2\boldsymbol{b}^\mathrm{T}\boldsymbol{x}\text{.}</math>
 
one starts with an initial guess <math>\boldsymbol{x}_0</math> and the corresponding residual <math>\boldsymbol{r}_0=\boldsymbol{b}-\boldsymbol{Ax}_0</math>, and computes the iterate and residual by the formulae
 
:<math>\begin{align}
\alpha_i&=\frac{\boldsymbol{p}_i^\mathrm{T}\boldsymbol{r}_i}{\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_i}\text{,}\\
\boldsymbol{x}_{i+1}&=\boldsymbol{x}_i+\alpha_i\boldsymbol{p}_i\text{,}\\
\boldsymbol{r}_{i+1}&=\boldsymbol{r}_i-\alpha_i\boldsymbol{Ap}_i
\end{align}</math>
 
where <math>\boldsymbol{p}_0,\boldsymbol{p}_1,\boldsymbol{p}_2,\ldots</math> are a series of mutually conjugate directions, i.e.,
 
:<math>\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_j=0</math>
 
for any <math>i\neq j</math>.
 
The conjugate direction method is imprecise in the sense that no formulae are given for selection of the directions <math>\boldsymbol{p}_0,\boldsymbol{p}_1,\boldsymbol{p}_2,\ldots</math>. Specific choices lead to various methods including the conjugate gradient method and [[Gaussian elimination]].
 
==Derivation from the Arnoldi/Lanczos iteration==
{{see|Arnoldi iteration|Lanczos iteration}}
The conjugate gradient method can also be seen as a variant of the Arnoldi/Lanczos iteration applied to solving linear systems.
 
===The general Arnoldi method===
In the Arnoldi iteration, one starts with a vector <math>\boldsymbol{r}_0</math> and gradually builds an [[orthonormal]] basis <math>\{\boldsymbol{v}_1,\boldsymbol{v}_2,\boldsymbol{v}_3,\ldots\}</math> of the [[Krylov subspace]]
 
:<math>\mathcal{K}(\boldsymbol{A},\boldsymbol{r}_0)=\{\boldsymbol{r}_0,\boldsymbol{Ar}_0,\boldsymbol{A}^2\boldsymbol{r}_0,\ldots\}</math>
 
by defining <math>\boldsymbol{v}_i=\boldsymbol{w}_i/\lVert\boldsymbol{w}_i\rVert_2</math> where
 
:<math>\boldsymbol{w}_i=\begin{cases}
\boldsymbol{r}_0 & \text{if }i=1\text{,}\\
\boldsymbol{Av}_{i-1}-\sum_{j=1}^{i-1}(\boldsymbol{v}_j^\mathrm{T}\boldsymbol{Av}_{i-1})\boldsymbol{v}_j & \text{if }i>1\text{.}
\end{cases}</math>
 
In other words, for <math>i>1</math>, <math>\boldsymbol{v}_i</math> is found by [[Gram-Schmidt orthogonalization|Gram-Schmidt orthogonalizing]] <math>\boldsymbol{Av}_{i-1}</math> against <math>\{\boldsymbol{v}_1,\boldsymbol{v}_2,\ldots,\boldsymbol{v}_{i-1}\}</math> followed by normalization.
 
Put in matrix form, the iteration is captured by the equation
 
:<math>\boldsymbol{AV}_i=\boldsymbol{V}_{i+1}\boldsymbol{\tilde{H}}_i</math>
 
where
 
:<math>\begin{align}
\boldsymbol{V}_i&=\begin{bmatrix}
\boldsymbol{v}_1 & \boldsymbol{v}_2 & \cdots & \boldsymbol{v}_i
\end{bmatrix}\text{,}\\
\boldsymbol{\tilde{H}}_i&=\begin{bmatrix}
h_{11} & h_{12} & h_{13} & \cdots & h_{1,i}\\
h_{21} & h_{22} & h_{23} & \cdots & h_{2,i}\\
& h_{32} & h_{33} & \cdots & h_{3,i}\\
& & \ddots & \ddots & \vdots\\
& & & h_{i,i-1} & h_{i,i}\\
& & & & h_{i+1,i}
\end{bmatrix}=\begin{bmatrix}
\boldsymbol{H}_i\\
h_{i+1,i}\boldsymbol{e}_i^\mathrm{T}
\end{bmatrix}
\end{align}</math>
 
with
 
:<math>h_{ji}=\begin{cases}
\boldsymbol{v}_j^\mathrm{T}\boldsymbol{Av}_i & \text{if }j\leq i\text{,}\\
\lVert\boldsymbol{w}_{i+1}\rVert_2 & \text{if }j=i+1\text{,}\\
0 & \text{if }j>i+1\text{.}
\end{cases}</math>
 
When applying the Arnoldi iteration to solving linear systems, one starts with <math>\boldsymbol{r}_0=\boldsymbol{b}-\boldsymbol{Ax}_0</math>, the residual corresponding to an initial guess <math>\boldsymbol{x}_0</math>. After each step of iteration, one computes <math>\boldsymbol{y}_i=\boldsymbol{H}_i^{-1}(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)</math> and the new iterate <math>\boldsymbol{x}_i=\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{y}_i</math>.
 
===The direct Lanczos method===
For the rest of discussion, we assume that <math>\boldsymbol{A}</math> is symmetric positive-definite. With symmetry of <math>\boldsymbol{A}</math>, the [[upper Hessenberg matrix]] <math>\boldsymbol{H}_i=\boldsymbol{V}_i^\mathrm{T}\boldsymbol{AV}_i</math> becomes symmetric and thus tridiagonal. It then can be more clearly denoted by
 
:<math>\boldsymbol{H}_i=\begin{bmatrix}
a_1 & b_2\\
b_2 & a_2 & b_3\\
& \ddots & \ddots & \ddots\\
& & b_{i-1} & a_{i-1} & b_i\\
& & & b_i & a_i
\end{bmatrix}\text{.}</math>
 
This enables a short three-term recurrence for <math>\boldsymbol{v}_i</math> in the iteration, and the Arnoldi iteration is reduced to the Lanczos iteration.
 
Since <math>\boldsymbol{A}</math> is symmetric positive-definite, so is <math>\boldsymbol{H}_i</math>. Hence, <math>\boldsymbol{H}_i</math> can be [[LU factorization|LU factorized]] without [[partial pivoting]] into
 
:<math>\boldsymbol{H}_i=\boldsymbol{L}_i\boldsymbol{U}_i=\begin{bmatrix}
1\\
c_2 & 1\\
& \ddots & \ddots\\
& & c_{i-1} & 1\\
& & & c_i & 1
\end{bmatrix}\begin{bmatrix}
d_1 & b_2\\
& d_2 & b_3\\
& & \ddots & \ddots\\
& & & d_{i-1} & b_i\\
& & & & d_i
\end{bmatrix}</math>
 
with convenient recurrences for <math>c_i</math> and <math>d_i</math>:
 
:<math>\begin{align}
c_i&=b_i/d_{i-1}\text{,}\\
d_i&=\begin{cases}
a_1 & \text{if }i=1\text{,}\\
a_i-c_ib_i & \text{if }i>1\text{.}
\end{cases}
\end{align}</math>
 
Rewrite <math>\boldsymbol{x}_i=\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{y}_i</math> as
 
:<math>\begin{align}
\boldsymbol{x}_i&=\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{H}_i^{-1}(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)\\
&=\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{U}_i^{-1}\boldsymbol{L}_i^{-1}(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)\\
&=\boldsymbol{x}_0+\boldsymbol{P}_i\boldsymbol{z}_i
\end{align}</math>
 
with
 
:<math>\begin{align}
\boldsymbol{P}_i&=\boldsymbol{V}_{i}\boldsymbol{U}_i^{-1}\text{,}\\
\boldsymbol{z}_i&=\boldsymbol{L}_i^{-1}(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)\text{.}
\end{align}</math>
 
It is now important to observe that
 
:<math>\begin{align}
\boldsymbol{P}_i&=\begin{bmatrix}
\boldsymbol{P}_{i-1} & \boldsymbol{p}_i
\end{bmatrix}\text{,}\\
\boldsymbol{z}_i&=\begin{bmatrix}
\boldsymbol{z}_{i-1}\\
\zeta_i
\end{bmatrix}\text{.}
\end{align}</math>
 
In fact, there are short recurrences for <math>\boldsymbol{p}_i</math> and <math>\zeta_i</math> as well:
 
:<math>\begin{align}
\boldsymbol{p}_i&=\frac{1}{d_i}(\boldsymbol{v}_i-b_i\boldsymbol{p}_{i-1})\text{,}\\
\zeta_i&=-c_i\zeta_{i-1}\text{.}
\end{align}</math>
 
With this formulation, we arrive at a simple recurrence for <math>\boldsymbol{x}_i</math>:
 
:<math>\begin{align}
\boldsymbol{x}_i&=\boldsymbol{x}_0+\boldsymbol{P}_i\boldsymbol{z}_i\\
&=\boldsymbol{x}_0+\boldsymbol{P}_{i-1}\boldsymbol{z}_{i-1}+\zeta_i\boldsymbol{p}_i\\
&=\boldsymbol{x}_{i-1}+\zeta_i\boldsymbol{p}_i\text{.}
\end{align}</math>
 
The relations above straightforwardly lead to the direct Lanczos method, which turns out to be slightly more complex.
 
===The conjugate gradient method from imposing orthogonality and conjugacy===
If we allow <math>\boldsymbol{p}_i</math> to scale and compensate for the scaling in the constant factor, we potentially can have simpler recurrences of the form:
 
:<math>\begin{align}
\boldsymbol{x}_i&=\boldsymbol{x}_{i-1}+\alpha_{i-1}\boldsymbol{p}_{i-1}\text{,}\\
\boldsymbol{r}_i&=\boldsymbol{r}_{i-1}-\alpha_{i-1}\boldsymbol{Ap}_{i-1}\text{,}\\
\boldsymbol{p}_i&=\boldsymbol{r}_i+\beta_{i-1}\boldsymbol{p}_{i-1}\text{.}
\end{align}</math>
 
As premises for the simplification, we now derive the orthogonality of <math>\boldsymbol{r}_i</math> and conjugacy of <math>\boldsymbol{p}_i</math>, i.e., for <math>i\neq j</math>,
 
:<math>\begin{align}
\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_j&=0\text{,}\\
\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_j&=0\text{.}
\end{align}</math>
 
The residuals are mutually orthogonal because <math>\boldsymbol{r}_i</math> is essentially a multiple of <math>\boldsymbol{v}_{i+1}</math> since for <math>i=0</math>, <math>\boldsymbol{r}_0=\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{v}_1</math>, for <math>i>0</math>,
 
:<math>\begin{align}
\boldsymbol{r}_i&=\boldsymbol{b}-\boldsymbol{Ax}_i\\
&=\boldsymbol{b}-\boldsymbol{A}(\boldsymbol{x}_0+\boldsymbol{V}_i\boldsymbol{y}_i)\\
&=\boldsymbol{r}_0-\boldsymbol{AV}_i\boldsymbol{y}_i\\
&=\boldsymbol{r}_0-\boldsymbol{V}_{i+1}\boldsymbol{\tilde{H}}_i\boldsymbol{y}_i\\
&=\boldsymbol{r}_0-\boldsymbol{V}_i\boldsymbol{H}_i\boldsymbol{y}_i-h_{i+1,i}(\boldsymbol{e}_i^\mathrm{T}\boldsymbol{y}_i)\boldsymbol{v}_{i+1}\\
&=\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{v}_1-\boldsymbol{V}_i(\lVert\boldsymbol{r}_0\rVert_2\boldsymbol{e}_1)-h_{i+1,i}(\boldsymbol{e}_i^\mathrm{T}\boldsymbol{y}_i)\boldsymbol{v}_{i+1}\\
&=-h_{i+1,i}(\boldsymbol{e}_i^\mathrm{T}\boldsymbol{y}_i)\boldsymbol{v}_{i+1}\text{.}\end{align}</math>
 
To see the conjugacy of <math>\boldsymbol{p}_i</math>, it suffices to show that <math>\boldsymbol{P}_i^\mathrm{T}\boldsymbol{AP}_i</math> is diagonal:
 
:<math>\begin{align}
\boldsymbol{P}_i^\mathrm{T}\boldsymbol{AP}_i&=\boldsymbol{U}_i^{-\mathrm{T}}\boldsymbol{V}_i^\mathrm{T}\boldsymbol{AV}_i\boldsymbol{U}_i^{-1}\\
&=\boldsymbol{U}_i^{-\mathrm{T}}\boldsymbol{H}_i\boldsymbol{U}_i^{-1}\\
&=\boldsymbol{U}_i^{-\mathrm{T}}\boldsymbol{L}_i\boldsymbol{U}_i\boldsymbol{U}_i^{-1}\\
&=\boldsymbol{U}_i^{-\mathrm{T}}\boldsymbol{L}_i
\end{align}</math>
 
is symmetric and lower triangular simultaneously and thus must be diagonal.
 
Now we can derive the constant factors <math>\alpha_i</math> and <math>\beta_i</math> with respect to the scaled <math>\boldsymbol{p}_i</math> by solely imposing the orthogonality of <math>\boldsymbol{r}_i</math> and conjugacy of <math>\boldsymbol{p}_i</math>.
 
Due to the orthogonality of <math>\boldsymbol{r}_i</math>, it is necessary that <math>\boldsymbol{r}_{i+1}^\mathrm{T}\boldsymbol{r}_i=(\boldsymbol{r}_i-\alpha_i\boldsymbol{Ap}_i)^\mathrm{T}\boldsymbol{r}_i=0</math>. As a result,
 
:<math>\begin{align}
\alpha_i&=\frac{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_i}{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{Ap}_i}\\
&=\frac{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_i}{(\boldsymbol{p}_i-\beta_{i-1}\boldsymbol{p}_{i-1})^\mathrm{T}\boldsymbol{Ap}_i}\\
&=\frac{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_i}{\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_i}\text{.}
\end{align}</math>
 
Similarly, due to the conjugacy of <math>\boldsymbol{p}_i</math>, it is necessary that <math>\boldsymbol{p}_{i+1}^\mathrm{T}\boldsymbol{Ap}_i=(\boldsymbol{r}_{i+1}+\beta_i\boldsymbol{p}_i)^\mathrm{T}\boldsymbol{Ap}_i=0</math>. As a result,
 
:<math>\begin{align}
\beta_i&=-\frac{\boldsymbol{r}_{i+1}^\mathrm{T}\boldsymbol{Ap}_i}{\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_i}\\
&=-\frac{\boldsymbol{r}_{i+1}^\mathrm{T}(\boldsymbol{r}_i-\boldsymbol{r}_{i+1})}{\alpha_i\boldsymbol{p}_i^\mathrm{T}\boldsymbol{Ap}_i}\\
&=\frac{\boldsymbol{r}_{i+1}^\mathrm{T}\boldsymbol{r}_{i+1}}{\boldsymbol{r}_i^\mathrm{T}\boldsymbol{r}_i}\text{.}
\end{align}</math>
 
This completes the derivation.
 
==References==
#{{cite journal|last1 = Hestenes|first1 = M. R.|authorlink1 = David Hestenes|last2 = Stiefel|first2 = E.|authorlink2 = Eduard Stiefel|title = Methods of conjugate gradients for solving linear systems|journal = Journal of Research of the National Bureau of Standards|volume = 49|issue = 6|date=December 1952|url = http://nvl.nist.gov/pub/nistpubs/jres/049/6/V49.N06.A08.pdf|format=PDF}}
#{{cite book|last = Saad|first = Y.|title = Iterative methods for sparse linear systems|edition = 2nd|chapter = Chapter 6: Krylov Subspace Methods, Part I|publisher = SIAM|year = 2003|isbn = 978-0-89871-534-7}}
 
{{Numerical linear algebra}}
 
[[Category:Numerical linear algebra]]
[[Category:Optimization algorithms and methods]]
[[Category:Gradient methods]]
[[Category:Articles containing proofs]]

Revision as of 17:33, 15 July 2013

In numerical linear algebra, the conjugate gradient method is an iterative method for numerically solving the linear system

Ax=b

where A is symmetric positive-definite. The conjugate gradient method can be derived from several different perspectives, including specialization of the conjugate direction method for optimization, and variation of the Arnoldi/Lanczos iteration for eigenvalue problems.

The intent of this article is to document the important steps in these derivations.

Derivation from the conjugate direction method

Template:Expand section The conjugate gradient method can be seen as a special case of the conjugate direction method applied to minimization of the quadratic function

f(x)=xTAx2bTx.

The conjugate direction method

In the conjugate direction method for minimizing

f(x)=xTAx2bTx.

one starts with an initial guess x0 and the corresponding residual r0=bAx0, and computes the iterate and residual by the formulae

αi=piTripiTApi,xi+1=xi+αipi,ri+1=riαiApi

where p0,p1,p2, are a series of mutually conjugate directions, i.e.,

piTApj=0

for any ij.

The conjugate direction method is imprecise in the sense that no formulae are given for selection of the directions p0,p1,p2,. Specific choices lead to various methods including the conjugate gradient method and Gaussian elimination.

Derivation from the Arnoldi/Lanczos iteration

Template:See The conjugate gradient method can also be seen as a variant of the Arnoldi/Lanczos iteration applied to solving linear systems.

The general Arnoldi method

In the Arnoldi iteration, one starts with a vector r0 and gradually builds an orthonormal basis {v1,v2,v3,} of the Krylov subspace

𝒦(A,r0)={r0,Ar0,A2r0,}

by defining vi=wi/wi2 where

wi={r0if i=1,Avi1j=1i1(vjTAvi1)vjif i>1.

In other words, for i>1, vi is found by Gram-Schmidt orthogonalizing Avi1 against {v1,v2,,vi1} followed by normalization.

Put in matrix form, the iteration is captured by the equation

AVi=Vi+1H~i

where

Vi=[v1v2vi],H~i=[h11h12h13h1,ih21h22h23h2,ih32h33h3,ihi,i1hi,ihi+1,i]=[Hihi+1,ieiT]

with

hji={vjTAviif ji,wi+12if j=i+1,0if j>i+1.

When applying the Arnoldi iteration to solving linear systems, one starts with r0=bAx0, the residual corresponding to an initial guess x0. After each step of iteration, one computes yi=Hi1(r02e1) and the new iterate xi=x0+Viyi.

The direct Lanczos method

For the rest of discussion, we assume that A is symmetric positive-definite. With symmetry of A, the upper Hessenberg matrix Hi=ViTAVi becomes symmetric and thus tridiagonal. It then can be more clearly denoted by

Hi=[a1b2b2a2b3bi1ai1bibiai].

This enables a short three-term recurrence for vi in the iteration, and the Arnoldi iteration is reduced to the Lanczos iteration.

Since A is symmetric positive-definite, so is Hi. Hence, Hi can be LU factorized without partial pivoting into

Hi=LiUi=[1c21ci11ci1][d1b2d2b3di1bidi]

with convenient recurrences for ci and di:

ci=bi/di1,di={a1if i=1,aicibiif i>1.

Rewrite xi=x0+Viyi as

xi=x0+ViHi1(r02e1)=x0+ViUi1Li1(r02e1)=x0+Pizi

with

Pi=ViUi1,zi=Li1(r02e1).

It is now important to observe that

Pi=[Pi1pi],zi=[zi1ζi].

In fact, there are short recurrences for pi and ζi as well:

pi=1di(vibipi1),ζi=ciζi1.

With this formulation, we arrive at a simple recurrence for xi:

xi=x0+Pizi=x0+Pi1zi1+ζipi=xi1+ζipi.

The relations above straightforwardly lead to the direct Lanczos method, which turns out to be slightly more complex.

The conjugate gradient method from imposing orthogonality and conjugacy

If we allow pi to scale and compensate for the scaling in the constant factor, we potentially can have simpler recurrences of the form:

xi=xi1+αi1pi1,ri=ri1αi1Api1,pi=ri+βi1pi1.

As premises for the simplification, we now derive the orthogonality of ri and conjugacy of pi, i.e., for ij,

riTrj=0,piTApj=0.

The residuals are mutually orthogonal because ri is essentially a multiple of vi+1 since for i=0, r0=r02v1, for i>0,

ri=bAxi=bA(x0+Viyi)=r0AViyi=r0Vi+1H~iyi=r0ViHiyihi+1,i(eiTyi)vi+1=r02v1Vi(r02e1)hi+1,i(eiTyi)vi+1=hi+1,i(eiTyi)vi+1.

To see the conjugacy of pi, it suffices to show that PiTAPi is diagonal:

PiTAPi=UiTViTAViUi1=UiTHiUi1=UiTLiUiUi1=UiTLi

is symmetric and lower triangular simultaneously and thus must be diagonal.

Now we can derive the constant factors αi and βi with respect to the scaled pi by solely imposing the orthogonality of ri and conjugacy of pi.

Due to the orthogonality of ri, it is necessary that ri+1Tri=(riαiApi)Tri=0. As a result,

αi=riTririTApi=riTri(piβi1pi1)TApi=riTripiTApi.

Similarly, due to the conjugacy of pi, it is necessary that pi+1TApi=(ri+1+βipi)TApi=0. As a result,

βi=ri+1TApipiTApi=ri+1T(riri+1)αipiTApi=ri+1Tri+1riTri.

This completes the derivation.

References

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    Extending the tax exemption would help. The exemption, which may be as a lot as $2 million per family, covers individuals who negotiate a principal reduction on their existing mortgage, sell their house short (i.e., for lower than the excellent loans), or take part in a foreclosure course of. An extension of theexemption would seem like a common-sense means to assist stabilize the housing market, but the political turmoil around the fiscal-cliff negotiations means widespread sense could not win out. Home Minority Chief Nancy Pelosi (D-Calif.) believes that the mortgage relief provision will be on the table during the grand-cut price talks, in response to communications director Nadeam Elshami. Buying or promoting of blue mild bulbs is unlawful.

    A vendor's stamp duty has been launched on industrial property for the primary time, at rates ranging from 5 per cent to 15 per cent. The Authorities might be trying to reassure the market that they aren't in opposition to foreigners and PRs investing in Singapore's property market. They imposed these measures because of extenuating components available in the market." The sale of new dual-key EC models will even be restricted to multi-generational households only. The models have two separate entrances, permitting grandparents, for example, to dwell separately. The vendor's stamp obligation takes effect right this moment and applies to industrial property and plots which might be offered inside three years of the date of buy. JLL named Best Performing Property Brand for second year running

    The data offered is for normal info purposes only and isn't supposed to be personalised investment or monetary advice. Motley Fool Singapore contributor Stanley Lim would not personal shares in any corporations talked about. Singapore private home costs increased by 1.eight% within the fourth quarter of 2012, up from 0.6% within the earlier quarter. Resale prices of government-built HDB residences which are usually bought by Singaporeans, elevated by 2.5%, quarter on quarter, the quickest acquire in five quarters. And industrial property, prices are actually double the levels of three years ago. No withholding tax in the event you sell your property. All your local information regarding vital HDB policies, condominium launches, land growth, commercial property and more

    There are various methods to go about discovering the precise property. Some local newspapers (together with the Straits Instances ) have categorised property sections and many local property brokers have websites. Now there are some specifics to consider when buying a 'new launch' rental. Intended use of the unit Every sale begins with 10 p.c low cost for finish of season sale; changes to 20 % discount storewide; follows by additional reduction of fiftyand ends with last discount of 70 % or extra. Typically there is even a warehouse sale or transferring out sale with huge mark-down of costs for stock clearance. Deborah Regulation from Expat Realtor shares her property market update, plus prime rental residences and houses at the moment available to lease Esparina EC @ Sengkang
  2. 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

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