Stephen Boyd & Lieven Vandenberghe的《凸优化》英文原版解析

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"这是一本由Stephen Boyd和Lieven Vandenberghe合著的英文原版《Convex Optimization》书籍，详细介绍了凸优化领域的理论与实践。书中涵盖了从绪论到共10章的内容，包括凸集和凸函数的基础概念，以及有约束和无约束的凸优化问题。每章都配有相应的习题，旨在帮助读者深入理解和应用所学知识。" 《Convex Optimization》是凸优化领域的经典著作，作者Stephen Boyd是斯坦福大学电气工程系的教授，而Lieven Vandenberghe则来自加州大学洛杉矶分校的电气工程系。这本书是了解和掌握凸优化技术的重要参考资料。凸优化是优化理论的一个分支，它主要研究在数学模型中寻找全局最优解的问题。凸优化的显著特点是，由于其目标函数和约束条件都是凸的，因此可以确保找到的是全局最优解，而不是局部最优解。这对于许多实际问题，如工程设计、经济学、机器学习等领域具有广泛的应用。本书首先介绍了凸优化的基本概念，包括凸集的定义（如线性空间、半空间、多面体、锥等）和凸函数的特性（如次可加性、 Jensen不等式等）。然后，书中的章节逐步深入，讲解了无约束优化问题，如梯度下降法、拟牛顿法等，以及有约束优化问题的解决方法，如拉格朗日乘子法、Karush-Kuhn-Tucker (KKT) 条件等。在约束优化部分，作者不仅讨论了线性规划、二次规划等基本问题，还涉及了更复杂的凸优化问题，如锥优化和广义线性规划。此外，书中还介绍了用于求解凸优化问题的各种算法，如内点法和快速梯度方法，并探讨了这些算法的收敛性和计算效率。为了帮助读者巩固理解，每章末尾都提供了大量习题，这些习题涵盖了各种类型和难度，从基础理论到实际应用，有助于读者将理论知识转化为实际技能。《Convex Optimization》是一本全面而深入的凸优化教材，适合对优化理论感兴趣的学者、学生以及从事相关领域工作的专业人士阅读。通过本书的学习，读者可以系统地掌握凸优化理论，提高解决实际问题的能力。

2 1 Introduction

for all x, y ∈ R

and all α, β ∈ R with α + β = 1, α ≥ 0, β ≥ 0. Comparing (1.3)

and (1.2), we see that convexity is more general than linearity: inequality re pl aces

the more res tr i cti ve equality, and the inequality must hold only for certain values

of α and β. Since any linear program is therefore a convex optimization problem,

we can consider convex optimization to be a generalization of linear programming.

1.1.1 Applications

The optimization problem (1.1) is an abstraction of the problem of making the best

possible choice of a vector in R

from a set of candidate choices. The variable x

represents the choice made; the constraints f

(x) ≤ b

represent ﬁrm requir ements

or speciﬁcations that limit the possible choices, and the objective value f

(x) rep-

resents the cost of choosing x. (We can also think of −f

(x) as representing the

value, or utility, of choosing x.) A solution of the optimization problem (1.1) corre-

sponds to a choice that has minimum cost (or maximum utility), among all choices

that meet the ﬁrm requirements.

In portfolio optimization, for example, we seek the best way to invest some

capital in a set of n assets. The variable x

represents the investment in the ith

asset, so the vector x ∈ R

describes the overall portfolio allocati on across the set of

assets. The constraints might represent a limit on the budget (i.e., a limit on the

total amount to be invested), th e requirement that investments are nonnegative

(assuming short positions are not allowed), and a minimum acceptable value of

expected return for the whole portfolio. The objective or cost function might be

a measure of the overall risk or variance of the portfolio retu r n. In this case,

the optimization problem (1.1) corresponds to choosing a portfolio allocation that

minimizes ri sk , among all possible allocations that meet the ﬁrm requirements.

Another example is device sizing in electronic design, which is the task of choos-

ing the width and length of each device in an electronic circuit. Here the variables

represent the widths and lengths of the devices. The constraints represent a va-

riety of engineering requirements, such as limits on the device sizes imposed by

the manufacturing process, timing requirements that ensure that the circuit can

operate reliably at a speciﬁed speed, and a limit on the total area of the circuit. A

common objective in a device sizing problem is the total power consumed by the

circuit. The optimization problem (1.1) is to ﬁnd the device sizes that satisfy the

design requirements (on manufacturability, timing, and area) and are most power

eﬃcient.

In data ﬁtting, the task is to ﬁnd a model, from a family of potential models,

that best ﬁts some observed data and prior information. Here the variables are the

parameters in the model, and the cons tr aints can represent prior information or

required l imit s on the parameters (such as nonnegativity). The objective function

might be a measure of misﬁt or prediction error between the observed data and

the values predicted by t he model, or a statistical measure of the u nli keliness or

implausibility of the parameter values. The optimization probl em (1.1) is to ﬁnd

the model parameter values that are consistent with the prior information, and give

the smallest misﬁt or prediction error with the observed data (or, in a statistical

1.1 Mathematical optimization 3

framework, are most likely).

An amazing variety of practical problems involving decision making (or system

design, analysis, and operation) can be cast in the form of a mathematical opti-

mization problem, or some variation such as a multicriterion optimization problem.

Indeed, mathematical optimization has become an important tool in many areas.

It is widel y used in engineering, in electronic design automation, automatic con-

trol systems, and optimal design problems arising in civil, chemical, mechanical,

and aerospace engineering. Optimization is used for problems arising in network

design and operation, ﬁnance, supply chain management, scheduling, and many

other areas. The list of applications is still steadily expanding.

For most of these applications, mathematical optimization is used as an aid to

a human deci si on maker, system designer, or system operator, who supervises the

process, checks the results, and modiﬁ es the problem (or the solution approach)

when necessary. This human decision maker also carries out any actions suggested

by the optimization problem, e.g., buying or selling asset s to achieve the opt imal

portfolio.

A r el atively recent phenomenon opens the possibility of many other applications

for mathematical optimization. With the proliferation of computers embedded in

products, we have seen a rapid growth in embedded optimization. In these em-

bedded applications, optimization is used to automatically make real-time choices,

and even carry out the associated actions, with no (or little) human intervention or

oversight. In some application areas, this blending of tradition al automatic control

systems and embedded optimization is well under way; in others, it is just start-

ing. Embedded real-time optimization raises some new challenges: in particular,

it requires solution methods that are extremely reliable, and solve problems in a

predictable amount of time (and memory).

1.1.2 Solving optimization problems

A solution method for a class of optimization problems is an algor ith m that com-

putes a solution of the problem (to some given accuracy), given a particular problem

from the class, i.e., an instance of the problem. Since the late 1940s, a large eﬀort

has gone into developing algorithms for solving various classes of optimization prob-

lems, analyzing their properties, and developing good software implementations.

The eﬀectiveness of these algorithms, i.e., our ability to solve the optimization prob-

lem (1.1), varies considerably, and depends on factors such as the particular forms

of the objective and constraint functions, how many variables and constraints there

are, and special structure, such as sparsity. (A problem is sparse if each constr aint

function depends on only a small number of the variables).

Even when the objective and constraint functions are smooth (for example,

polynomials) the general optimization problem (1.1) is surprisingly diﬃcult to solve.

Approaches to the general problem therefore involve some kind of compromise, such

as very long computation time, or the possibility of not ﬁnding the solution. Some

of these methods are discussed in §1.4.

There are, however, some important exceptions to the general rule that most

optimization problems are diﬃcul t to solve. For a few problem classes we have

4 1 Introduction

eﬀective algorithms that can reliably solve even large problems, with hundreds or

thousands of variables and constraints. Two imp or tant and well known examples,

described in §1.2 below (and in detail in chapter 4), are least-squares problems and

linear programs. It i s less well known that convex optimization is another exception

to the rule: Like least-squares or linear programming, there are very eﬀe ct ive

algorithms that can reliably and eﬃciently solve even large convex problems.

1.2 Least-squares and linear programming

In this section we describe two very widely known and used special subclasses of

convex optimization: least-squares and linear programming. (A c omple te technical

treatment of these problems will be given in chapter 4.)

1.2.1 Least-squares problems

A least-squares problem is an optimi zati on problem with no constraints (i.e., m =

0) and an objective which is a sum of s qu ar es of terms of the form a

x − b

minimize f

(x) = kAx − bk

i=1

x − b

)

(1.4)

Here A ∈ R

k×n

(with k ≥ n), a

are the rows of A, and the vector x ∈ R

is the

optimization variable.

Solving l eas t-s quares problems

The solution of a least-squares problem (1.4) can be reduce d to solving a set of

linear equations ,

A)x = A

so we have the analytical solution x = (A

−1

b. For least-squares problems

we have good algorithms (and software implementations) for solving the problem to

high accuracy, with very high reliability. The least-squares problem can be solved

in a time approximately proportional to n

k, with a known constant. A current

desktop computer can solve a least-squares problem with hundreds of variables, and

thousands of terms, in a few seconds; more powerful computers, of course, can solve

larger problems, or the same size problems, faster. (Moreover, these solution times

will decrease exponentially in the future, according to Moore’s law.) Algorithms

and software for solving least-squares problems are reliable enough for embedded

optimization.

In many cases we can solve even larger least-squares problems, by exploiting

some special structure in the coeﬃcient matrix A. Suppose, for example, that the

matrix A is sparse, which means that it has far fewer than kn nonzero entries. By

exploiting sparsity, we can usually solve the least-squares problem much faster than

order n

k. A current desktop computer can solve a sparse least-squares problem

1.2 Least-squares and li near programming 5

with tens of thousands of variables, and hundreds of thousands of terms, in around

a minute (although this depends on the particular sparsity pattern).

For extr eme ly large problems (say, with millions of variables), or for problems

with exacting real-time c omput in g requirements, solving a least-square s problem

can be a challenge. But in the vast majority of cases, we can say th at existing

methods are very eﬀective, and extremely reliable. Ind ee d, we can say that solving

least-squares problems (that are not on the boundary of what is currently achiev-

able) is a (mature) technology, that can be reliably used by many people who do

not know, and do not need to know, the details.

Using least-squares

The least-squ are s problem is the basis for regression analysis, optimal control, and

many parameter estimation and data ﬁtting methods. It has a number of statistical

interpretations, e.g., as maximum likelihood estimation of a vector x, given linear

measurements corr u pt ed by Gaussian measurement errors.

Recognizing an optimization problem as a least-s q uar es problem is straightfor-

ward; we only need to verify that the objective is a quadratic function (and then

test whether th e associated quadratic form is positive semideﬁnite). While the

basic l east -s q uar es problem has a simple ﬁxed form, several standard techniques

are used to increase its ﬂexibility in applications.

In weighted least-squares, the weighted least-squares cost

i=1

x − b

)

where w

, . . . , w

are positive, is minimized. (This problem is readily cast and

solved as a standard least-squares problem.) Here the weights w

are chosen to

reﬂect diﬀering levels of concern about the sizes of the t er ms a

x − b

, or simply

to inﬂuence the solution. In a statistical setting, weighted least-squares arises

in estimation of a vector x, given linear measurements corru pt ed by errors with

unequal variances.

Another technique in least-squares is regularization, in which extra terms are

added to the cost function. In the simpl es t case, a positive multiple of the sum of

squares of the variables is added to the cost function:

i=1

x − b

)

+ ρ

i=1

where ρ > 0. (This pr obl em too can be formulated as a standard least-squares

problem.) The extra terms penalize large values of x, and result in a sensible

solution in cases when minimizing the ﬁrst sum only does not. The parameter ρ is

chosen by the user to give the right trade-oﬀ between making the original objective

function

i=1

x−b

)

small, while keeping

i=1

not too big. Regularization

comes up in statistical estimation when the vector x to be e st imate d is given a prior

distribution.

Weighted least-squares and regul ari zati on are covered in chapter 6; their sta-

tistical interpretations are given in chapter 7.

6 1 Introduction

1.2.2 Linear programming

Another important class of optimization problems is linear programming, in which

the objective and all const r aint functions are linear:

minimize c

subject to a

x ≤ b

, i = 1, . . . , m.

(1.5)

Here the vectors c, a

, . . . , a

∈ R

and scalars b

, . . . , b

∈ R are problem pa-

rameters that specify the object ive and constraint functions.

Solving l ine ar programs

There is no simple analytical formula for the solution of a linear program (as there

is for a least-squares problem), but there are a variety of very eﬀective methods for

solving them, including Dantzig’s simplex method, and the more recent interior-

point metho d s descr ibed later in this book. While we cann ot give the exact number

of arithmetic operations required to solve a linear program (as we can for least-

squares), we c an establish rigorous bounds on the number of operations required

to solve a linear program, to a given accuracy, using an interior-point method. The

complexity in practice is order n

m (assuming m ≥ n) but with a constant that is

less well characterized than for least-sq uar es . These algorithms are quite r e li able,

although perhaps not quite as re liab le as methods for least-squares. We can easily

solve problems with hundreds of variables and thousands of constraints on a small

desktop computer, in a matter of seconds. If the problem is sparse, or has some

other exploitable structure, we can often solve problems with tens or hundreds of

thousands of variables and constraints.

As with least-squares problems, it is still a challenge to solve extremely large

linear pr ograms, or to solve linear programs with ex actin g real-time computing re-

quirements. But, like least-squares, we can say that solv in g (most) linear programs

is a mature technology. Linear programming solvers can be (and are) embedded in

many tools and applications.

Using linear programming

Some applications lead directly to linear programs in the form (1.5), or one of

several other standard forms. In many other cases the original optimization prob-

lem does not have a standard linear program form, but can be tr ans for med to an

equivalent linear program (and then, of course, solved) using techniques covered in

detail in chapter 4.

As a simple example, consider the Chebyshev approximation problem:

minimize max

i=1,...,k

x − b

(1.6)

Here x ∈ R

is the variable, and a

, . . . , a

∈ R

, b

, . . . , b

∈ R are parameters

that specify t he problem instance. Note the resemblance to the least-squares prob-

lem (1.4). For both problems, the objective is a measure of the size of the terms

x − b

. In least-squares, we use the sum of squares of the terms as objective,

whereas in Chebyshev approximation, we u s e the maximum of the absolute values.

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