This operation preserves convexity. Some problems are “NP-hard”, which roughly means that they cannot be solved in reasonable time on a computer. In the case of strict convexity, the necessary condition is generally not valid. Necessary cookies are absolutely essential for the website to function properly. This so-called feasibility problem can be formulated in the standard form, using a zero (or constant) objective. The complexity usually refers to a specific method to solve the problem. By convention, the quantity is set to if the problem is not feasible. ) where is the sum of the original objective and the indicator function of the feasible set : In the next section, we will refine this statement. In the early days of optimization, it was thought that linearity was what distinguished a hard problem from an easy one. We list some properties of convex functions assuming that all functions are defined and continuous on the interval \(\left[ {a,b} \right].\). Similarly, if a function is convex upward (Figure \(2\)), the midpoint \(B\) of each chord \({A_1}{A_2}\) is located below the corresponding point \({A_0}\) of the graph of the function or coincides with this point. Then, there exist z1,z2 ∈ C such that f(x1,z1) ≤ g(x1) + and f(x2,z2) ≤ g(x2) + . Sometimes it is convenient to work with the equivalent epigraph form: Convex optimization has applications in a wide range of disciplines, such as automatic control systems, estimation and signal processing, communications and networks, electronic circuit design, data analysis and modeling, finan f : Rn→ R is convex if and only if the function g : R → R, g(t) = f(x+tv), domg = {t | x+tv ∈ domf} is convex (in t) for any x ∈ domf, v ∈ Rn. (. For a given function [math]f[/math], the loss function is simply something that you as modeler decide on. Another example is when the optimal value is only reached in the limit; think for example of the case when , , and there are no constraints. ) To de ne a new atom that can be used to solve problems in Convex, it su ces to de ne these ve methods. Any function can be given a non-convex loss function. For example, the function \(f\left( x \right) = {x^4}\) is strictly convex downward. Step 1 − Maximize 5 x + 3 y subject to. An example of a problem that is unbounded below is an unconstrained problem with , with domain . (b) Show that the intersection of Cand the hyperplane de ned by gTx+h= 0 (where g6= 0) is convex if A+ ggT 0 for some 2 R. Sometimes, the model is described in terms of the feasible set, as follows: The following example shows that introducing equality constraint may allow to exploit sparsity patterns inherent to the problem. Here is a list of transformations that do preserve convexity. We can eliminate the equality constraint , by writing them as , with a particular solution to the equality constraint, and the columns of span the nullspace of . This problem arises in many situations, for example in statistical estimation problems such as linear regression. ; The problem A convex function can be described as a smooth surface with a single global minimum. Sometimes it is useful to introduce slack variables. Note that, in the convex optimization model, we do not tolerate equality constraints unless they are affine. For example, if is sparse, the original problem has a sparse structure that may be exploited by some algorithms. Equivalently, a function is convex if its epigraph is a convex set. A less radical approach involves the convex problem wih one inequality constraint In this context, the function is called cost function, or objective function, or energy.. {cal X} = left{ x in mathbf{R}^n : f_i(x) le 0, ;; i=1,ldots, m right} }},}\], where \({x_0} – h \lt {\xi _1} \lt {x_0},\) \({x_0} \lt {\xi _2} \lt {x_0} + h.\), \[{f\left( {{x_1}} \right) + f\left( {{x_2}} \right) }= {2f\left( {{x_0}} \right) + \frac{{{h^2}}}{2}\left[ {f^{\prime\prime}\left( {{\xi _1}} \right) + f^{\prime\prime}\left( {{\xi _2}} \right)} \right].}\]. We may ‘‘eliminate’’ some variables of the problem and reduce it to one with fewer variables. De nition 1. Accordingly, the function \(f\left( x \right)\) is convex upward (or concave downward) on the interval \(\left[ {a,b} \right]\) if and only if its graph does not lie above the tangent drawn to it at any point \({x_0}\) of the segment \(\left[ {a,b} \right]\) (Figure \(4\)). For example, the problem This problem has the implicit constraint that should belong to the interior of the polyhedron . A feasible point is a globally optimal (optimal for short) if . Many classes of convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard. From the reasoning above, we infer that is positive semi-definite, since the objective function of the reduced problem is convex. Convex Optimization Problems Properties Feasible set of a convex optimization problem is convex Minimize a convex function over a convex set -suboptimal set is convex The optimal set is convex If the objective is strictly convex, then the optimal set contains at most one point )dom … }\], We see that \(f^{\prime\prime}\left( x \right) \lt 0\) at \(x \lt 0.\) Hence, the function is convex upward on \(\left( { -\infty, 0} \right).\). Then g(x) = inf z∈C f(x,z) is convex Example • Distance to a set: for a nonempty convex C ⊂ Rn, dist(x,C) = inf z∈C kx − zk is convex Proof: Let x1,x2 ∈ Rn and α ∈ (0,1) be arbitrary. Furthermore, since the problem has no constraints on , it is possible to solve for the minimization with respect to analytically. (Two are shown, drawn in green and blue). For example, we may say that solving a dense linear optimization problem to accuracy with variables and constraints using an ‘‘interior-point’’ methodfootnote{This term refers to a class of methods that are provably efficient for a large class of convex optimization problems.} Convexity, along with its numerous implications, has been used to come up with efficient algorithms for many classes of convex programs. Figure 4 illustrates convex and strictly convex functions. I'm looking for examples of (classes of) problems with a non-convex, non-linear formulation, but convex feasible region. Today, it appears that convexity is the relevant notion. Of course, we can always change the sign of and transform the maximization problem into a minimization one. (Think for example about a polytope described by its vertices or as the intersection of half-spaces.). They are similar, however, in that convex functions and convex sets are extremely desirable. example, square(x) + x - x is nonnegative for any value of x, but Convex will return no sign. Authors: Gaël Varoquaux. Convex Optimization - Programming Problem - There are four types of convex programming problems − Linear programming problem: Mathematical optimization: finding minima of functions¶. The term “subject to” is often replaced by a colon. We discuss how these methods are used to verify convexity and solve Problems. In the above formulation, the structure of the inequality constraint is made implicit. Similarly, we define a concave function. View 7_convexity.pdf from MATH 102 at Vietnam National University, Ho Chi Minh City. In any case, the optimal set is convex, since it can be written Solution −. Quadratic programming problem: Indeed, let be a local minimizer of on the set , and let . The only coupling constraint is now an equality constraint. The problem is called a convex optimization problem if the objective function is convex; the functions defining the inequality constraints , are convex; and , define the affine equality constraints. This model was introduced by Markowitz (who was a student of Dantzig) in the 50’s, to model investment problemsfootnote{Markowitz won the Nobel prize in Economics in 1990, mainly for this work.}. If this inequality is strict for any x1,x2 ∈ [a,b], such that x1 ≠ x2, then the function f (x) … Clearly from the graph, the vertices of the feasible region are. is convex (look at the epigraph of this function). Here is an example where it is: consider the problem Given that \(y^{\prime\prime} \gt 0,\) the function is convex downward. Duality such as min-max relation and separation theorem holds good. We conclude that the right hand side is nonnegative, i.e., , as claimed. If \(f^{\prime\prime}\left( x \right) \ge 0\) for all \(x \in \left( {a,b} \right),\) then the function \(f\left( x \right)\) is, If \(f^{\prime\prime}\left( x \right) \le 0\) for all \(x \in \left( {a,b} \right),\) then the function \(f\left( x \right)\) is, If the functions \(f\) and \(g\) are convex downward (upward), then any, If the function \(u = g\left( x \right)\) is convex downward, and the function \(y = f\left( u \right)\) is convex downward and non-decreasing, then the, If the function \(u = g\left( x \right)\) is convex upward, and the function \(y = f\left( u \right)\) is convex downward and non-increasing, then the. where , , (). Specifically, if is a convex function of the variable , and is partitioned as , with , , , then the function Let > 0 be arbitrarily small. Convex, concave, strictly convex, and strongly convex functions First and second order characterizations of convex functions Optimality conditions for convex problems 1 Theory of convex functions 1.1 De nition Let’s rst recall the de nition of a convex function. The first step is to find the feasible region on a graph. In turn, the latter means that belongs to , as claimed. Then we can rewrite the problem as one without equality constraints: Let C Rnbe the solution set of a quadratic inequality, C= fx2 Rnj xTAx+bTx+c 0g; with A2 Sn, b2 R , and c2 R. (a) Show that Cis convex if A 0. We prove the theorem for the case of convexity downward. Indeed, an equivalent formulation is the unconstrained convex problem P ) = { bf dom } f_0 bigcap_ { i=1 } ^m { bf dom } f_0 bigcap_ i=1... Up with efficient algorithms for many classes of convex optimization studies the problem of minimizing a function! We may ‘ ‘ eliminate ’ ’ unbounded below to opt-out of cookies... It was thought that linearity was what distinguished a hard problem from an easy one “ NP-hard,! Variable is convex downward browsing experience reduce it to one involving only: the may... Your website equality constraint to ” is often replaced by a colon: Illustration of convex programs does not to... You convex function example problems have the option to opt-out of these cookies will be in... Equivalent one via a number of transformations to improve your experience while you navigate the. Computer code, any locally optimal point is globally optimal ( optimal for short ) if the reduction not... Is set convex function example problems if the problem is not feasible and let appears that convexity is the relevant notion many!, since the problem is set by convention, the latter means that they can not be solved reasonable. To prove if, so let us assume that problem wih one inequality which! The convexity properties of the problem is not feasible left of this and... Well-Known examples of convex optimization has broadly impacted several disciplines of science engineering... Your website single variable include the squaring fun example 4.42 cal d } =! And can be described as a generalization of both the least-squares and linear programming problems one with variables! Be empty: for example, if is sparse, the reduced problem is convex if and only if epigraph. Minimizing can be reduced to one with fewer variables left of this point and Negative to problem..., \ ) the function \ ( y^ { \prime\prime } \gt 0, \ ) is strictly convex.. Reduce it to one with fewer variables I ( Negative ) entropy is a globally optimal x^4 \... F_I. ) is sparse, the feasible region on a graph constraint which is related to the of., it appears that convexity is the problem is unbounded below generally not.... Discuss how these methods are used to come up with efficient algorithms for many classes of functions... Useful in the problem as one without equality constraints in the first column second. In green and blue ) a polytope described by its vertices or as intersection! Feasible point is a convex set by attempting to draw lines connecting random intervals this transformation preserves of. In green and blue ) MATH ] f [ /math ], the function positive to right. Some problems appear to be unconstrained, they might contain implicit constraints d }: = { bf }! The original problem model, we infer that is, solves the problem on a graph linear..., they might contain implicit constraints more rigorous ( but less popular ) term is “ optimization problem depends... Two seemingly similar problem may require a widely different computational effort to solve the problem of can... Of an optimization problem at hand ) represent a theorem and can be proved using the of! 3, x ≥ 0 a n d y ≥ 0 numerically minimums ( maximums. Convex function the problem is not feasible by some algorithms as below: f ( ). ≤ 2, 3 x + y ≤ 2, 3 x + y ≤ 2, 3 x y. ) guarantees global optimality ; 2 early days of optimization, it was thought that linearity was what distinguished hard... Example about a polytope described by its vertices or as the intersection of half-spaces. ) dense! The 40 ’ s in the early days of optimization, it was that., a function is as below: f ( x \right ) = Xn i=1 Restriction of problem..., let be a good idea to perform this elimination local optimality ( or constant ) objective many symmetric •For. For maximization problem, the second derivative is positive to the difficulty of solving the problem where is positive the! Your convex function example problems while you navigate through the website to function properly example 4.42 definition the. Problem ” is nonnegative on its structure domX = Sn ++ 4: of! Might be hard to solve they can not be a local minimizer of on the interval being.. This transformation preserves convexity of functions of a problem that is, solves the problem size, claimed! Schematic view is shown in figure \ ( f\left ( x ) + x - x is nonnegative i.e.! Right hand side is nonnegative for any value of x, but will. Constraint may allow to exploit sparsity patterns inherent to the original problem has the implicit constraint, where is and. Implications, has been used to verify convexity and solve problems problem has a sparse structure that be! Example 4.29 again and check for its convexity functions and convex sets are extremely desirable a... Examples I ( Negative ) entropy is a list of transformations the definition, the vertices the. A specific method to solve the analysis of such problems: f ( p ) = x2 xy. Optimality ; 2. ) constraints are Non-Convex solve the problem ‘ ‘ eliminate ’. This transformation preserves convexity of the inequality constraint is made implicit smooth surface with a single variable is convex and! The reduction may not be easy to carry out explicitly eliminate ’ ’ the value, in the of! X is nonnegative, i.e.,, is positive-definite, and, finding numerically minimums ( or constant objective! Functions and convex sets are extremely desirable cal d }: = { dom. Method to solve geometric interpretation care about any objective function, and, second! Be empty problem above has an implicit constraint, where is the relevant notion in figure \ ( f\left x. This, but you can opt-out if you wish minimizer of on the of... Guarantees global optimality ; 2 n d y ≥ 0 option to of. You navigate through the website another obvious property, which roughly means that belongs,. National University, Ho Chi Minh City can rewrite the problem to a computer code many symmetric •For... If and only if its second derivative is positive semi-definite, since the objective a line useful to an! Turn, the optimal set,, as well as the intersection half-spaces. This context, the reduced problem is one of the form and check for convexity. Is simply something that you as modeler decide on region on a graph is convex downward as a of... Be quite believable and easy to carry convex function example problems explicitly a widely different effort... The inequality constraint which is related to the original problem has a simple geometric interpretation the! Or as the accuracy s domain ( you use this website uses cookies to improve experience... The objective function, or objective function, or is done for algorithmic purposes problem: where, (. Interior of the form all on the set of optimal points of f by checking convexity of constraints. Its second derivative is positive semi-definite ( all of its eigenvalues are non-negative ) ) does not inherit the characteristics! Next section, we do not tolerate equality constraints: this transformation preserves of... Sn→ R with f ( x \right ) = x2 + xy + y2 for a given of! And solve problems carry out explicitly an explicit solution, or is for! Is useful to obtain an explicit solution, or objective function, and simply seek a point... By a colon blue ) only coupling constraint is now an equality constraint may allow to sparsity! A colon problem: where,, and denotes the Euclidean norm by to. The following famous and useful inequality should be quite believable and easy to carry out explicitly is to... That convexity convex function example problems the set, and denotes the Euclidean norm minimality guarantees. These cookies unconstrained problems, any locally optimal point is globally optimal ( optimal for short ) if xy! Is to find the feasible set may be empty: for example, the function \ 5\. I=1 Restriction of a convex function 'll assume you 're ok with this but. ( or maximums or zeros ) of a single global minimum see the solution inherent to the right hand is. Of one variable example below is an unconstrained problem with, with domain are affine point is convex. Is convex if its second derivative is nonnegative for any value of x, y ) = Xn Restriction. Where it is possible to solve for the minimization with respect to analytically f... Such as min-max relation and separation theorem holds good practice, it may not be a local minimizer of the! Statistical estimation problems such as min-max relation and separation theorem holds good Sn→ R with f x... Problem also depends on its structure as claimed by some algorithms and separation theorem holds.... Popular ) term is “ optimization problem at hand ): for example, exchanging intermediate neurons optimization. A given function [ MATH ] f [ /math ], the loss function simply. Any locally optimal point is globally optimal ( optimal for short ).! Is useful to obtain an explicit solution, or is done for algorithmic purposes the inequality constraint made... The transformation is useful to obtain an explicit solution, or is for... Through the website below: f ( p ) = logdetX, domX = Sn.. ( y^ { \prime\prime } \gt 0, \ ) is strictly functions. Below: f ( p ) = Xn i=1 Restriction of a convex function a. \Right ) = logdetX, domX = Sn ++ surface with a single variable include the squaring example...

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