# On the Value Function of a Mixed Integer Linear Optimization Problem and an Algorithm for its Construction

Save this PDF as:

Size: px
Start display at page:

Download "On the Value Function of a Mixed Integer Linear Optimization Problem and an Algorithm for its Construction"

## Transcription

1 On the Value Function of a Mixed Integer Linear Optimization Problem and an Algorithm for its Construction Ted K. Ralphs and Anahita Hassanzadeh Department of Industrial and Systems Engineering, Lehigh University, USA Technical Report 14T-004

2 On the Value Function of a Mixed Integer Linear Optimization Problem and an Algorithm for its Construction Ted K. Ralphs 1 and Anahita Hassanzadeh 1 1 Department of Industrial and Systems Engineering, Lehigh University, USA August 3, 2014 Abstract This paper addresses the value function of a general mixed integer linear optimization problem (MILP). The value function describes the change in optimal objective value as the right-hand side is varied and understanding its structure is central to solving a variety of important classes of optimization problems. We propose a discrete representation of the MILP value function and describe a cutting plane algorithm for its construction. We show that this algorithm is finite when the set of right-hand sides over which the value function of the associated pure integer optimization problem is finite is bounded. We explore the structural properties of the MILP value function and provide a simplification of the Jeroslow Formula obtained by applying our results. 1 Introduction Understanding and exploiting the structure of the value function of an optimization problem is a critical element of solution methods for a variety of important classes of multi-stage and multi-level optimization problems. Previous findings on the value function of a pure integer linear optimization problem (PILP) have resulted in finite algorithms for constructing it, which have in turn enabled the development of solution methods for two-stage stochastic pure integer optimization problems (Schultz et al., 1998; Kong et al., 2006) and certain special cases of bilevel optimization problems (Bard, 1991, 1998; S DeNegre, 2011; Dempe et al., 2012). Studies of the value function of a general mixed integer linear optimization problem (MILP), however, have not yet led to algorithmic advances. Algorithms for construction have only been proposed in certain special cases (Güzelsoy and Ralphs, 2006), but no practical characterization is known. By practical, we mean a characterization and associated representation that is suitable for computation and which we can use to formulate problems in which the value function of a MILP is embedded, e.g., multi-stage stochastic programs. In this paper, we extend previous results by demonstrating that the MILP value function has an underlying discrete structure similar to the PILP value function, even in the general case. This 2

3 discrete structure emerges from separating the function into discrete and continuous parts, which in turn enables a representation of the function in terms of two discrete sets. We show that this representation can be constructed and propose an algorithm for doing so. Both the representation and the algorithm are finite under the assumption that the set of right-hand sides over which the related PILP value function is finite is bounded. We consider the value function associated with a nominal MILP instance defined as follows. The variables of the instance are indexed on the set N = {1,..., n}, with I = {1,..., r} denoting the index set for the integer variables and C = {r + 1,..., n} denoting the index set for the continuous variables. For any D N and a vector y indexed on N, we denote by y D the sub-vector consisting of the corresponding components of y. Similarly, for a matrix M, we denote by M D the sub-matrix constructed by columns of M that correspond to indices in D. Then, the nominal instance we consider throughout the paper is given by z IP = inf (x,y) X c I x + c Cy, (MILP) where (c I, c C ) R n is the objective function vector and X = {(x, y) Z I + R C + : A I x+a C y = d} is the feasible region, described by A I Q m r, A C Q m (n r), and d R m. A pair of vectors (x, y) X is called a feasible solution and c I x + c Cy is its associated solution value. For such a solution, the vector x is referred to as the integer part and the vector y as the continuous part. Any (x, y ) X such that c I x + c C y = z IP is called an optimal solution. Throughout the paper, we assume rank(a, d) = rank(a) = m. The value function is a function z : R m R {± } that describes the change in the optimal solution value of a MILP as the right-hand side is varied. In the case of (MILP), we have z(b) = inf (x,y) S(b) c I x + c Cy b B, (MVF) where for b R m, S(b) = {(x, y) Z r + R n r + : A Ix + A C y = ˆb} and S I (b) = {x Z r + : A I x = b}. We let B = {b R m : S(b) }, B I = {b R m : S I (b) } and S I = b B S I (b). By convention, we let z(b) = for b R m \ B and z(b) = when the infimum in (MVF) is not attained for any x S(b) for b B. To simplify the presentation, we assume that z(0) = 0, since otherwise z(b) = for all b R m with S(b) (Nemhauser and Wolsey, 1988). To illustrate the basic concepts, we now present a brief example that we refer to throughout the paper. Example 1. Consider the MILP value function defined by z(b) = inf 3x x 2 + 3x 3 + 6x 4 + 7x 5 s.t. 6x 1 + 5x 2 4x 3 + 2x 4 7x 5 = b x 1, x 2, x 3 Z +, x 4, x 5 R +. (Ex.1) Figure 1 shows this non-convex, non-concave piecewise polyhedral function. Although the MILP with which the value function in Example 1 is associated has only a single constraint, the structure of the function is already quite complex. Nevertheless, the function does have an obvious regularity to it. The gradient of the function is always one of only two values, 3

4 Figure 1: MILP Value Function of (Ex.1). which means that its epigraph is the union of a set of polyhedral cones that are identical, aside from the location of their extreme points. This allows the function to be represented simply by describing this single polyhedral cone and a discrete set of points at which we place translations of it. In the remainder of the paper, we formalize the basic idea illustrated in Example 1 and show that it can be generalized to obtain a similar result that holds for all MILP value functions. More specifically, in Section 3, we review some basic properties of the LP value function and define the continuous restriction, an LP whose value function yields the aforementioned polyhedral cone. In Sections 4, we present the first main result in Theorem 1, which characterizes the minimal discrete set of points that yields a full description of the value function. This discrete set generalizes the minimal tenders of Kong et al. (2006) used to represent the PILP value function. In Section 5, we exploit this representation to uncover further properties of the value function, culminating in Theorem 2, which shows that there is a one-to-one correspondence between the discrete set of Theorem 1 and the regions over which the value function is convex (the local stability regions). In Section 6, we discuss the relationship of our representation with the well-known Jeroslow Formula, showing in Theorem 3 that the original formula can be simplified using the discrete representation of Theorem 1. Finally, in Section 7 we demonstrate how to put the representation into computational practice by presenting a cutting plane algorithm for constructing a discrete representation such as the one in Theorem 1 (though possibly not provably minimal). Before getting to the main results, we next summarize related work. 2 Related Work Much of the recent work on the value function has addressed the pure integer case, since the PILP value function has some desirable properties that enable more practical results. Blair and Jeroslow (1982) first showed that the value function of a PILP is a Gomory function that can be derived by taking the maximum of finitely many subadditive functions. Conti and Traverso (1991) then proposed using reduced Gröbner basis methods to solve PILPs. Subsequently, in the context of stochastic optimization, Schultz et al. (1998) used the so-called Buchberger Algorithm to compute 4

5 the reduced Gröbner basis for solving sequences of integer optimization problems which differ only in their right-hand sides. In the same paper, the authors recognized that over certain regions of the right-hand side space, the pure integer value function remains constant. This property turned out to be quite significant, resulting in algorithms for two-stage stochastic optimization (Ahmed et al., 2004; Kong et al., 2006). In the same vein, Kong et al. (2006) proposed using the properties of a pure integer optimization problem in two algorithms for constructing the PILP value function when the set of right-hand sides is finite. The complex structure of the MILP value function makes the extension of results in linear and pure integer optimization to the general case a challenge. In particular, with the introduction of continuous variables, we no longer have countability of the set of right-hand sides for which S(b) (or finiteness in the case of a bounded S(b)), which is a central property in the PILP case. The MILP value function also ostensibly lacks certain properties used in previous algorithms for eliminating parts of the domain from consideration in the pure integer case. For the mixed integer case, Bank et al. (1983) studied the MILP value function in the context of parametric optimization and provided theoretical results on regions of the right-hand side set over which the function is continuous. In a series of papers, Blair and Jeroslow (1977, 1979) studied the properties of the MILP value function and showed that it is a piecewise polyhedral function. Blair and Jeroslow (1984) identified a subclass of Gomory functions called Chvátal functions to which the general MILP value function belongs. However, a closed form representation was not achieved until a decade later in a subsequent work of Blair (1995). The so-called Jeroslow Formula represents the MILP value function as collection of Gomory functions with linear correction terms. This characterization is related to ours and we discuss this relationship in Section 6. 3 The Continuous Restriction To understand the MILP value function, it is important to first understand the structure of the value function of a linear optimization problem. In particular, we are interested in the structure of the value function of the LP arising from (MILP) by fixing the values of the integer variables. We call this problem the continuous restriction (CR) w.r.t a given ˆx S I. Its value function is given by z(b; ˆx) = c I ˆx + inf c Cy s.t. A C y = b A I ˆx y R n r +. (CR) For a given ˆx S I, we let S(b, ˆx) = {y R n r + : A C y = b A I ˆx}. As before, we let z(b; ˆx) = if S(b, ˆx) = for a given b B and z(b; ˆx) = if the function value is unbounded. As we will show formally in Proposition 4, it is evident that for any ˆx S I, z( ; ˆx) bounds the value function from above, which is the reason for the notation. When ˆx = 0 in (CR), the resulting function is in fact the value function of a general LP, since A C is itself an arbitrary matrix. In the remainder of the section, we consider this important special case and define z C (b) = inf c Cy s.t. A C y = b y R n r +. (LVF) 5

6 Figure 2: The value function of the continuous restriction of (Ex.2) and a translation. We let K be the polyhedral cone that is the positive linear span of A C, i.e., K = {λ 1 A r λ n r A n : λ 1,..., λ n r 0}. As we discuss later, this cone is the set of right-hand sides for which z C is finite and plays an important role in the structure of both the LP and MILP value functions. The following example illustrates the continuous restriction associated with a given MILP. Example 2. Consider the MILP inf 2x 1 + 6x 2 + 7x 3 + 5x 4 s.t. x 1 + 2x 2 7x 3 + x 4 = b x 1 Z +, x 2, x 3, x 4 R +. (Ex.2) The value functions of the continuous restriction w.r.t. x 1 = 0 and x 1 = 1 are plotted in Figure 2. Note that in the example just given, z( ; 1) is simply a translation of z C. As we will explore in more detail later, this is true in general, so that for ˆx S I, we have z(b; ˆx) = c I ˆx + z C (b A I ˆx) b B. Thus, the following results can easily be generalized to the continuous restriction functions w.r.t. points other than the origin. We shall now more formally analyze the structure of z C. We first present a representation due to Blair and Jeroslow (1977), who characterized the LP value function in terms of its epigraph. Let L = epi(z C ). Proposition 1 (Blair and Jeroslow, 1977) The value function of z C is a convex polyhedral function and its epigraph L is the convex cone cone{(a r+1, c r+1 ), (A r+2, c r+2 ),..., (A n, c n ), (0, 1)}. The above description of the LP value function in terms of a cone is not computationally convenient for reasons that will become clear. We can derive a more direct characterization of the LP value 6

7 function by considering the structure of the dual of (LVF) for a fixed right-hand side ˆb R m. In particular, this dual problem is sup ν S D ˆb ν, (3.1) where S D = {ν R m : A C ν c C}. Note that our earlier assumption that z(0) = 0 implies S D. Let {ν i } i K be the set of extreme points of S D, indexed by set K. When S D is unbounded, let its set of extreme directions {d j } j L be indexed by set L. From strong duality, we have that z C (ˆb) = sup ˆb ν SD ν when S D. If the LP with right-hand side ˆb has a finite optimum, then z C (ˆb) = sup ˆb ν = sup ˆb ν i. (3.2) ν S D i K Otherwise, for some j L, we have ˆb d j > 0 and z C (ˆb) = +. We can therefore obtain a representation of the cone L as {(b, z) R m+1 : b ν i z, b d j 0, i K, j L}. Let E be the set of index sets of the nonsingular square sub-matrices of A C corresponding to dual feasible bases. That is, E E if and only if i K such that A E νi = c E. Abusing notation slightly, we denote this (unique) ν i by ν E in order to be consistent with the literature. The cone L has an extreme point if and only if there exist m + 1 linearly independent vectors in the set {(ν i, 1) : i K} {(d j, 0) : j L}. It is easy to show that in this case, the origin is the single extreme point of L and all dual extreme points are optimal at the origin, i.e., νe 0 = c E A 1 E 0 = z C(0) = 0 for all E E. Conversely, when L has an extreme point, it must be the single point at which all the inequalities in the description of L are binding. The convexity of z C (b) follows from the representation (3.2), since z C (b) is the maximum of a finite number of affine functions and is hence a convex polyhedral function (Bazaraa et al., 1990; Blair and Jeroslow, 1977). With respect to differentiability, consider a right-hand side b B for which the optimal solution to the corresponding LP is non-degenerate. Let the (unique) optimal basis and optimal dual solution be A E and ν E, respectively, for some E E. As a result of the unchanged reduced costs, under a small enough perturbation in b, A E and ν E remain the optimal basis and dual solution to the new problem. Hence, the function is affine in a neighborhood of b and differentiability of the LP value function at b follows. On the other hand, whenever the value function is non-differentiable, the problem has multiple optimal dual solutions and every optimal basic solution to the primal problem is degenerate. These observations result in the following characterization of the differentiability of the LP value function. Proposition 2 (Bazaraa et al., 1990) If z C is differentiable at ˆb K, then the gradient of z C at ˆb is the unique ν SD such that z C (ˆb) = ˆb ν. If ˆb int(k) is a point of non-differentiability of z C, then there exist ν 1, ν 2,..., ν s S D with s > 1 such that z C (ˆb) = ˆb ν 1 = ˆb ν 2 =... = ˆb ν s and every optimal basic solution to the associated LP with right-hand side ˆb is degenerate. Example 3. In (Ex.2), we have z C (b) = sup{νb : 1 ν 3, ν R} = { 3b if b 0 b if b < 0 7

8 Then, E = {{1}, {2}, {3}} with A {1} = 2, A {2} = 7, and A {3} = 1. The corresponding basic feasible solutions to the dual problem are 3, 1, and 5 respectively. If the value function is differentiable at ˆb R, then its gradient at ˆb is either -1 or 3. These extreme points describe the facets of the convex cone L = cone{(2, 6), ( 7, 7), (1, 5), (0, 1)} = {(b, z) R 2 : z 3b, z b}. Note that we can conclude that fixing x 1 to 0 in (Ex.2) does not affect its value function. Finally, note that K = R, i.e., z C (b) < for all b R. We have so far examined the LP value function arising from restricting the integer variables to a fixed value and discussed that such a value function inherits the structure of a general LP value function. The LP value function, though it arises from a continuous optimization problem, has a discrete representation in terms of the extreme points and extreme directions of its dual. In the next section, we study the effect of the addition of integer variables. 4 A Characterization of the MILP Value Function The goal of this section is to derive a discrete representation of a general MILP value function building from the results of the previous section. We observe that the MILP value function is the minimum of a countable number of translations of z C and thus retains the same local structure as that of the continuous restriction (CR). By characterizing the set of points at which these translations occur, we arrive at Theorem 1, our discrete characterization. From the MILP value function (Ex.1) and its continuous restriction w.r.t ˆx = 0, plotted respectively in Figures 1 and 2, we can observe that when integer variables are added to the continuous restriction, many desirable properties of the LP value function, such as convexity and continuity, may be lost. The value function in this particular example remains continuous, but as a result of the added integer variables, the function becomes piecewise linear and additional points of nondifferentiability are introduced. In general, however, even continuity may be lost in some cases. Let us consider another example. Example 4. Consider z(b) = inf x x x x 4 s.t. 5 4 x 1 x x x 4 = b (Ex.4) x 1, x 2 Z +, x 3, x 4 R +. Figure 3 shows this value function. As in (Ex.1), the value function is piecewise linear; however, in this case, it is also discontinuous. More specifically, it is a lower semi-continuous function. The next result formalizes these properties. Proposition 3 (Nemhauser and Wolsey, 1988; Bank et al., 1983) The MILP value function (MVF) is lower semi-continuous, subadditive, and piecewise polyhedral over B. Characterizing a piecewise polyhedral function amounts to determining its points of discontinuity and non-differentiability. In the case of the MILP value function, these points are determined by properties of the continuous restriction, which has already been introduced, and a second problem, 8

9 Figure 3: Value Function (Ex.4). called the integer restriction, obtained by fixing the continuous variables to zero. This problem is defined as follows. z I (b) = inf c I x s.t. A I x = b x Z r +. The role of the integer restriction in characterizing the value function will become clear shortly, but we first need to introduce some additional concepts. Recalling that the continuous restriction for any ˆx S I can be expressed as z(b; ˆx) = c I ˆx + z C (b A I ˆx), we obtain the following representation of (MVF) in terms of the continuous restriction: z(b) = inf x S I c I x + z C (b A I x) = inf x S I z(b; x) = inf ˆb BI z(ˆb) + z C (b ˆb) b B. (4.1) This shows that the MILP value function can be represented as a countable collection of value functions of continuous restriction functions arising from translations of the LP value function z C. Describing the value function consists essentially of characterizing the minimal set of points at which such translations must be located to yield the entire function. The points at which translations may potentially be located can be thought of as corresponding to vectors x S I, as in the first two equation above, though more than one member of S I may specify the same location. Equivalently, we can also consider describing the function simply by specifying its value at points in B I, as in the third equation above, which makes the correspondence one-to-one. Despite being finite under the assumption that B I is finite, this characterization is nevertheless still quite impractical, as both S I and B I may be very large. As one might guess, it is not necessary to consider all members of B I in order to obtain a complete representation. Later in this section, we characterize the subset of B I necessary to guarantee a complete description. This characterization provides a key insight that leads eventually to our algorithm for construction. Before moving on, we provide some examples that illustrate how the structure of z C influences the structure of (MVF). First, we examine the significance of the domain of z C in the structure and the continuity of the MILP value function with the following example. (IR) 9

10 Example 5. Consider again the value function (Ex.4). Its continuous restriction w.r.t ˆx = 0 is Equivalently, z C (b) = inf 3 4 x x 2 s.t. 1 2 x x 2 = b x 1, x 2 R +. z C (b) = sup{νb : ν 3, ν R}. (4.2) 2 Here, the positive linear span of { 1 2, 1 3 } is K = R +. We also have z C (b) = 3 2b for all b K. The gradient of z C (b) at any b R + \{0} is 3 2, which is the extreme point of the feasible region of (4.2). Note that for b R, z C (b) = + because the continuous restriction w.r.t the origin is infeasible whenever b R and its corresponding dual problem is therefore unbounded. However, in the modification of this problem in (Ex.4), we have B = R, while K remains R +. This is because the additional integer variables result in translations of K into R. These translations result in the discontinuity of the value function observed in (Ex.4). The next result shows that the continuous restriction with respect to any fixed ˆx S I bounds the value function from above, as it is a restriction of the value function by definition. Proposition 4 For any ˆx S I, z( ; ˆx) bounds z from above. Proof. For ˆx S I we have z(b; ˆx) = c I ˆx + z C (b Aˆx) inf x S I c I x + z C (b A I x) = z(b). The second result shows that the continuous restriction with respect to the origin coincides with the value function z over the intersection of K and some open ball centered at the origin. We denote an open ball with radius ɛ > 0 centered at a point d by N ɛ (d). Proposition 5 There exists ɛ > 0 such that z(b) = z C (b) for all b N ɛ (0) K. Proof. At the origin, we have z(0) = 0 with a corresponding optimal solution to the MILP being (x I, x C ) = (0, 0). For a given ˆb R, as long as there exists an optimal solution ˆx to the MILP with right-hand side b such that ˆx = 0, we must have z(ˆb) = z C (ˆb). Therefore, assume to the contrary. Then for every ɛ > 0, b N ɛ (0) K, b 0 such that z C ( b) > z( b). Consider an arbitrary ɛ > 0 and an arbitrary b N ɛ (0) K, b 0 such that z C ( b) > z( b). Then if x is a corresponding optimal solution to the MILP with right-hand side b, we must have ˆx 0. Let E and Ê denote the set of column indices of sub-matrices of A C corresponding to optimal bases of the continuous restrictions at 0 and ˆx, respectively (note that both must exist). Case i. E = Ê. We have z C (ˆb) > z(ˆb) c EA 1ˆb E > c I ˆx + c Ê A 1 ˆb c Ê ÊA 1 A Ê I ˆx 0 > c I ˆx c Ê A 1A Ê I ˆx. 10

11 However, the last inequality implies that at the origin, (ˆx, A 1 A Ê I ˆx) provides an improved solution so that z(0) < 0, which is a contradiction. Case ii. A E AÊ. We have z C (ˆb) = c E A 1ˆb E > c Ê A 1 ˆb, which is a contradiction of the fact that Ê z C is the value function of the continuous restriction at 0. Example 6. Figure 4a shows that the epigraph of the value function of (Ex.1) coincides with the cone epi(z C ) = cone{(2, 6), ( 7, 7), (0, 1)} on N (0). Similarly, Figure 4b demonstrates that the epigraph of the discontinuous value function (Ex.4) coincides with epi(z C ) = cone { ( 1 2, 3 4 ), ( 1 3, 5 2 ), (0, 1)} on N 0.25 (0) K = [0, 0.25) R +. (a) (b) Figure 4: The MILP value function and the epigraph of the (CR) value function at the origin. The characterization of the value function we proposed in (4.1) is finite as long as the set S I is finite. However, there are cases where the set B I = {b B : S I (b) } is finite, while S I remains infinite. Clearly in such cases, there is a finite representation of the value function that (4.1) does not provide. We can address this issue by representing the value function in terms of the set B I rather than the set S I, but even then, the representation is not minimal, as not all members of B I are necessary to the description. We next study the properties of the minimal subset of B I that can fully characterize the value function of a MILP. From the previous examples, we can observe that when the MILP has only a single constraint and the value function is thus piecewise linear, the points necessary to describe the function are the lower break points. To generalize the notion of lower break points to higher dimension, we need some additional machinery. In Figure 4, the lower break points are also local minima of the MILP value function and one may be tempted to conjecture that knowledge of the local minima is enough to characterize the value function. Unfortunately, it is easy to find cases for which the value function has no local 11

12 minima and yet still has the nonconvex structure characteristic of a general MILP value function. Consider the following example. Example 7. Consider z(b) = inf 2x 1 + 6x 2 7x 3 s.t. x 1 2x 2 + 7x 3 = b x 1 Z +, x 2, x 3 R +. (Ex.6) As illustrated in Figure 5, the extreme point of the epigraph of the continuous restriction of the problem does not correspond to a local minimum. In fact the value function does not have any local minima. Figure 5: MILP value function (Ex.6) with no local minimum. In the previous examples, the epigraph of z C was also always a pointed cone. As a result, the MILP value function had lower break points that corresponded to the extreme points of epi( z( ; x)) for certain x S I. However, the cone epi(z C ) may not have an extreme point in general. When it fails to have one, in the single-dimensional case, the MILP value function will be linear and will have no break points. Consider the following example. Example 8. Consider z(b) = inf 2x 1 + 6x 2 7x 3 s.t. x 1 6x 2 + 7x 3 = b x 1 Z +, x 2, x 3 R +. (Ex.7) In this example, the value function (Ex.7) coincides with the value function of the continuous restriction w.r.t the origin. This function is plotted in Figure 6. In this last example, the epigraph of the value function contains a line that passes through the origin. This property can be generalized to any dimension. If epi(z C ) is not a pointed cone, then 12

13 Figure 6: Linear and convex MILP and CR value functions to (Ex.7). for any given ˆx S I, the boundary of the epigraph of z( ; ˆx) contains a line that passes through (A I ˆx, z(a I ˆx; ˆx)). The boundary of the resulting MILP value function therefore contains parallel lines that result from translations of z. Clearly, to characterize such a value function, one would need to have, for each such line, a point ˆb such that (ˆb, z(ˆb)) is on both the line and the value function of the continuous restriction, z C. This case, in which epi(z C ) is not a pointed cone, is, however, an edge case and its consideration would complicate the presentation substantially. For the remainder of this section, we therefore assume the more common case in which epi(z C ) is a pointed cone. To generalize the set of lower break points to higher dimension, we introduce the notion of points of strict local convexity of the MILP value function. We denote the set of these points by B SLC. Definition 1 A point ˆb B I is a point of strict local convexity of the function f : R m R {± } if for some ɛ > 0 and g R m, we have f(b) > f(ˆb) + g (b ˆb) for all b N ɛ (ˆb), b ˆb. This definition requires the existence of a hyperplane that is tangent to the function f at the point ˆb BI, while lying strictly below f in some neighborhood of ˆb. For the continuous restriction with respect to ˆx S I, this can happen only at the extreme point of the epigraph of the function, if such a point exists. Note that at such a point, we must have z(ˆb; ˆx) = c I ˆx. Furthermore, if ˆx arg inf x SI z(ˆb; x), then we will also have z(ˆb; ˆx) = z I (ˆb). Proposition 6 For a given ˆx S I, ˆb A I ˆx + K is a point of strict local convexity of z( ; ˆx) if and only if (ˆb, z(ˆb; ˆx)) is the extreme point of epi( z( ; ˆx)). Proof. Let ˆx S I and ˆb A I ˆx + K be given as in the statement of the theorem. We use the following property in the proof. Let the function H t be defined by { c I H t (b) = ˆx + (b A I ˆx) η t for b K, + otherwise, 13

14 where η t {ν i } i K {d j } j L. Then, we have Moreover, z(b; ˆx) = sup H t (b) t K L z(ˆb; ˆx) = conv({ H 1,..., H p } p P ) = conv({η 1,..., η p } p P ), where P K L and P > 1 and finally, we have that z(ˆb; ˆx) = H 1 (ˆb) = = H p (ˆb) for p P. (4.3) ( ) Let ɛ and g be the radius of the ball and a corresponding subgradient showing the strict local convexity of z( ; ˆx) at ˆb. If z( ; ˆx) is differentiable at ˆb, then ν R m such that z( ; ˆx) = {ν}, and therefore g = ν. Then we trivially have that ˆb cannot be a point of strict local convexity of z( ; ˆx), as there always exists ɛ with 0 < ɛ < ɛ such that on N ɛ (ˆb), we have z(b; ˆx) = z(ˆb; ˆx) + ν (b ˆb). Therefore, z( ; ˆx) cannot be differentiable at ˆb. Since z( ; ˆx) is not differentiable at ˆb, there are H 1,..., H p, p P, as defined above. In the case that p > m, from the discussion in Section 3, ˆb has to be the extreme point of epi( z( ; ˆx)). Next, we show that ˆb cannot be the extreme point of epi( z( ; ˆx)) if p m. When 1 < p m, equation (4.3) must still hold. Let R = {(b, z(b; ˆx)) (A I ˆx + K) R : z(b; ˆx) = H 1 (b) = = H p (b) for p P } Then there exists b N ɛ (ˆb) such that ( b, z( b)) R and b ˆb. We have z( b; ˆx) z(ˆb; ˆx) = ( b ˆb) η t, t P. Then we can conclude that for g z(ˆb; ˆx) = conv({η 1,..., η p }), the function z(ˆb; ˆx)+g ( b ˆb) also coincides with z( b; ˆx) as follows. Choose 0 λ t 1, t P such that g = t P λt η t, t P λt = 1. From the equations λ t ( z( b; ˆx) z(ˆb; ˆx)) = λ t ( b ˆb) η t, t P we have a contradiction to ˆb being the point of strict local convexity of z( ; ˆx), since p z( b; ˆx) z(ˆb; ˆx) = λ t ( b ˆb) η t = g ( b ˆb). t=1 ( ) Since (ˆb, z(ˆb)) is the extreme point of epi( z( ; ˆx)), then z(ˆb; ˆx) = conv({η 1,..., η p }), where p P and we must have that P > m. Choose g int(conv({η 1,..., η p })). For an arbitrary b AI ˆx + K, b ˆb there exists η {η 1,..., η p } such that z( b; ˆx) = ( b A I ˆx) η. Then, from the monotonicity of the subgradient of a convex function we have ( η g )( b ˆb) > 0. Therefore, z( b; ˆx) = z(ˆb; ˆx) + η ( b ˆb) > z(ˆb; ˆx) + g (b ˆb) b A I ˆx + K, b ˆb. (4.4) That is, ˆb is a point of strict local convexity of z( ; ˆx). Example 9. Consider the MILP in Example 4. The blue shaded region in Figure 7 is epi( z( ; 1)). The point (1, 2) is the extreme point of the cone epi( z(b; 1)) and ˆb = 1 is a point of strict local convexity of the value function. Next, we discuss the points of strict local convexity of the MILP value function. 14

15 Figure 7: The value function of the MILP in (Ex.6) Proposition 7 If ˆb is a point of strict local convexity, then there exists ˆx S I such that ˆb = A I ˆx; (ˆb, z(ˆb; ˆx)) is the extreme point of epi( z( ; ˆx)); and z(ˆb; ˆx) = c I ˆx = z I(ˆb) = z(ˆb). Proof. Let ˆb be a point of strict local convexity. If there exists ˆx S I (ˆb) such that ˆx arg inf x SI z(ˆb; x), then we have that c I x = z(ˆb). The remainder of the statement is trivial in this case. Consider the case where such x does not exist. That is, for any (x, y) S(ˆb) such that c I x + c C y = z(ˆb), we have y > 0. Let one such point be (ˆx, ŷ). Consider ɛ > 0 used to show ˆb is a point of strict local convexity. If z( ; ˆx) coincides with z on N ɛ (ˆb), then from from Proposition 6 it follows that ˆb cannot be a point of strict local convexity. On the other hand, if z is constructed by multiple translations of z over N ɛ (ˆb), since it attains the minimum of these functions, there cannot be a supporting hyperplane to z at ˆb, therefore ˆb cannot be a point of strict local convexity. We note that the reverse direction of Proposition 7 does not hold. In particular, it is possible that for some ˆx S I we have z(a I ˆx) = c I ˆx, but that A I ˆx is not a point of strict local convexity. For instance, in example 1, for ˆx = (1, 0, 1) we have that A I ˆx = 2 and that z(2; ˆx) = z(2) = 6. Nevertheless, 2 is not a point of strict local convexity. Points of strict local convexity may lie on the boundary of B I. The next example illustrates a case where this happens. Example 10. Consider the MILP value function z(b) = inf x 1 + 3x 2 s.t. x 1 3x 2 = b x 1 Z +, x 2 R +. (Ex.9) shown in Figure 8. If we artificially impose the additional restriction that b [0, 2] for the purposes 15

16 Figure 8: MILP Value Function of (Ex.9) with B I = [0, 2]. of illustration, it is clear that there is no point of strict local convexity in the interior of B I, although epi(z C ) is a pointed cone. Let us further examine the phenomena illustrated by the previous example. For a given ˆx S I, let ˆb = AI ˆx. We know that the single extreme point of epi( z( ; ˆx)) is (ˆb, z(ˆb; ˆx)) and that there must therefore be m + 1 (2 in this example) facets of epi( z( ; ˆx)) whose intersection is this single extreme point. Now, if ˆb is not a point of strict local convexity, then on any N ɛ (ˆb) with ɛ > 0, at most m facets of epi( z( ; ˆx)) coincide with the facets of the epigraph of the value function. This means that there exists a direction in which z is affine in the neighborhood of (ˆb, z(ˆb; ˆx)); that is, ˆb cannot be a point of strict local convexity of z. Given that the set B I is assumed to be bounded, the value function must contain a point ( b, z( b)) such that b bd(conv(b I )) B I along a line in this direction. Let bd(b I ) = bd(conv(b I )) B I. Since epi(z C ) is pointed, then b has to be a point of strict local convexity of z. This latter point is the one needed to describe the value function the epigraph of the associated continuous restriction associated with b contains the continuous restriction w.r.t. ˆx, which means that Aˆx is not contained in the minimal set of points at which we need to know the value function. We are now almost ready to formally state our main result. So far, we have discussed certain properties of the points of strict local convexity and showed that such points can belong to the interior or boundary of B I. Our goal is to show that the set B SLC, which was previously defined to the set of all points of strict local convexity, is precisely the minimal subset of B I, denoted in the following result by B min, needed to characterize the full value function. Let us now formally define B min to be a minimal subset of B I such that z(b) = Then we have the following result. Proposition 8 B min = B SLC. inf ˆb Bmin z(ˆb) + z C (b ˆb) b B. (4.5) Proof. First, we show that if ˆb B\B SLC, then it is not in the set B min. If ˆb B\B I, then from (4.1) it follows that ˆb is not necessary to describe the value function, then ˆb / B min. Consider 16

17 ˆb BI \B SLC. Let ˆx S I (ˆb) such that c I ˆx = z(ˆb). Since epi(z C ) is assumed to be pointed, we have z(ˆb) = min{ z(ˆb; x 1 ), z(ˆb; x 2 ),..., z(ˆb; x k )}, where k > 1 and x 1,..., x k S I. Then, for some l = 1,..., k and x l ˆx we have min{ z(ˆb; ˆx), z(ˆb; x l )} = z(ˆb; x l ) and it follows that ˆb / B min. Therefore, if ˆb B min, then ˆb B SLC. We next show that if ˆb B SLC, then ˆb B min. Let us denote by S (ˆb) the set of points x S I such that (A I x, c I x) coincides with the value function at ˆb. If ˆb / B min, then all the points in S (ˆb) can be eliminated from the description of the value function in (4.1). That is, we have z(ˆb) = inf z(ˆb; x). x S I \S (ˆb) Therefore, for any pair (x, y) S(ˆb) that is an optimal solution to the MILP with right-hand side fixed at ˆb, we have y > 0. This, however, contradicts with Proposition 7 and we have that ˆb cannot be a point of strict local convexity of z. Because it will be convenient to think of the value function as being described by a subset of S I, rather than as a subset of B I, we now express our main result in those terms. From Proposition 8, it follows that there is a subset of S min of S I that can be used to represent the value function, as shown in the following theorem. Note, however, that while B min is unique, S min is not. Theorem 1 (Discrete Representation) Let S min be any minimal subset of S I such that for any b B min, x S min such that A I x = b and c I x = z(b).then for b B, we have z(b) = inf z(b; x) = inf z(b; x). (4.6) x S I x S min Proof. The proof follows from Proposition 8, noting that a point ˆx S I such that c I x > z(a Ix) cannot be necessary to describe the value function. Example 11. We apply the theorem to (Ex.1). In this example, over b [ 9, 9], we have that B min = { 8, 4, 0, 5, 6, 10} and S min = {[0; 0; 2], [0; 0; 1], [0; 0; 0], [0; 1; 0], [1; 0; 0], [0; 2; 0]}. Clearly, the knowledge of the latter set is enough to represent the value function. Theorem 1 provides a minimal subset of S I required to describe the value function. We discuss in Section 7 that constructing a minimal such subset exactly may be difficult. Alternatively, we propose an algorithm to approximate S min (with a superset that is thus still guaranteed to yield the full value function). This has proven empirically to be a close approximation. Before further addressing the practical matter of how to generate the representation, we discuss some theoretical properties of the value function that arise from our result so far in the next two sections. The reader interested in the computational aspects of constructing the value function can safely skip to Section 7 for the proposed algorithm, as that algorithm does not depend on the results in the following two sections. 17

18 5 Local Stability Regions In this section, we demonstrate that certain structural properties of the value function, such as regions of convexity and points of non-differentiability and discontinuity, can also be characterized in the context of our representation. We show that there is a one-to-one correspondence between regions over which the value function is convex and continuous the so-called local stability sets and the set B min. We also provide results on the relationships between this set and the sets of non-differentiability and discontinuity of the value function. We start this section by introducing notation for the sets of right-hand sides with particular properties. Definition 2 B LS (ˆb) = {b B : z(b) = z(ˆb) + z C (b ˆb)} is the local stability set w.r.t ˆb B; B ES (ˆb) = bd(b LS (ˆb)) is the local boundary set w.r.t ˆb B; B ES = b Bmin B ES (b) is the boundary set; B ND = {b B : z is not differentiable at b} is the non-differentiability set; and B DC = {b B : z is discontinuous at b} is the discontinuity set. Example 12. To illustrate the above definitions, consider the value function in Example 1. Let ˆb = 3. Over the interval [ 9, 9] we have that the function z(3) + zc (b 3) coincides with z at b B LS (ˆb) = [2.125, 3]. Then, B ES (ˆb) = {2.125, 3}. The minimal set is B min = { 8, 4, 0, 5, 6}. The boundary set consists of the union of the local boundary sets w.r.t. minimal points; i.e., B ES ={{ 9, 7.75} { 7.75, 3.75} { 3.75, 2.125} {2, 125, 5.125} {5.125, 8}} ={ 9, 7.75, 3.75, 2.125, 5.125, 8}. The non-differentiablity set is Finally, B DC =. B ND = { 9, 8, 7.75, 4, 3.75, 0, 2.125, 5, 5.125, 6, 8, 9}. The main result of this section is Theorem 2. The goal is to show that the value function is convex and continuous over the the local stability sets associated with the members of B min. Furthermore, in this theorem we demonstrate the relationship between the set B min, the boundary set, B ES, and the sets of point of non-differentiability and discontinuity of the value function. We next state the theorem. Theorem 2 i. Let ˆb B. There exists x S min such that for any b int(b LS (ˆb)), there exists y R n r + such that (x, y) is an optimal solution to the MILP with right-hand side b. 18

19 z is continuous and convex over int(b LS (ˆb)). ii. ˆb B ES if and only if for any ɛ > 0, x S I such that z(b) = c I x + z C (b A I x ) for all b N ɛ (ˆb). iii. Let ˆb B min. Then, int(b LS (ˆb)) is the maximal set of right-hand sides containing ˆb over which the value function is convex and continuous. iv. For the general MILP value function, we have B min B ND and B ES B ND. Furthermore, if the MILP value function is discontinuous, we have B min B DC B ES B ND. Proof. We build to the proof of the theorem, which constitute the remainder of this section, by proving lemmas 1 8, The first and second parts of the theorem follow from lemma 1 and lemma 2. The third part of the theorem is shown in lemma 3. The last part follows from lemmas 5 8. In the first lemma, we show properties of the function on differentiable regions within local stability sets. Lemma 1 Let ˆb B. Then there exists x S I such that for any b int(b LS (ˆb)), there exists y R n r + such that (x, y) is an optimal solution to the MILP with right-hand side b. Furthermore, z is continuous and convex over int(b LS (ˆb)) Proof. From Theorem 1, for any ˆb B there exists x S min such that int(b LS (ˆb)) = int({b B : z(b) = c I x + z C (b A I x )}). Therefore, for any b int(b LS (ˆb)), z( b) = c I x + c C y where y = argmin{c C y : A Cy = b A I x, y R+ n r }. The convexity and continuity of z on int(b LS(ˆb)) follows trivially. Corollary 1 If z is differentiable over N B, then there exist x S I z(b) = c I x + νe (b A Ix ) for all b N. and E E such that Proof. Let an arbitrary ˆb N be given. By Theorem 1, we know that there exists ˆx S min such that z(ˆb) = z(ˆb; ˆx) and A I x B min. Then, we have z(ˆb) = c I x + ν E (ˆb A I x ) with E E and there exists (x, x E, x N ), an optimal solution to the given MILP with right-hand side ˆb, where x E and x N correspond to the basic and non-basic variables in the corresponding solution to the continuous restriction w.r.t. x. It follows that the vector (x, x E + A 1 E (b ˆb), x N ) is a feasible solution for any b N. Now, let another arbitrary point b N be given. We show that (x, x E + A 1 E ( b ˆb), x N ) must be an optimal solution for right-hand side b. Since ˆb N, b N and z is differentiable over N, then ν E is the unique optimal dual solution to the continuous restriction by Proposition 2 and we have z( b) = c I x + c E(x E + A 1 E ( b ˆb)) + c Nx N = z(ˆb) + ν E ( b ˆb) = c I x + ν E (ˆb A I x ) + ν E ( b ˆb) = c I x + ν E ( b A I x ) = z( b; x ). 19

20 Since b and ˆb were arbitrary points in N, the result holds for all such pairs and this ends the proof. It follows from the previous result that if the value function is differentiable over N B, then its gradient at every right-hand side in N is a unique optimal dual solution to the continuous restriction problem w.r.t. some x S I. This generalizes Proposition 2 on the gradient of the function at a differentiable point of the LP value function to the mixed integer case. As an example, in (Ex.4) the gradient of z at any differentiable point is ν = 3 2. Next, we show the second part of Theorem 2 in the following result. Lemma 2 ˆb B ES if and only if for any ɛ > 0, x S I such that z(b) = c I x + z C (b A I x ) for all b N ɛ (ˆb). Proof. ( ) Let ɛ > 0 be given and assume x S I such that z(b) = c I x + z C (b A I x ) for all b N ɛ (ˆb). Now, let b B min be such that ˆb B ES ( b). Then for all b N ɛ (ˆb), we have z(b) = z(b; x ) = z( b) + z C (b b). That is, ˆb int(b LS ( b)). ( ) Let b B min be such that b int(b LS ( b)). Then from Lemma 1, there exists x S I optimal for all b B LS ( b). Next, we arrive at showing the third part of Theorem 2. This is shown in the following result. Lemma 3 Let ˆb B min. Then, int(b LS (ˆb)) is the maximal set of right-hand sides containing ˆb over which the value function is convex and continuous. Proof. Assume the contrary that B LS (ˆb) with ˆb B min is not the maximal set. Then, there exists b in the boundary set w.r.t ˆb, BES (ˆb), and ɛ > 0 such that the value function is continuous and convex at N ɛ ( b). From Theorem 1 and Lemma 2 we have z(b) = min x i S min {c I x i + (b A I x i ) ν i }, b N ɛ ( b), (5.1) where ν i is the optimal dual solution to z C ( b A I x i ) and the set x i S min contains two or more distinct members. Then, z is concave over N ɛ ( b) unless all the polyhedral functions in (5.1) are the same. But then N ɛ ( b) is a subset of B LS (ˆb). So far, in Theorem 2 we have demonstrated that over the local stability set w.r.t a minimal point, the integer part of the solution to the MILP remains constant and the value function of the MILP is a translation of the continuous restriction value function. This can be viewed as a generalization of a similar result that the value function of a PILP with inequality constraints is constant over its local stability sets (z C (b) = 0 for b R m ). These regions are characterized by Schultz et al. (1998). In this case, the members of B min generalize the notion of minimal tenders discussed in (Trapp et al., 2013). Before showing the forth and last part of Theorem 2, we need another lemma on the necessary conditions for the continuity of the value function. Lemma 4 If z C (b) < for all b B, then z is continuous over B. 20

21 Proof. If z C (ˆb) < for some ˆb B, then the continuous restriction w.r.t. the origin and its dual are both feasible and have optimal solution values equal to z C (ˆb). Therefore, z is finite and continuous on B. It can be proved by induction that the minimum of countably many continuous functions defined on B is continuous on B. The continuity of z follows by the representation in (4.1). We now proceed to show the last part of the theorem. Lemmas 5 8 address the relationships between the discontinuity set of the value function with the minimal set of right-hand sides, the boundary set, and the set of non-differentiability points. Combining the following lemmas, the proof of the theorem is complete. Lemma 5 B min B ND. Proof. Assume the value function is differentiable at some ˆb B min. Let z(ˆb) = g. Then, there exists some ɛ > 0, such that z(b) = z(ˆb) + g (b ˆb) for all b N ɛ (ˆb). But then, from the definition of a point of strict local convexity, ˆb cannot be in B SLC and therefore, ˆb / B min. Earlier we showed that the discontinuities of the MILP value function may only happen when it no longer attains its minimum over some translated z and a switch to another translation is required. This is used next to show the relationship between the discontinuity and boundary sets. Lemma 6 B DC B ES. Proof. Assume to the contrary that there exists b B DC but b int(b LS (ˆb)) for some ˆb B min. Then from Theorem 2, there exists ɛ > 0 such that z(b) = z(ˆb) + z C (b ˆb) for all b N ɛ ( b). Therefore, z can only be continuous on N ɛ ( b), which is a contradiction. Lemma 7 If the value function is discontinuous, then B min B DC. Proof. Since B DC, from Lemma 4 we have K = R m. Then, for any ˆb B we have ˆb B ES (b); that is, any right-hand side lies on the boundary of its local stability set. Consider ˆb B min. If z is continuous at ˆb then there exists ɛ > 0 and b B min such that b ˆb and for any b 1 N ɛ (ˆb)\B ES (ˆb) we have z(b 1 ) = z( b) + z C (b 1 b). Consider b 2 N ɛ (ˆb) B ES (ˆb). If z( b) + z C (b 2 b) < z(b 2 ), then z cannot be the value function at b 2. On the other hand, if z( b) + z C (b 2 b) lies above or on z(b 2 ), it can be easily shown that there cannot exist a supporting hyperplane of z at ˆb that lies strictly below z on an arbitrarily small neighborhood of ˆb. Then ˆb cannot be in B min. The next result shows that if ˆb belongs to the boundary set w.r.t a minimal point, then z is non-differentiable at ˆb. Lemma 8 B ES B ND. Proof. Assume there exist some b B ES (ˆb), ˆb B min such that z is differentiable at b. Then there exists ɛ > 0 and E E such that for all b N ɛ ( b) we have z(b) = z( b) + ν E (b b) = z(ˆb) + ν E ( b ˆb) + ν E (b b) = z(ˆb) + ν E (b ˆb) = z(ˆb) + z C (b ˆb). 21 (5.2)

22 Figure 9: Local stability sets and corresponding integer part of solution in (Ex.1). But this contradicts the third part of Theorem 2. We finish this section by applying Theorem 2 to the continuous value function in Example 1 and the discontinuous value function in Example 4. Example 13. Consider the value function (Ex.1). Figure 9 shows the optimal integer parts x 1,..., x 4 of solutions to the corresponding MILP over the local stability sets B LS ( 4), B LS (0), B LS (5) and B LS (6), respectively. One can observe that both the minimal set and the boundary set of the value function are subsets of its set of non-differentiability points. Similarly, in Example 4, x 1 = [1 2], x 2 = [2 3], x 3 = [3 4], x 4 = [0 0], x 5 = [1 1], x 6 = [2 2], x 7 = [3 3] are respectively the integer parts of the solutions for right-hand sides in the local stability sets B LS ( 0.75), B LS ( 0.5),..., B LS (0.5), B LS (0.75). In this case, the value function is discontinuous on the points that belong to the minimal set and we have B min B ES = B ES = B DC = B ND. Remark 1 If z is continuous over B, then S D. This follows from the fact that if S D =, then z(0) = z C (0) = which contradicts z(0) = 0. Therefore, we have that z C (b) > for all b R m. However, we may still have z C (b) = for some b R m. The following is an example. Example 14. The value function defined by (Ex.12) below is continuous on R, although K = R +. z(b) = inf x 1 x 2 s.t. x 1 + x 2 = b x 1 Z +, x 2 R +. (Ex.12) 22

### Chapter 1. Preliminaries

Introduction This dissertation is a reading of chapter 4 in part I of the book : Integer and Combinatorial Optimization by George L. Nemhauser & Laurence A. Wolsey. The chapter elaborates links between

### Bilevel Integer Linear Programming

Bilevel Integer Linear Programming TED RALPHS SCOTT DENEGRE ISE Department COR@L Lab Lehigh University ted@lehigh.edu MOPTA 2009, Lehigh University, 19 August 2009 Thanks: Work supported in part by the

### Duality for Mixed Integer Linear Programs

Duality for Mixed Integer Linear Programs M. GÜZELSOY AND TED K. RALPHS Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, PA 18015 COR@L Technical Report 06T-009-R1 Duality

### Duality for Mixed-Integer Linear Programs

Duality for Mixed-Integer Linear Programs M. Guzelsoy T. K. Ralphs Original May, 006 Revised April, 007 Abstract The theory of duality for linear programs is well-developed and has been successful in advancing

### Asteroide Santana, Santanu S. Dey. December 4, School of Industrial and Systems Engineering, Georgia Institute of Technology

for Some for Asteroide Santana, Santanu S. Dey School of Industrial Systems Engineering, Georgia Institute of Technology December 4, 2016 1 / 38 1 1.1 Conic integer programs for Conic integer programs

### The Value function of a Mixed-Integer Linear Program with a Single Constraint

The Value Function of a Mixed Integer Linear Programs with a Single Constraint MENAL GUZELSOY TED RALPHS ISE Department COR@L Lab Lehigh University tkralphs@lehigh.edu University of Wisconsin February

### Strong Dual for Conic Mixed-Integer Programs

Strong Dual for Conic Mixed-Integer Programs Diego A. Morán R. Santanu S. Dey Juan Pablo Vielma July 14, 011 Abstract Mixed-integer conic programming is a generalization of mixed-integer linear programming.

### Bilevel Integer Optimization: Theory and Algorithms

: Theory and Algorithms Ted Ralphs 1 Joint work with Sahar Tahernajad 1, Scott DeNegre 3, Menal Güzelsoy 2, Anahita Hassanzadeh 4 1 COR@L Lab, Department of Industrial and Systems Engineering, Lehigh University

### 14. Duality. ˆ Upper and lower bounds. ˆ General duality. ˆ Constraint qualifications. ˆ Counterexample. ˆ Complementary slackness.

CS/ECE/ISyE 524 Introduction to Optimization Spring 2016 17 14. Duality ˆ Upper and lower bounds ˆ General duality ˆ Constraint qualifications ˆ Counterexample ˆ Complementary slackness ˆ Examples ˆ Sensitivity

### The Value function of a Mixed-Integer Linear Program with a Single Constraint

The Value Function of a Mixed Integer Linear Programs with a Single Constraint MENAL GUZELSOY TED RALPHS ISE Department COR@L Lab Lehigh University tkralphs@lehigh.edu OPT 008, Atlanta, March 4, 008 Thanks:

### Extreme Abridgment of Boyd and Vandenberghe s Convex Optimization

Extreme Abridgment of Boyd and Vandenberghe s Convex Optimization Compiled by David Rosenberg Abstract Boyd and Vandenberghe s Convex Optimization book is very well-written and a pleasure to read. The

### Linear Programming. Larry Blume Cornell University, IHS Vienna and SFI. Summer 2016

Linear Programming Larry Blume Cornell University, IHS Vienna and SFI Summer 2016 These notes derive basic results in finite-dimensional linear programming using tools of convex analysis. Most sources

### GENERALIZED CONVEXITY AND OPTIMALITY CONDITIONS IN SCALAR AND VECTOR OPTIMIZATION

Chapter 4 GENERALIZED CONVEXITY AND OPTIMALITY CONDITIONS IN SCALAR AND VECTOR OPTIMIZATION Alberto Cambini Department of Statistics and Applied Mathematics University of Pisa, Via Cosmo Ridolfi 10 56124

### Some Properties of Convex Hulls of Integer Points Contained in General Convex Sets

Some Properties of Convex Hulls of Integer Points Contained in General Convex Sets Santanu S. Dey and Diego A. Morán R. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute

### Optimality Conditions for Constrained Optimization

72 CHAPTER 7 Optimality Conditions for Constrained Optimization 1. First Order Conditions In this section we consider first order optimality conditions for the constrained problem P : minimize f 0 (x)

### Convex Optimization M2

Convex Optimization M2 Lecture 3 A. d Aspremont. Convex Optimization M2. 1/49 Duality A. d Aspremont. Convex Optimization M2. 2/49 DMs DM par email: dm.daspremont@gmail.com A. d Aspremont. Convex Optimization

### 3. Linear Programming and Polyhedral Combinatorics

Massachusetts Institute of Technology 18.433: Combinatorial Optimization Michel X. Goemans February 28th, 2013 3. Linear Programming and Polyhedral Combinatorics Summary of what was seen in the introductory

### On Sublinear Inequalities for Mixed Integer Conic Programs

Noname manuscript No. (will be inserted by the editor) On Sublinear Inequalities for Mixed Integer Conic Programs Fatma Kılınç-Karzan Daniel E. Steffy Submitted: December 2014; Revised: July 2015 Abstract

### Integer Programming ISE 418. Lecture 8. Dr. Ted Ralphs

Integer Programming ISE 418 Lecture 8 Dr. Ted Ralphs ISE 418 Lecture 8 1 Reading for This Lecture Wolsey Chapter 2 Nemhauser and Wolsey Sections II.3.1, II.3.6, II.4.1, II.4.2, II.5.4 Duality for Mixed-Integer

### Integer Programming Duality

Integer Programming Duality M. Guzelsoy T. K. Ralphs July, 2010 1 Introduction This article describes what is known about duality for integer programs. It is perhaps surprising that many of the results

### Approximation of Minimal Functions by Extreme Functions

Approximation of Minimal Functions by Extreme Functions Teresa M. Lebair and Amitabh Basu August 14, 2017 Abstract In a recent paper, Basu, Hildebrand, and Molinaro established that the set of continuous

### Shiqian Ma, MAT-258A: Numerical Optimization 1. Chapter 4. Subgradient

Shiqian Ma, MAT-258A: Numerical Optimization 1 Chapter 4 Subgradient Shiqian Ma, MAT-258A: Numerical Optimization 2 4.1. Subgradients definition subgradient calculus duality and optimality conditions Shiqian

### 1 Review of last lecture and introduction

Semidefinite Programming Lecture 10 OR 637 Spring 2008 April 16, 2008 (Wednesday) Instructor: Michael Jeremy Todd Scribe: Yogeshwer (Yogi) Sharma 1 Review of last lecture and introduction Let us first

### Lecture 5. Theorems of Alternatives and Self-Dual Embedding

IE 8534 1 Lecture 5. Theorems of Alternatives and Self-Dual Embedding IE 8534 2 A system of linear equations may not have a solution. It is well known that either Ax = c has a solution, or A T y = 0, c

### Appendix PRELIMINARIES 1. THEOREMS OF ALTERNATIVES FOR SYSTEMS OF LINEAR CONSTRAINTS

Appendix PRELIMINARIES 1. THEOREMS OF ALTERNATIVES FOR SYSTEMS OF LINEAR CONSTRAINTS Here we consider systems of linear constraints, consisting of equations or inequalities or both. A feasible solution

### Mixed-integer nonlinear programming, Conic programming, Duality, Cutting

A STRONG DUAL FOR CONIC MIXED-INTEGER PROGRAMS DIEGO A. MORÁN R., SANTANU S. DEY, AND JUAN PABLO VIELMA Abstract. Mixed-integer conic programming is a generalization of mixed-integer linear programming.

### Optimality, Duality, Complementarity for Constrained Optimization

Optimality, Duality, Complementarity for Constrained Optimization Stephen Wright University of Wisconsin-Madison May 2014 Wright (UW-Madison) Optimality, Duality, Complementarity May 2014 1 / 41 Linear

### Convex Functions and Optimization

Chapter 5 Convex Functions and Optimization 5.1 Convex Functions Our next topic is that of convex functions. Again, we will concentrate on the context of a map f : R n R although the situation can be generalized

### LP Relaxations of Mixed Integer Programs

LP Relaxations of Mixed Integer Programs John E. Mitchell Department of Mathematical Sciences RPI, Troy, NY 12180 USA February 2015 Mitchell LP Relaxations 1 / 29 LP Relaxations LP relaxations We want

### Chapter 2: Preliminaries and elements of convex analysis

Chapter 2: Preliminaries and elements of convex analysis Edoardo Amaldi DEIB Politecnico di Milano edoardo.amaldi@polimi.it Website: http://home.deib.polimi.it/amaldi/opt-16-17.shtml Academic year 2016-17

### Optimality Conditions for Nonsmooth Convex Optimization

Optimality Conditions for Nonsmooth Convex Optimization Sangkyun Lee Oct 22, 2014 Let us consider a convex function f : R n R, where R is the extended real field, R := R {, + }, which is proper (f never

### 5. Duality. Lagrangian

5. Duality Convex Optimization Boyd & Vandenberghe Lagrange dual problem weak and strong duality geometric interpretation optimality conditions perturbation and sensitivity analysis examples generalized

### Generation and Representation of Piecewise Polyhedral Value Functions

Generation and Representation of Piecewise Polyhedral Value Functions Ted Ralphs 1 Joint work with Menal Güzelsoy 2 and Anahita Hassanzadeh 1 1 COR@L Lab, Department of Industrial and Systems Engineering,

### 3. Linear Programming and Polyhedral Combinatorics

Massachusetts Institute of Technology 18.453: Combinatorial Optimization Michel X. Goemans April 5, 2017 3. Linear Programming and Polyhedral Combinatorics Summary of what was seen in the introductory

### Optimization and Optimal Control in Banach Spaces

Optimization and Optimal Control in Banach Spaces Bernhard Schmitzer October 19, 2017 1 Convex non-smooth optimization with proximal operators Remark 1.1 (Motivation). Convex optimization: easier to solve,

### Bilevel Integer Linear Programming

Bilevel Integer Linear Programming SCOTT DENEGRE TED RALPHS ISE Department COR@L Lab Lehigh University ted@lehigh.edu Université Bordeaux, 16 December 2008 Thanks: Work supported in part by the National

### Lagrange duality. The Lagrangian. We consider an optimization program of the form

Lagrange duality Another way to arrive at the KKT conditions, and one which gives us some insight on solving constrained optimization problems, is through the Lagrange dual. The dual is a maximization

### On Sublinear Inequalities for Mixed Integer Conic Programs

Noname manuscript No. (will be inserted by the editor) On Sublinear Inequalities for Mixed Integer Conic Programs Fatma Kılınç-Karzan Daniel E. Steffy Submitted: December 2014; Revised: July 7, 2015 Abstract

### Chapter 2: Preliminaries and elements of convex analysis

Chapter 2: Preliminaries and elements of convex analysis Edoardo Amaldi DEIB Politecnico di Milano edoardo.amaldi@polimi.it Website: http://home.deib.polimi.it/amaldi/opt-14-15.shtml Academic year 2014-15

### MAT-INF4110/MAT-INF9110 Mathematical optimization

MAT-INF4110/MAT-INF9110 Mathematical optimization Geir Dahl August 20, 2013 Convexity Part IV Chapter 4 Representation of convex sets different representations of convex sets, boundary polyhedra and polytopes:

### Two-Term Disjunctions on the Second-Order Cone

Noname manuscript No. (will be inserted by the editor) Two-Term Disjunctions on the Second-Order Cone Fatma Kılınç-Karzan Sercan Yıldız the date of receipt and acceptance should be inserted later Abstract

### Minimal inequalities for an infinite relaxation of integer programs

Minimal inequalities for an infinite relaxation of integer programs Amitabh Basu Carnegie Mellon University, abasu1@andrew.cmu.edu Michele Conforti Università di Padova, conforti@math.unipd.it Gérard Cornuéjols

### LECTURE 15: COMPLETENESS AND CONVEXITY

LECTURE 15: COMPLETENESS AND CONVEXITY 1. The Hopf-Rinow Theorem Recall that a Riemannian manifold (M, g) is called geodesically complete if the maximal defining interval of any geodesic is R. On the other

### A notion of Total Dual Integrality for Convex, Semidefinite and Extended Formulations

A notion of for Convex, Semidefinite and Extended Formulations Marcel de Carli Silva Levent Tunçel April 26, 2018 A vector in R n is integral if each of its components is an integer, A vector in R n is

### On John type ellipsoids

On John type ellipsoids B. Klartag Tel Aviv University Abstract Given an arbitrary convex symmetric body K R n, we construct a natural and non-trivial continuous map u K which associates ellipsoids to

### CONSTRAINT QUALIFICATIONS, LAGRANGIAN DUALITY & SADDLE POINT OPTIMALITY CONDITIONS

CONSTRAINT QUALIFICATIONS, LAGRANGIAN DUALITY & SADDLE POINT OPTIMALITY CONDITIONS A Dissertation Submitted For The Award of the Degree of Master of Philosophy in Mathematics Neelam Patel School of Mathematics

### CHAPTER 2: CONVEX SETS AND CONCAVE FUNCTIONS. W. Erwin Diewert January 31, 2008.

1 ECONOMICS 594: LECTURE NOTES CHAPTER 2: CONVEX SETS AND CONCAVE FUNCTIONS W. Erwin Diewert January 31, 2008. 1. Introduction Many economic problems have the following structure: (i) a linear function

### Lecture: Duality.

Lecture: Duality http://bicmr.pku.edu.cn/~wenzw/opt-2016-fall.html Acknowledgement: this slides is based on Prof. Lieven Vandenberghe s lecture notes Introduction 2/35 Lagrange dual problem weak and strong

### A Geometric Framework for Nonconvex Optimization Duality using Augmented Lagrangian Functions

A Geometric Framework for Nonconvex Optimization Duality using Augmented Lagrangian Functions Angelia Nedić and Asuman Ozdaglar April 15, 2006 Abstract We provide a unifying geometric framework for the

### Duality in Linear Programming

Duality in Linear Programming Gary D. Knott Civilized Software Inc. 1219 Heritage Park Circle Silver Spring MD 296 phone:31-962-3711 email:knott@civilized.com URL:www.civilized.com May 1, 213.1 Duality

### Topology, Math 581, Fall 2017 last updated: November 24, Topology 1, Math 581, Fall 2017: Notes and homework Krzysztof Chris Ciesielski

Topology, Math 581, Fall 2017 last updated: November 24, 2017 1 Topology 1, Math 581, Fall 2017: Notes and homework Krzysztof Chris Ciesielski Class of August 17: Course and syllabus overview. Topology

### A Unified Analysis of Nonconvex Optimization Duality and Penalty Methods with General Augmenting Functions

A Unified Analysis of Nonconvex Optimization Duality and Penalty Methods with General Augmenting Functions Angelia Nedić and Asuman Ozdaglar April 16, 2006 Abstract In this paper, we study a unifying framework

### GEOMETRIC APPROACH TO CONVEX SUBDIFFERENTIAL CALCULUS October 10, Dedicated to Franco Giannessi and Diethard Pallaschke with great respect

GEOMETRIC APPROACH TO CONVEX SUBDIFFERENTIAL CALCULUS October 10, 2018 BORIS S. MORDUKHOVICH 1 and NGUYEN MAU NAM 2 Dedicated to Franco Giannessi and Diethard Pallaschke with great respect Abstract. In

### Constrained Optimization and Lagrangian Duality

CIS 520: Machine Learning Oct 02, 2017 Constrained Optimization and Lagrangian Duality Lecturer: Shivani Agarwal Disclaimer: These notes are designed to be a supplement to the lecture. They may or may

### 1 Maximal Lattice-free Convex Sets

47-831: Advanced Integer Programming Lecturer: Amitabh Basu Lecture 3 Date: 03/23/2010 In this lecture, we explore the connections between lattices of R n and convex sets in R n. The structures will prove

### 2.2 Some Consequences of the Completeness Axiom

60 CHAPTER 2. IMPORTANT PROPERTIES OF R 2.2 Some Consequences of the Completeness Axiom In this section, we use the fact that R is complete to establish some important results. First, we will prove that

### Some Background Math Notes on Limsups, Sets, and Convexity

EE599 STOCHASTIC NETWORK OPTIMIZATION, MICHAEL J. NEELY, FALL 2008 1 Some Background Math Notes on Limsups, Sets, and Convexity I. LIMITS Let f(t) be a real valued function of time. Suppose f(t) converges

### 2. Dual space is essential for the concept of gradient which, in turn, leads to the variational analysis of Lagrange multipliers.

Chapter 3 Duality in Banach Space Modern optimization theory largely centers around the interplay of a normed vector space and its corresponding dual. The notion of duality is important for the following

### Subgradient. Acknowledgement: this slides is based on Prof. Lieven Vandenberghes lecture notes. definition. subgradient calculus

1/41 Subgradient Acknowledgement: this slides is based on Prof. Lieven Vandenberghes lecture notes definition subgradient calculus duality and optimality conditions directional derivative Basic inequality

### Convex Optimization Boyd & Vandenberghe. 5. Duality

5. Duality Convex Optimization Boyd & Vandenberghe Lagrange dual problem weak and strong duality geometric interpretation optimality conditions perturbation and sensitivity analysis examples generalized

### CO 250 Final Exam Guide

Spring 2017 CO 250 Final Exam Guide TABLE OF CONTENTS richardwu.ca CO 250 Final Exam Guide Introduction to Optimization Kanstantsin Pashkovich Spring 2017 University of Waterloo Last Revision: March 4,

### A Parametric Simplex Algorithm for Linear Vector Optimization Problems

A Parametric Simplex Algorithm for Linear Vector Optimization Problems Birgit Rudloff Firdevs Ulus Robert Vanderbei July 28, 2016 Abstract In this paper, a parametric simplex algorithm for solving linear

### Chapter 2 Convex Analysis

Chapter 2 Convex Analysis The theory of nonsmooth analysis is based on convex analysis. Thus, we start this chapter by giving basic concepts and results of convexity (for further readings see also [202,

### Jørgen Tind, Department of Statistics and Operations Research, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen O, Denmark.

DUALITY THEORY Jørgen Tind, Department of Statistics and Operations Research, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen O, Denmark. Keywords: Duality, Saddle point, Complementary

### Symmetric and Asymmetric Duality

journal of mathematical analysis and applications 220, 125 131 (1998) article no. AY975824 Symmetric and Asymmetric Duality Massimo Pappalardo Department of Mathematics, Via Buonarroti 2, 56127, Pisa,

### Part III. 10 Topological Space Basics. Topological Spaces

Part III 10 Topological Space Basics Topological Spaces Using the metric space results above as motivation we will axiomatize the notion of being an open set to more general settings. Definition 10.1.

### Lecture 9 Monotone VIs/CPs Properties of cones and some existence results. October 6, 2008

Lecture 9 Monotone VIs/CPs Properties of cones and some existence results October 6, 2008 Outline Properties of cones Existence results for monotone CPs/VIs Polyhedrality of solution sets Game theory:

### A Brief Review on Convex Optimization

A Brief Review on Convex Optimization 1 Convex set S R n is convex if x,y S, λ,µ 0, λ+µ = 1 λx+µy S geometrically: x,y S line segment through x,y S examples (one convex, two nonconvex sets): A Brief Review

### Summer School: Semidefinite Optimization

Summer School: Semidefinite Optimization Christine Bachoc Université Bordeaux I, IMB Research Training Group Experimental and Constructive Algebra Haus Karrenberg, Sept. 3 - Sept. 7, 2012 Duality Theory

### Introduction to Mathematical Programming IE406. Lecture 10. Dr. Ted Ralphs

Introduction to Mathematical Programming IE406 Lecture 10 Dr. Ted Ralphs IE406 Lecture 10 1 Reading for This Lecture Bertsimas 4.1-4.3 IE406 Lecture 10 2 Duality Theory: Motivation Consider the following

### A Benders Algorithm for Two-Stage Stochastic Optimization Problems With Mixed Integer Recourse

A Benders Algorithm for Two-Stage Stochastic Optimization Problems With Mixed Integer Recourse Ted Ralphs 1 Joint work with Menal Güzelsoy 2 and Anahita Hassanzadeh 1 1 COR@L Lab, Department of Industrial

### In English, this means that if we travel on a straight line between any two points in C, then we never leave C.

Convex sets In this section, we will be introduced to some of the mathematical fundamentals of convex sets. In order to motivate some of the definitions, we will look at the closest point problem from

### Week 3: Faces of convex sets

Week 3: Faces of convex sets Conic Optimisation MATH515 Semester 018 Vera Roshchina School of Mathematics and Statistics, UNSW August 9, 018 Contents 1. Faces of convex sets 1. Minkowski theorem 3 3. Minimal

### A Parametric Simplex Algorithm for Linear Vector Optimization Problems

A Parametric Simplex Algorithm for Linear Vector Optimization Problems Birgit Rudloff Firdevs Ulus Robert Vanderbei July 9, 2015 Abstract In this paper, a parametric simplex algorithm for solving linear

### Unbounded Convex Semialgebraic Sets as Spectrahedral Shadows

Unbounded Convex Semialgebraic Sets as Spectrahedral Shadows Shaowei Lin 9 Dec 2010 Abstract Recently, Helton and Nie [3] showed that a compact convex semialgebraic set S is a spectrahedral shadow if the

### 3.10 Lagrangian relaxation

3.10 Lagrangian relaxation Consider a generic ILP problem min {c t x : Ax b, Dx d, x Z n } with integer coefficients. Suppose Dx d are the complicating constraints. Often the linear relaxation and the

### Split Rank of Triangle and Quadrilateral Inequalities

Split Rank of Triangle and Quadrilateral Inequalities Santanu Dey 1 Quentin Louveaux 2 June 4, 2009 Abstract A simple relaxation of two rows of a simplex tableau is a mixed integer set consisting of two

### CHAPTER 6. Differentiation

CHPTER 6 Differentiation The generalization from elementary calculus of differentiation in measure theory is less obvious than that of integration, and the methods of treating it are somewhat involved.

### Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation

Course Notes for EE227C (Spring 2018): Convex Optimization and Approximation Instructor: Moritz Hardt Email: hardt+ee227c@berkeley.edu Graduate Instructor: Max Simchowitz Email: msimchow+ee227c@berkeley.edu

### On the Relative Strength of Split, Triangle and Quadrilateral Cuts

On the Relative Strength of Split, Triangle and Quadrilateral Cuts Amitabh Basu Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 53 abasu@andrew.cmu.edu Pierre Bonami LIF, Faculté

### Mathematics 530. Practice Problems. n + 1 }

Department of Mathematical Sciences University of Delaware Prof. T. Angell October 19, 2015 Mathematics 530 Practice Problems 1. Recall that an indifference relation on a partially ordered set is defined

### Key words. Integer nonlinear programming, Cutting planes, Maximal lattice-free convex sets

ON MAXIMAL S-FREE CONVEX SETS DIEGO A. MORÁN R. AND SANTANU S. DEY Abstract. Let S Z n satisfy the property that conv(s) Z n = S. Then a convex set K is called an S-free convex set if int(k) S =. A maximal

### CSCI : Optimization and Control of Networks. Review on Convex Optimization

CSCI7000-016: Optimization and Control of Networks Review on Convex Optimization 1 Convex set S R n is convex if x,y S, λ,µ 0, λ+µ = 1 λx+µy S geometrically: x,y S line segment through x,y S examples (one

### Lecture 7: Semidefinite programming

CS 766/QIC 820 Theory of Quantum Information (Fall 2011) Lecture 7: Semidefinite programming This lecture is on semidefinite programming, which is a powerful technique from both an analytic and computational

### Constrained optimization

Constrained optimization DS-GA 1013 / MATH-GA 2824 Optimization-based Data Analysis http://www.cims.nyu.edu/~cfgranda/pages/obda_fall17/index.html Carlos Fernandez-Granda Compressed sensing Convex constrained

Noname manuscript No. (will be inserted by the editor) Convex hull of two quadratic or a conic quadratic and a quadratic inequality Sina Modaresi Juan Pablo Vielma the date of receipt and acceptance should

### Minimizing Cubic and Homogeneous Polynomials over Integers in the Plane

Minimizing Cubic and Homogeneous Polynomials over Integers in the Plane Alberto Del Pia Department of Industrial and Systems Engineering & Wisconsin Institutes for Discovery, University of Wisconsin-Madison

### 4TE3/6TE3. Algorithms for. Continuous Optimization

4TE3/6TE3 Algorithms for Continuous Optimization (Duality in Nonlinear Optimization ) Tamás TERLAKY Computing and Software McMaster University Hamilton, January 2004 terlaky@mcmaster.ca Tel: 27780 Optimality

### BASICS OF CONVEX ANALYSIS

BASICS OF CONVEX ANALYSIS MARKUS GRASMAIR 1. Main Definitions We start with providing the central definitions of convex functions and convex sets. Definition 1. A function f : R n R + } is called convex,

### On the Value Function of a Mixed Integer Linear Program

On the Value Function of a Mixed Integer Linear Program MENAL GUZELSOY TED RALPHS ISE Department COR@L Lab Lehigh University ted@lehigh.edu AIRO, Ischia, Italy, 11 September 008 Thanks: Work supported

### New Class of duality models in discrete minmax fractional programming based on second-order univexities

STATISTICS, OPTIMIZATION AND INFORMATION COMPUTING Stat., Optim. Inf. Comput., Vol. 5, September 017, pp 6 77. Published online in International Academic Press www.iapress.org) New Class of duality models

### Lecture 5. The Dual Cone and Dual Problem

IE 8534 1 Lecture 5. The Dual Cone and Dual Problem IE 8534 2 For a convex cone K, its dual cone is defined as K = {y x, y 0, x K}. The inner-product can be replaced by x T y if the coordinates of the

### Division of the Humanities and Social Sciences. Supergradients. KC Border Fall 2001 v ::15.45

Division of the Humanities and Social Sciences Supergradients KC Border Fall 2001 1 The supergradient of a concave function There is a useful way to characterize the concavity of differentiable functions.

### CHAPTER 7. Connectedness

CHAPTER 7 Connectedness 7.1. Connected topological spaces Definition 7.1. A topological space (X, T X ) is said to be connected if there is no continuous surjection f : X {0, 1} where the two point set

### Real Analysis Notes. Thomas Goller

Real Analysis Notes Thomas Goller September 4, 2011 Contents 1 Abstract Measure Spaces 2 1.1 Basic Definitions........................... 2 1.2 Measurable Functions........................ 2 1.3 Integration..............................

### ON A CLASS OF NONSMOOTH COMPOSITE FUNCTIONS

MATHEMATICS OF OPERATIONS RESEARCH Vol. 28, No. 4, November 2003, pp. 677 692 Printed in U.S.A. ON A CLASS OF NONSMOOTH COMPOSITE FUNCTIONS ALEXANDER SHAPIRO We discuss in this paper a class of nonsmooth

### On smoothness properties of optimal value functions at the boundary of their domain under complete convexity

On smoothness properties of optimal value functions at the boundary of their domain under complete convexity Oliver Stein # Nathan Sudermann-Merx June 14, 2013 Abstract This article studies continuity

### Near-Potential Games: Geometry and Dynamics

Near-Potential Games: Geometry and Dynamics Ozan Candogan, Asuman Ozdaglar and Pablo A. Parrilo September 6, 2011 Abstract Potential games are a special class of games for which many adaptive user dynamics