# Nonpivot and Implicit Projected Dynamical Systems on Hilbert Spaces

Authors: Monica Gabriela Cojocaru; Stephane Pia
Journal: Journal of Function Spaces and Applications
 (2012)

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## Abstract

This paper presents a generalization of the concept and uses of projected dynamical
systems to the case of nonpivot Hilbert spaces. These are Hilbert spaces in
which the topological dual space is not identified with the base space. The generalization
consists of showing the existence of such systems and their relation to
variational problems, such as variational inequalities. In the case of usual Hilbert
spaces these systems have been extensively studied, and, as in previous works, this
new generalization has been motivated by applications, as shown below.


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## Body

## 1. Introduction

In this paper we study the existence of solutions for a class of differential equations with discontinuous and nonlinear right-hand side on the class of nonpivot Hilbert spaces. This class of equations (called projected differential equations) was first introduced (in the form we use) in [1]; however have other studies of a similar formulation has been known since [2–4]. The formulation of the flow of such equations as dynamical systems in ℝn is due to [1, 5], and it has been applied to study the dynamics of solutions of finite-dimensional variational inequalities in [5, 6].Finite-dimensional variational inequalities theory provides solutions to a wide class of equilibrium problems in mathematical economics, optimization, management science, operations research, finance, and so forth (see, e.g., [4, 6–8] and the references therein). Therefore there has been a steady interest over the years in studying the stability of solutions to finite-dimensional variational inequalities (and consequently the stability of equilibria for various problems). In general, such a study is done by associating a projected dynamical system to a variational inequality problem; however in the past few years the applied problems, as well as the theoretical results, have progressed to a qualitative study of stability of solutions to variational inequality problems on Hilbert spaces and even on Banach spaces. Examples of the kind of variational problems (and their applications) can be found in see [9–19] and the references therein).In this paper we present a new step in this study: we show that a projected differential equation has solutions on a non-pivot Hilbert space of any dimension. We prove the existence and uniqueness of integral curves and show they remain in a given constraint set of interest. As in the finite-dimensional case, a dynamics given by solutions to a projected differential equation is interesting because it describes these problems as dynamical systems. Moreover, as shown in this paper, the new results were needed to be developed for the study of the weighted traffic equilibrium problem (see [20]). Our goal in this paper is to present the mathematical techniques involved in proving the existence of solutions to projected differential equations in a non-pivot setting, which is in fact similar to the one in [21], but adapted to a non-pivot space; in addition, there are a number of preliminary results needed prior to obtaining our main result, which are remarkable since they also hold in a larger setting, namely, that of a reflexive Banach space (see the results in [22, 23]). Last but not least, we also present a projected system formulation called implicit. These kinds of systems have been introduced in the literature in [24], but without any existence result being presented in their case. We thus solve this additional problem in this paper as well.


## 2. Background Material

In this section we present several definitions and results pertinent to the reader and considered essential for the presentation of the later material.


### 2.1. Dual Realization of a Hilbert Space

Each time we work with a Hilbert spaceV, it is necessary to decide whether or not we identify the topological dual space V*=ℒ(V,ℝ) with V. Commonly this identification is made, one of the reasons for this being that the vectors of the polar of a set of V are in V. In some cases the identification does not make sense. For clarity of presentation, we remind below of the basic results regarding the dual realization of a Hilbert space. The readers can refer to [25] for additional information.First, consider a pre-Hilbert spaceV with an inner product ((x,y)), and its topological dual V*=ℒ(V,ℝ). It is well known that V* is a Banach space for the classical dual norm (∥f∥*=supx∈V(|f(x)|/∥x∥)). It is also known that there exists an isometry J:V→V* such that J is linear and for all x∈V, J(x)=grad(∥x∥2/2). This mapping J is called a duality mapping of (V,V*).Theorem 2.1 (Theorem  1 page 68, [25]).
LetV be a Hilbert space with the inner product ((x,y)) and J∈ℒ(V,V*) the duality mapping above. Then J is a surjective isometry from V to V*. The dual space V* is a Hilbert space with the inner product:
(2.1)((f,g))*=((J-1f,J-1g))=f(J-1g).Theorem 2.2 (Theorem  2 page 69, [25]).
Let V be a pre-Hilbert space. Then there exists a completionV̂ of V, that is, an isometry j from V to the Hilbert space V̂ such that j(V) is dense in V̂.Definition 2.3.
LetV be a Hilbert space. We call {F,j}, where(i)
F is a Hilbert space,(ii)
j is an isometry from F to ℒ(V,ℝ), a dual realization of V. We then set
(2.2)〈f,x〉=j∘f(x),∀f∈F,∀x∈V,
where 〈f,x〉 is the duality pairing for F×V.Remark 2.4.
The duality pairing is a nondegenerate bilinear form onF×V and ∥f∥F=supx∈V(|〈f,x〉|/∥x∥). These properties permit us to prove that F is isomorphic to V*.We deduce from Theorems2.1 and 2.2 that k=j-1∘J∈ℒ(V,F) is a surjective isometry such that(2.3)(x,y)=〈k(x),y〉.
We use the following convention here: when a dual realization {F,j} of a space has been chosen, we set F=V* and j∘f(x)=〈f,x〉. We say that the isometry k:V→V* is the duality operator associated to the inner product on V and to the duality pairing on V*×V by the relation(2.4)(x,y)=〈k(x),y〉.
A special but most frequent case is to choose a dual realization of V the couple {V,J}; in this case the Hilbert space V is called a pivot space. To be more precise, we introduce the following definition.Definition 2.5.
A Hilbert spaceH with an inner product (x,y) is called a pivot space, if we identify H* with H. In that case
(2.5)H*=H,j=J,〈x,y〉=(x,y).Sometimes it does not make sense to identify the space itself with its topological dual, as the following example shows.Let us considerV=L2(ℝ,(1+|x|))⊂L2(ℝ) (dense subspace of L2(ℝ)) endowed with the inner product:(2.6)(u,v)V=∫R(1+|x|)u(x)v(x)dx.An element φ∈L2(ℝ)* is also an element of V*. If we identify φ to an element f∈L2(ℝ), this function does not define a linear form on V, and the expression φ(v)=〈f,v〉V has no meaning on V. In this situation it is necessary to work in a non-pivot Hilbert space. We provide now some useful examples of non-pivot H-spaces.LetΩ⊂ℝn be an open subset of, a:Ω→R+∖{0}, a continuous and strictly positive function called “weight” and s:Ω→R+∖{0}, a continuous and strictly positive function called “real time density.” The bilinear form defined on 𝒞0(Ω) (continuous functions with compact support on Ω) by(2.7)(x,y)a,s=∫Ωx(ω)y(ω)a(ω)s(ω)dω
is an inner product. We remark here that if a is a weight, then a-1=1/a is also a weight. Let us introduce the following.Definition 2.6.
We callL2(Ω,a,s) a completion of 𝒞0(Ω) for the inner product 〈x,y〉a,s.We now introduce ann-dimensional version of the previous space. If we denote by Vi=L2(Ω,ℝ,ai,si) and Vi*=L2(Ω,ℝ,ai-1,si), the space(2.8)V=∏i=1mVi
is a non-pivot Hilbert space with the inner product:(2.9)(F,G)V=(F,G)a,s=∑i=1m∫ΩFi(ω)Gi(ω)ai(ω)si(ω)dω.
The space(2.10)V*=∏i=1mVi*
is clearly a non-pivot Hilbert space for the following inner product(2.11)(F,G)V*=(F,G)a-1,s=∑i=1m∫ΩFi(ω)Gi(ω)si(ω)ai(ω)dω,
and the following bilinear form(2.12)V*×V⟶R,〈f,x〉V*×V=〈f,x〉s=∑i=1m∫Ωfi(ω)xi(ω)si(ω)dω
defines a duality between V and V*. More precisely we have the following (see [20] for a proof).Proposition 2.7.
The bilinear form (2.12) defines a duality mapping between V*×V, given by
(2.13)J(F)=(a1F1,…,amFm).For applications of these spaces, the reader can refer to [20].


### 2.2. Variational Analysis in Non-Pivot H-Spaces

LetX be a Hilbert space of arbitrary (finite or infinite) dimension and let K⊂X be a nonempty, closed, convex subset. We assume the reader is familiar with tangent and normal cones toKatx∈K (TK(x), respectively, NK(x)), and with the projection operator ofXontoK,PK:X→K given by ∥PK(z)-z∥=infx∈K∥x-z∥. Moreover we use here the following characterization of PK(x):(2.14)x¯=PK(x)⟺〈J(x-x¯),y-x¯〉≤0,∀y∈K.
The properties of the projection operator on Hilbert and Banach spaces are well known (see e.g., [26–28]). The directional derivative of the operator PK is defined, for any x∈K and any element v∈X, as the limit (for a proof see [26]):(2.15)πK(x,v):=lim⁡δ→0+PK(x+δv)-xδ;moreoverπK(x,v)=PTK(x)(v).
Let πK:K×X→X be the operator given by (x,v)↦πK(x,v). Note that πK is nonlinear and discontinuous on the boundary of the set K. In [1, 29] several characterizations of πK are given.The following theorem has been proven in the framework of reflexive strictly convex and smooth Banach spaces. We will use it to obtain a decomposition theorem in non-pivot Hilbert spaces (for a proof see [30, Th. 2.4]).Theorem 2.8.
LetX be a real reflexive strictly convex and smooth Banach space, and let C be a non-empty, closed and convex cone of X. Then for all x∈X and for all f∈X* the following decompositions hold:
(2.16)x=PC(x)+J-1ΠC0J(x),〈ΠC0J(x),PC(x)〉=0,f=PC0(f)+JΠCJ-1(f),〈PC0(f),ΠCJ-1(f)〉=0.
Here PC is the metric projection operator on K, and ΠC0 is the generalized projection operator on C0 (for a definition of ΠC0 see [28]).Remark 2.9.
It is known thatPC and ΠC coincide whenever the cone C belongs to a Hilbert space. This observation implies the following result.Corollary 2.10.
LetC be a nonempty closed convex cone of a non-pivot Hilbert space X. Then for all x∈X and f∈X* the following decompositions hold:
(2.17)x=PC(x)+J-1PC0J(x),〈PC0J(x),PC(x)〉=0,f=PC0(f)+JPCJ-1(f),〈PC0(f),PCJ-1(f)〉=0.We highlight that Zarantonello has shown in [27] a similar decomposition result in reflexive Banach spaces.Lemma 2.11 ([26, Lemma  4.5]).
For any closed convex setK,
(2.18)PK(x+h)=x+h+∘(‖h‖),x∈K,h∈TK(x),where ∘(∥h∥)/∥h∥→0 as h→0 over any locally compact cone of increments.Remark 2.12.
To prove Lemma2.11 only the properties of the norm in Hilbert spaces are used; therefore the proof is valid in the non-pivot setting.The following lemma has been proven in the pivot case in [26]. We give below a similar proof in non-pivot spaces.Lemma 2.13.
For anyx∈K,
(2.19)PK(x+h)=x+PTK(x)(h)+∘(‖h‖),
where ∘(∥h∥)/∥h∥→0 as h→0 over any locally compact cone of increments.Proof.
Clearly, we have in general that(2.20)‖a+b‖2=‖a‖2+‖b‖2+2(a,b).Taking
(2.21)a:=x+h-Px+TK(x)(x+h),b:=Px+TK(x)(x+h)-PK(x+h),we get
(2.22)‖x+h-PK(x+h)‖2=‖x+h-Px+TK(x)(x+h)‖2+‖Px+TK(x)(x+h)-PK(x+h)‖2+2(x+h-Px+TK(x)(x+h),Px+TK(x)(x+h)-PK(x+h)),
but
(2.23)(x+h-Px+TK(x)(x+h),Px+TK(x)(x+h)-PK(x+h))=〈J(x+h-Px+TK(x)(x+h)),Px+TK(x)(x+h)-PK(x+h)〉≥0
using the variational principle (2.14) applied to Px+TK(x)(x+h). By definition of the projection operator we have
(2.24)‖x+h-PK(x+h)‖2≤‖x+h-PK[Px+TK(x)(x+h)]‖2.Therefore we have
(2.25)‖x+h-Px+TK(x)(x+h)‖2+‖Px+TK(x)(x+h)-PK(x+h)‖2≤‖x+h-PK[Px+TK(x)(x+h)]‖2.As Px+TK(x)(x+h)=x+PTK(x)(h) (just apply the definition and the variational principle (2.14)), we have
(2.26)‖h-PTK(x)(h)‖2+‖x+PTK(x)(h)-PK(x+h)‖2≤‖x+h-PK(x+PTK(x)(h))‖2,but using the Corollary 2.10 we have h=PTC(x)(h)+J-1PNK(x)(J(h)), and therefore,
(2.27)‖PK(x+h)-x-PTK(x)(h)‖2≤‖J-1PNK(x)(J(h))+x+PTK(x)(h)-PK(x+PTK(x)(h))‖2-‖J-1PNK(x)(J(h))‖2≤‖x+PTK(x)(h)-PK(x+PTK(x)(h))‖2+2‖J-1PNK(x)(J(h))‖‖x+PTK(x)(h)-PK(x+PTK(x)(h))‖.But by Lemma 2.11, x+PTK(x)(h)-PK(x+PTK(x)(h))=o(∥PTK(x)(h)∥), so we can write
(2.28)‖PK(x+h)-x-PTK(x)(h)‖2≤(2‖J-1PNK(x)(J(h))‖+o(‖PTK(x)(h)‖))o(‖PTK(x)(h)‖).Therefore we have,
(2.29)‖PK(x+h)-x-PTK(x)(h)‖2≤o(‖h‖)2.


## 2.1. Dual Realization of a Hilbert Space

Each time we work with a Hilbert spaceV, it is necessary to decide whether or not we identify the topological dual space V*=ℒ(V,ℝ) with V. Commonly this identification is made, one of the reasons for this being that the vectors of the polar of a set of V are in V. In some cases the identification does not make sense. For clarity of presentation, we remind below of the basic results regarding the dual realization of a Hilbert space. The readers can refer to [25] for additional information.First, consider a pre-Hilbert spaceV with an inner product ((x,y)), and its topological dual V*=ℒ(V,ℝ). It is well known that V* is a Banach space for the classical dual norm (∥f∥*=supx∈V(|f(x)|/∥x∥)). It is also known that there exists an isometry J:V→V* such that J is linear and for all x∈V, J(x)=grad(∥x∥2/2). This mapping J is called a duality mapping of (V,V*).Theorem 2.1 (Theorem  1 page 68, [25]).
LetV be a Hilbert space with the inner product ((x,y)) and J∈ℒ(V,V*) the duality mapping above. Then J is a surjective isometry from V to V*. The dual space V* is a Hilbert space with the inner product:
(2.1)((f,g))*=((J-1f,J-1g))=f(J-1g).Theorem 2.2 (Theorem  2 page 69, [25]).
Let V be a pre-Hilbert space. Then there exists a completionV̂ of V, that is, an isometry j from V to the Hilbert space V̂ such that j(V) is dense in V̂.Definition 2.3.
LetV be a Hilbert space. We call {F,j}, where(i)
F is a Hilbert space,(ii)
j is an isometry from F to ℒ(V,ℝ), a dual realization of V. We then set
(2.2)〈f,x〉=j∘f(x),∀f∈F,∀x∈V,
where 〈f,x〉 is the duality pairing for F×V.Remark 2.4.
The duality pairing is a nondegenerate bilinear form onF×V and ∥f∥F=supx∈V(|〈f,x〉|/∥x∥). These properties permit us to prove that F is isomorphic to V*.We deduce from Theorems2.1 and 2.2 that k=j-1∘J∈ℒ(V,F) is a surjective isometry such that(2.3)(x,y)=〈k(x),y〉.
We use the following convention here: when a dual realization {F,j} of a space has been chosen, we set F=V* and j∘f(x)=〈f,x〉. We say that the isometry k:V→V* is the duality operator associated to the inner product on V and to the duality pairing on V*×V by the relation(2.4)(x,y)=〈k(x),y〉.
A special but most frequent case is to choose a dual realization of V the couple {V,J}; in this case the Hilbert space V is called a pivot space. To be more precise, we introduce the following definition.Definition 2.5.
A Hilbert spaceH with an inner product (x,y) is called a pivot space, if we identify H* with H. In that case
(2.5)H*=H,j=J,〈x,y〉=(x,y).Sometimes it does not make sense to identify the space itself with its topological dual, as the following example shows.Let us considerV=L2(ℝ,(1+|x|))⊂L2(ℝ) (dense subspace of L2(ℝ)) endowed with the inner product:(2.6)(u,v)V=∫R(1+|x|)u(x)v(x)dx.An element φ∈L2(ℝ)* is also an element of V*. If we identify φ to an element f∈L2(ℝ), this function does not define a linear form on V, and the expression φ(v)=〈f,v〉V has no meaning on V. In this situation it is necessary to work in a non-pivot Hilbert space. We provide now some useful examples of non-pivot H-spaces.LetΩ⊂ℝn be an open subset of, a:Ω→R+∖{0}, a continuous and strictly positive function called “weight” and s:Ω→R+∖{0}, a continuous and strictly positive function called “real time density.” The bilinear form defined on 𝒞0(Ω) (continuous functions with compact support on Ω) by(2.7)(x,y)a,s=∫Ωx(ω)y(ω)a(ω)s(ω)dω
is an inner product. We remark here that if a is a weight, then a-1=1/a is also a weight. Let us introduce the following.Definition 2.6.
We callL2(Ω,a,s) a completion of 𝒞0(Ω) for the inner product 〈x,y〉a,s.We now introduce ann-dimensional version of the previous space. If we denote by Vi=L2(Ω,ℝ,ai,si) and Vi*=L2(Ω,ℝ,ai-1,si), the space(2.8)V=∏i=1mVi
is a non-pivot Hilbert space with the inner product:(2.9)(F,G)V=(F,G)a,s=∑i=1m∫ΩFi(ω)Gi(ω)ai(ω)si(ω)dω.
The space(2.10)V*=∏i=1mVi*
is clearly a non-pivot Hilbert space for the following inner product(2.11)(F,G)V*=(F,G)a-1,s=∑i=1m∫ΩFi(ω)Gi(ω)si(ω)ai(ω)dω,
and the following bilinear form(2.12)V*×V⟶R,〈f,x〉V*×V=〈f,x〉s=∑i=1m∫Ωfi(ω)xi(ω)si(ω)dω
defines a duality between V and V*. More precisely we have the following (see [20] for a proof).Proposition 2.7.
The bilinear form (2.12) defines a duality mapping between V*×V, given by
(2.13)J(F)=(a1F1,…,amFm).For applications of these spaces, the reader can refer to [20].


## 2.2. Variational Analysis in Non-Pivot H-Spaces

LetX be a Hilbert space of arbitrary (finite or infinite) dimension and let K⊂X be a nonempty, closed, convex subset. We assume the reader is familiar with tangent and normal cones toKatx∈K (TK(x), respectively, NK(x)), and with the projection operator ofXontoK,PK:X→K given by ∥PK(z)-z∥=infx∈K∥x-z∥. Moreover we use here the following characterization of PK(x):(2.14)x¯=PK(x)⟺〈J(x-x¯),y-x¯〉≤0,∀y∈K.
The properties of the projection operator on Hilbert and Banach spaces are well known (see e.g., [26–28]). The directional derivative of the operator PK is defined, for any x∈K and any element v∈X, as the limit (for a proof see [26]):(2.15)πK(x,v):=lim⁡δ→0+PK(x+δv)-xδ;moreoverπK(x,v)=PTK(x)(v).
Let πK:K×X→X be the operator given by (x,v)↦πK(x,v). Note that πK is nonlinear and discontinuous on the boundary of the set K. In [1, 29] several characterizations of πK are given.The following theorem has been proven in the framework of reflexive strictly convex and smooth Banach spaces. We will use it to obtain a decomposition theorem in non-pivot Hilbert spaces (for a proof see [30, Th. 2.4]).Theorem 2.8.
LetX be a real reflexive strictly convex and smooth Banach space, and let C be a non-empty, closed and convex cone of X. Then for all x∈X and for all f∈X* the following decompositions hold:
(2.16)x=PC(x)+J-1ΠC0J(x),〈ΠC0J(x),PC(x)〉=0,f=PC0(f)+JΠCJ-1(f),〈PC0(f),ΠCJ-1(f)〉=0.
Here PC is the metric projection operator on K, and ΠC0 is the generalized projection operator on C0 (for a definition of ΠC0 see [28]).Remark 2.9.
It is known thatPC and ΠC coincide whenever the cone C belongs to a Hilbert space. This observation implies the following result.Corollary 2.10.
LetC be a nonempty closed convex cone of a non-pivot Hilbert space X. Then for all x∈X and f∈X* the following decompositions hold:
(2.17)x=PC(x)+J-1PC0J(x),〈PC0J(x),PC(x)〉=0,f=PC0(f)+JPCJ-1(f),〈PC0(f),PCJ-1(f)〉=0.We highlight that Zarantonello has shown in [27] a similar decomposition result in reflexive Banach spaces.Lemma 2.11 ([26, Lemma  4.5]).
For any closed convex setK,
(2.18)PK(x+h)=x+h+∘(‖h‖),x∈K,h∈TK(x),where ∘(∥h∥)/∥h∥→0 as h→0 over any locally compact cone of increments.Remark 2.12.
To prove Lemma2.11 only the properties of the norm in Hilbert spaces are used; therefore the proof is valid in the non-pivot setting.The following lemma has been proven in the pivot case in [26]. We give below a similar proof in non-pivot spaces.Lemma 2.13.
For anyx∈K,
(2.19)PK(x+h)=x+PTK(x)(h)+∘(‖h‖),
where ∘(∥h∥)/∥h∥→0 as h→0 over any locally compact cone of increments.Proof.
Clearly, we have in general that(2.20)‖a+b‖2=‖a‖2+‖b‖2+2(a,b).Taking
(2.21)a:=x+h-Px+TK(x)(x+h),b:=Px+TK(x)(x+h)-PK(x+h),we get
(2.22)‖x+h-PK(x+h)‖2=‖x+h-Px+TK(x)(x+h)‖2+‖Px+TK(x)(x+h)-PK(x+h)‖2+2(x+h-Px+TK(x)(x+h),Px+TK(x)(x+h)-PK(x+h)),
but
(2.23)(x+h-Px+TK(x)(x+h),Px+TK(x)(x+h)-PK(x+h))=〈J(x+h-Px+TK(x)(x+h)),Px+TK(x)(x+h)-PK(x+h)〉≥0
using the variational principle (2.14) applied to Px+TK(x)(x+h). By definition of the projection operator we have
(2.24)‖x+h-PK(x+h)‖2≤‖x+h-PK[Px+TK(x)(x+h)]‖2.Therefore we have
(2.25)‖x+h-Px+TK(x)(x+h)‖2+‖Px+TK(x)(x+h)-PK(x+h)‖2≤‖x+h-PK[Px+TK(x)(x+h)]‖2.As Px+TK(x)(x+h)=x+PTK(x)(h) (just apply the definition and the variational principle (2.14)), we have
(2.26)‖h-PTK(x)(h)‖2+‖x+PTK(x)(h)-PK(x+h)‖2≤‖x+h-PK(x+PTK(x)(h))‖2,but using the Corollary 2.10 we have h=PTC(x)(h)+J-1PNK(x)(J(h)), and therefore,
(2.27)‖PK(x+h)-x-PTK(x)(h)‖2≤‖J-1PNK(x)(J(h))+x+PTK(x)(h)-PK(x+PTK(x)(h))‖2-‖J-1PNK(x)(J(h))‖2≤‖x+PTK(x)(h)-PK(x+PTK(x)(h))‖2+2‖J-1PNK(x)(J(h))‖‖x+PTK(x)(h)-PK(x+PTK(x)(h))‖.But by Lemma 2.11, x+PTK(x)(h)-PK(x+PTK(x)(h))=o(∥PTK(x)(h)∥), so we can write
(2.28)‖PK(x+h)-x-PTK(x)(h)‖2≤(2‖J-1PNK(x)(J(h))‖+o(‖PTK(x)(h)‖))o(‖PTK(x)(h)‖).Therefore we have,
(2.29)‖PK(x+h)-x-PTK(x)(h)‖2≤o(‖h‖)2.


## 3. Non-Pivot and Implicit PDS in Hilbert Spaces

### 3.1. PDS in Pivot H-Spaces

LetX be a pivot Hilbert space of arbitrary (finite or infinite) dimension and let K⊂X be a nonempty, closed, convex subset. The following result has been shown (see [21]).Theorem 3.1.
LetX be a Hilbert space and let K be a nonempty, closed, convex subset. Let F:K→X be a Lipschitz continuous vector field and let x0∈K. Then the initial value problem associated to the projected differential equation (PrDE)
(3.1)dx(τ)dτ=πK(x(τ)),-F(x(τ)),x(0)=x0∈K
has a unique absolutely continuous solution on the interval [0,∞).This result is a generalization of the one in [6], where X:=ℝn, K was a convex polyhedron and F had linear growth.Definition 3.2.
Aprojected dynamical system then is given by a mapping ϕ:ℝ+×K→K which solves the initial value problem: ϕ̇(t,x)=πK(ϕ(t,x),-F(ϕ(t,x)))a.a.t, ϕ(0,x)=x0∈K.


### 3.2. PDS in Non-Pivot H-Spaces

In this subsection we show that, with minor modifications, the existence of PDS in non-pivot H-spaces can be obtained. We first introduce non-pivot projected dynamical systems (NpPDSs) and then show their existence. In analogy with [21] we first introduce the following.Definition 3.3.
ForF:K→X*, a non-pivot projected differential equation (NpPrDE) is a discontinuous ODE given by
(3.2)dx(t)dt=πK(x(t),-(J-1∘F)(x(t)))=PTK(x(t))(-(J-1∘F)(x(t))).Consequently the associated Cauchy problem is given by(3.3)dx(t)dt=πK(x(t),-(J-1∘F)(x(t))),x(0)=x0∈K.
Next we define what we mean by a solution for a Cauchy problem of type (3.3).Definition 3.4.
An absolutely continuous functionx:ℐ⊂ℝ→X, such that
(3.4)x(t)∈K,x(0)=x0∈K,∀t∈I,ẋ(t)=πK(x(t),-(J-1∘F)(x(t))),a.e.on⁡I
is called a solution for the initial value problem (3.3).Finally, assuming that problem (3.3) has solutions as described above, then we are ready to introduce the following.Definition 3.5.
Anon-pivot projected dynamical system (NpPDS) is given by a mapping ϕ:ℝ+×K→K which solves the initial value problem ϕ̇(t,x)=πK(ϕ(t,x),-(J-1∘F)(ϕ(t,x))),a.a.t,ϕ(0,x)=x0∈K.To end this section we show how problem (3.3) can be equivalently (in the sense of solution set coincidence) formulated as a differential inclusion problem. Finally, in Subsection 3.3 we show that solutions for this new differential inclusion problem exist. We introduce the following differential inclusion:(3.5)ẋ(t)∈J-1(-F(x)-NK(x)),x(0)=x0∈K,
and we call x:ℐ⊂ℝ→X absolutely continuous a solution to (3.5) if(3.6)x(t)∈K,x(0)=x0∈K,∀t∈I,ẋ(t)∈J-1(-F(x)-NK(x)),a.a.t.
We introduce also the following differential inclusion:(3.7)ẋ(t)∈J-1(-F(x)-ÑK(x)),x(0)=x0∈K,
where(3.8)ÑK(x)={n∈NK(x)∣‖n‖≤‖F(x)‖}.Obviously, we callx:ℐ⊂ℝ→X absolutely continuous a solution to (3.7) if(3.9)x(t)∈K,x(0)=x0∈K,∀t∈I,ẋ(t)∈J-1(-F(x)-ÑK(x)),a.a.t.Proposition 3.6.
The solution set of problem (3.3) coincides with the solution set of problem (3.9).Proof.
(3.3)⇒(3.9). Let x(·) be an absolutely continuous function on K such that x(·) is a solution to (3.3). Then x(t)∈K, for all t∈I and ẋ(t)=πK(x(t),-(J-1∘F)(x(t))),a.e.onℐ; therefore using Corollary 2.10 we get ẋ(t)=-J-1(F(x))-J-1PNK(x)(-F(x)), a.e.∈I. Evidently, PNK(x)(-F(x))∈NK(x). Moreover as NK(x) is a closed, convex cone, we get that ∥PNK(x)(-F(x))∥X*≤∥-F(x)∥X*)NK0(x)=TK(x) and both contains 0). Therefore ∃ñK(x)∈ÑK(x),ñK(x):=PNK(x)(-F(x)) such that ẋ(t)=-J-1(F(x(t))-ñK(x)) for a.a t∈I, so we have ẋ(t)∈-J-1(F(x(t))-ÑK(x)) for a.a t∈I, and x(·) is a solution to (3.9).
(3.9)⇒(3.3). As the trajectory remains in K it is clear that ẋ(t)∈TK(x(t)). First we show that for almost all t∈I we have
(3.10)ẋ(t)∈NK⊥(x(t)).
Let us consider three different cases; first suppose that x(t)∈int(K), we have then NK(x(t))={0X*} and then NK⊥(x(t))=X* and (3.10) is automatically satisfied. Suppose now that x(t)∈∂K and in x(t), ∂K is smooth. In that case TK(x(t)) is flat and NK⊥(x(t))⊊TK(x(t)) with NK⊥(x(t)) not reduced to {0X*}, if ẋ(t)∉NK⊥(x(t)); then in a neighbourhood 𝒱(t) the trajectory x(t′),t′∈𝒱(t) goes in int(K), so we are in the first case and we can exclude time t. Suppose now that x(t)∈∂K and x(t) is in a corner point. In that case NK⊥(x(t))={0}; therefore if ẋ(t)=0, (3.10) is satisfied. If ẋ(t)≠0, it means that x(t′)≠x(t) for t′∈𝒱(t), with x(t′) in one of the two previous cases; as we can “exclude” time t, we have (3.10). As we can write ẋ(t)=J-1(-F(x)-ñK(x)), we have
(3.11)〈J(ẋ(t))-JJ-1(-F(x)),ẋ(t)〉=0.Using the polarity between NK(x(t)) and TK(x(t)) and the variational principle (2.14) we deduce (3.3).


### 3.3. Existence of NpPDS

In this section we show that problem (3.3) has solutions and consequently that NpPDSs exist in the sense of Definition 3.5, by showing that problem (3.7) has solutions, in the sense of Definition 3.4. To obtain the main result of this paper, we need some preliminary ones, according to the following steps.(1)
We first prove the existence of a sequence of approximate solutions with “good” properties such that(3.12)∀k≥k0,(xk(t),ẋk(t))∈graph(J-1(-F-ÑK))+M,
for any neighbourhood ℳ of 0 in X×X. This step constitutes Theorem 3.9.(2)
we prove next that the sequence obtained in the first step converges to a solution of problem (3.7) and that it has a weakly convergent subsequence whose derivative converges to ẋ(·).The methodology of the proofs is completely analogous to that used for pivot Hilbert spaces in [21]. Therefore we present the results with summary proofs, pointing out where they need to be updated for the case of a non-pivot H-space. The main difference in all proofs is made by the presence of the linear mapping J.The main result can be stated as follows.Theorem 3.7.
LetX be a Hilbert space and X* its topological dual and let K⊂X be a nonempty, closed and convex subset. Let F:K→X* be a Lipschitz continuous vector field with Lipschitz constant b. Let x0∈K. Then the initial value problem (3.3) has a unique solution on ℝ+.Proof Existence of a solution on an interval[0,l],l<∞
For this part of the proof, we need two major results, as follows.
Proposition 3.8.
LetX be a nonpivot H-space, let X* be its topological dual, and let K⊂X be a non-empty, closed and convex subset. Let F:K→X* be a Lipschitz continuous vector field with Lipschitz constant b, so that on K∩BX(x0,L), with L>0 and x0∈K arbitrarily fixed, we have ∥F(x)∥≤M:=∥F(x0)∥+bL.
Then the set-valued mapping𝒩p:K∩BX(x0,L)→ℝ given by
(3.13)x⟼〈F-ÑK(x),p〉
has a closed graph.
Proof .
The proof is similar to the one in [21].
We show first that the mapping𝒩p:K∩BX(x0,L)→ℝ given by x↦〈-ÑK(x),p〉 has a closed graph. It is clear that for each p∈X, the set-valued map 𝒩p:K∩BX(x0,L)→ℝ maps K∩BX(x0,L) into 2[-M∥p∥,M∥p∥]. Let {(xn,zn)}n∈graph(𝒩p) such that (xn,zn)→(x,z)∈X×2[-M∥p∥,M∥p∥]. We want to show that (x,y)∈graph(𝒩p). From zn∈graph(𝒩p), for all n, we deduce that there exists yn∈-ÑK(xn) such that zn=〈yn,p〉. Since the set -ÑK(x)⊂B¯X*(0,M) and B¯X*(0,M) is weakly compact, then there exists a subsequence ynk and y∈X* such that
(3.14)ynk⇀y
for the weak topology σ(X*,X**)=byreflexivityσ(X*,X), which is equivalent to
(3.15)〈ynk,β〉⟶〈y,β〉,∀β∈X.
Suppose now that y∉-ÑK(x). This implies that at least one of the following two alternatives should be satisfied.(1)
There existsw∈K such that 〈y,w-x〉<λ<0.(2)
∥y∥>μ>∥F(x)∥.In the first case as 〈ynk,β〉→〈y,β〉,∀β∈X for k>k0 we have 〈ynk,w-x〉<λ/2. But 〈ynk,w-xnk〉=〈ynk,w-x〉+〈ynk,x-xnk〉 and as xnk→x, there exists k1>0 such that ∀k≥k1, we have 〈ynk,x-xnk〉≤∥x-xnk∥∥ynk∥<(|λ|/4M)M=|λ|/4. Thus 〈ynk,w-xnk〉<λ/4<0, for all k>max(k0,k1). But this contradicts the fact that ynk∈-ÑK(xnk).
In the second case as〈ynk,β〉→〈y,β〉,∀β∈X, we have ([31, Proposition III.12]) ∥F(x)∥<∥y∥≤liminfk→∞∥ynk∥ which is a contradiction because yn∈-ÑK(xn),∀n∈ℕ. The continuity of F and the first part of the proof implies that
(3.16)x⟼〈F-ÑK(x),p〉
has non-empty, closed and convex values for each x∈K and has a closed graph.
The next result is constructing the sequence of approximate solutions for the problem (3.7).
Theorem 3.9.
LetX be a Hilbert space and X* its topological dual, and let K⊂X be a non-empty, closed and convex subset. Let F:K→X* be a Lipschitz continuous vector field so that on K∩BX(x0,L), with L>0 and x0∈K, we have ∥F(x)∥≤M:=∥F(x0)∥+bL. Let l:=L/M and ℐ:=[0,l]. Then there exists a sequence {xk(·)} of absolutely continuous functions defined on ℐ, with values in K, such that for all k≥0,xk(0)=x0 and for almost all t∈ℐ, {xk(t)} and {ẋk(t)} (the sequence of its derivatives) have the following property: for every neighbourhood ℳ of 0 in X×X there exists k0=k0(t,ℳ) such that
(3.17)∀k≥k0,(xk(t),ẋk(t))∈graph(-F-ÑK)+M.
Proof.
The proof, based on topological properties of the spaceX, can be found in [21]. However, given we are now working in non-pivot H-spaces, then instead of zp:=PK(x-hpF(x)) we now construct zp:=PK(x-hpJ-1∘F(x)).
Next we show that the sequence{xk(·)} built in Theorem 3.9 is uniformly convergent to some x(·). Again, following closely [21], by Theorem 3.9 there exists a pair (uk,-F(uk)-nk)∈graph(-F-ÑK) such that
(3.18)xk(t)-uk(t)=ϵ1,k(t),ẋk(t)+J-1(F(uk(t))+nk)=ϵ2,k(t),where ϵ1,k(t) and ϵ2,k(t) are vector functions, not necessarily continuous, satisfying ∥ϵ1,k(t)∥<ϵk and ∥ϵ2,k(t)∥<ϵk where ϵk→0 as k→∞ and nk∈ÑK(uk) and nm∈ÑK(um).
Letk,m be two indexes. Then we evaluate
(3.19)12ddt‖xk(t)-xm(t)‖2=〈J(ẋk(t)-ẋm(t)),xk(t)-xm(t)〉=〈-F(uk(t))+F(xk(t))+F(um(t))-F(xm(t)),xk(t)-xm(t)〉+〈-F(xk(t))+F(xm(t)),xk(t)-xm(t)〉+〈-nk+nm,uk(t)-um(t)〉+〈-nk+nm,-uk(t)+xk(t)+um(t)-xm(t)〉+〈J(ϵ1,k(t)-ϵ2,m(t)),xk(t)-xm(t)〉.
But using the monotonicity of x↦NK(x), the isometry property of J, and the b-Lipschitz continuity of F we get that
(3.20)12ddt‖xk(t)-xm(t)‖2≤b‖xk(t)-xm(t)‖2+(ϵk+ϵm)‖nk-nm‖+(1+b)(ϵk+ϵm)‖xk(t)-xm(t)‖.We now let ϕ(t):=∥xk(t)-xm(t)∥, so from the previous inequalities we get
(3.21)ϕ̇(t)ϕ(t)≤bϕ(t)2+(ϵk+ϵm)[(1+b)ϕ(t)+2M].
Using the same technique as in [21] we get
(3.22)ϕ(t)2≤ab(ϵk+ϵm)(e2bt-1)≤ab(ϵk+ϵm)(e2bl-1),
where l is the length of ℐ. So the Cauchy criteria are satisfied uniformly and we get the conclusion.
From the previous step we know that{xk(·)} is uniformly convergent to x(·) and as (xk(t),ẋk(t))∈graph(-F-ÑK)+ℳ, we now deduce that there exists a θ such that ∥ẋk(t)∥≤θ. Using the arguments in [21] and the result of [32], we deduce the existence of a subsequence of {ẋk}weakly*-convergent to ẋ(·)∈L∞(I,X).
Finally, we finish this part of the proof by showing thatx(·) is indeed a solution of the differential inclusion (3.7). From Theorem 3.9, for each k≥k0 and almost every t∈ℐ there exists a pair
(3.23)(uk(t),vk(t))∈graph(-F-ÑK)
such that ∥xk(t)-uk(t)∥<ϵk and ∥ẋk(t)-vk(t)∥<ϵk, where ϵk→0 when k→∞. Let p∈X arbitrarily fixed. Then for almost all t∈ℐ(3.24)(uk(t),〈vk(t),p〉)∈graph(〈-F-ÑK,p〉),‖〈ẋk(t),p〉-〈vk(t),p〉‖≤‖p‖ϵk.
So uk(t)→x(t) for every t∈ℐ and 〈vk(t),p〉→〈ẋk(t),p〉 for almost all t∈ℐ. By Proposition 3.8, we know that graph(〈-F-ÑK,p〉) is closed, so it follows that for almost all t∈ℐ,
(3.25)(x(t),〈ẋk(t),p〉)∈graph(〈-F-ÑK,p〉).
Since the set F(x(t))-ÑK(x(t)) is convex and closed, it follows that
(3.26)ẋ(t)∈J-1(-F(x(t)-ÑK))(x(t)).
By Proposition 3.6, x(t) is a solution of problem (3.3).
Uniqueness of Solutions on[0,l]
Step 1 (x(·) is the unique solution).
Suppose that we have two solutionsx1(·) and x2(·) starting at the same initial point. For any fixed t∈ℐ we get
(3.27)12ddt‖x1(t)-x1(t)‖2=〈J(ẋ1(t)-ẋ2(t)),x1(t)-x2(t)〉=〈J(ẋ1(t))-J(ẋ2(t)),x1(t)-x2(t)〉≤‖J-1(-F(x1(t))-n1+F(x2(t))+n2),x1(t)-x2(t)>‖≤b‖x1(t)-x2(t)‖2,
because the metric projection is a nonexpansive operator in X, J is a linear isometry, and F is b-Lipschitz. By Gronwall’s inequality we obtain ∥x1(t)-x2(t)∥2≤0, so we have x1(t)=x2(t) for any t∈ℐ.
Existence of Solutions onℝ+
From above we can assert the existence of a solution to problem (3.3) on an interval [0;l], with b>0 fixed and L>0 arbitrary. We note that we can choose L such that l≥1/(1+b) in the following way: if ∥F(x0)∥=0, we let L=1, and if ∥F(x0)∥≠0, then we let L≥∥F(x0)∥. In both cases we obtain l≥1/(1+b). Therefore beginning at each initial point x0∈K, problem (3.3) has a solution on an interval of length at least [0;1/(1+b)]. Now if we consider problem (3.3) with x0=x(1/(1+b)), applying again all the above, we obtain an extension of the solution on an interval of length at least 1/(1+b). By continuing this solution we obtain a solution on [0,∞).


### 3.4. Implicit PDS

In this section we consider a generic Hilbert spaceX, where generic is taken to mean that the dimensionality could be either finite or infinite, and the space could be either a pivot or a non-pivot space. Let us introduce the following definition.Definition 3.10.
LetX be a generic H-space and let K′⊂X be a non-empty, closed subset. Consider a pair (g,K) such that K is convex and g:K′→K=r(K′)⊂X, is continuous, injective, and g-1 is Lipschitz continuous.
ConsiderF:X→X* satisfying (F∘g)(y)=F(y),∀y∈K′. Then the pair (g,K) is called a convexification pair of(F,K′).Example 3.11.
Here is an example of such a convexification pair inℝ2. Let K′={(x,y)∈ℝ2∣0≤x≤1,0≤y≤x} and let g be the map of K′ into K=[0,1]×[0,1], namely:
(3.28)g(x,y)=(x,21+xy+1-x1+x).
We can easily check that g is continuous and monotone. Now take F to be F(x,y)=(x,a), where a is an arbitrary constant in ℝ. Then we have F∘g(x,y)=(x,a)=F(x,y).We now introduce another type of a projected equation as follows.Definition 3.12.
LetX be a generic H-space and let K′⊂X be a non-empty, closed subset. An implicit projected differential equation (ImPrDE) is a (PrDE) given by (3.2) where x(t):=g(y(t)),g:K′→K⊂X, that is:
(3.29)dg(y(t))dt=PTK(g(y(t)))(-J-1∘F∘g(y(t))).The motivation for the introduction of such an equation comes from the desire to study the dynamics on a setK′⊂X, where K′ could be nonconvex, and to study as well some dynamic problems on a so-called translated set (see Section 4 below).Considering now (3.29) and a convexification pair (g,K) of a nonempty, closed K′⊂X, then the Cauchy problem associated to (3.29) and the pair (g,K) is given by(3.30)dg(y(t))dt=πK(g(y(t)),-(J-1∘F|K′))(y(t)),g(y(0))=x0∈K.
Next we define what we mean by a solution for a Cauchy problem of type (3.30).Definition 3.13.
An absolutely continuous functiony:ℐ⊂ℝ→X, such that
(3.31)y(t)∈K′,g(y(0))=x0∈K,∀t∈I,dg(y(t))dt=πK(g(y(t)),-(J-1∘F|K′)(y(t))),a.e.on⁡I
is called a solution for the initial value problem (3.30).We claim that problem (3.30) has solutions by Theorem 3.9. It is obvious that by a change of variable x(·):=g(y(·)), problem (3.30) has solutions on K, in the sense of Definition 3.4. But since g is assumed continuous and strictly monotone, then g is invertible and so y(·)=g-1(x(·)); moreover, we see that such a y is a solution to problem (3.30) in the above sense.Now we are ready to introduce the following.Definition 3.14.
Animplicit projected dynamical system (ImPDS) is given by a mapping ϕ:ℝ+×K′→K which solves the initial value problem:
(3.32)ϕ̇(t,g(y(t)))=πK(ϕ(t,g(y(t))),-(J-1∘F))(ϕ(t,y(t))),a.a.t,ϕ(0,g(y(0)))=x0∈K,
where (g,K) is a convexification pair.Theorem 3.15.
LetX be a generic Hilbert space, and let K′ be a non-empty closed subset of X. Let K be non-empty, closed and convex, let g:K′→K be continuous and strictly monotone, and let F:K′→X be Lipschitz continuous such that (F∘g)|K′=F. Let also x0∈K and L>0 such that ∥x0∥≤L. Then the initial value problem (3.30) has a unique solution on the interval [0,l], where l=L/(∥F(x0)∥+bL).Proof.
The proof consists in the modification of a few easy steps of the proof given in [21] combined with the results of the present paper.


## 3.1. PDS in Pivot H-Spaces

LetX be a pivot Hilbert space of arbitrary (finite or infinite) dimension and let K⊂X be a nonempty, closed, convex subset. The following result has been shown (see [21]).Theorem 3.1.
LetX be a Hilbert space and let K be a nonempty, closed, convex subset. Let F:K→X be a Lipschitz continuous vector field and let x0∈K. Then the initial value problem associated to the projected differential equation (PrDE)
(3.1)dx(τ)dτ=πK(x(τ)),-F(x(τ)),x(0)=x0∈K
has a unique absolutely continuous solution on the interval [0,∞).This result is a generalization of the one in [6], where X:=ℝn, K was a convex polyhedron and F had linear growth.Definition 3.2.
Aprojected dynamical system then is given by a mapping ϕ:ℝ+×K→K which solves the initial value problem: ϕ̇(t,x)=πK(ϕ(t,x),-F(ϕ(t,x)))a.a.t, ϕ(0,x)=x0∈K.


## 3.2. PDS in Non-Pivot H-Spaces

In this subsection we show that, with minor modifications, the existence of PDS in non-pivot H-spaces can be obtained. We first introduce non-pivot projected dynamical systems (NpPDSs) and then show their existence. In analogy with [21] we first introduce the following.Definition 3.3.
ForF:K→X*, a non-pivot projected differential equation (NpPrDE) is a discontinuous ODE given by
(3.2)dx(t)dt=πK(x(t),-(J-1∘F)(x(t)))=PTK(x(t))(-(J-1∘F)(x(t))).Consequently the associated Cauchy problem is given by(3.3)dx(t)dt=πK(x(t),-(J-1∘F)(x(t))),x(0)=x0∈K.
Next we define what we mean by a solution for a Cauchy problem of type (3.3).Definition 3.4.
An absolutely continuous functionx:ℐ⊂ℝ→X, such that
(3.4)x(t)∈K,x(0)=x0∈K,∀t∈I,ẋ(t)=πK(x(t),-(J-1∘F)(x(t))),a.e.on⁡I
is called a solution for the initial value problem (3.3).Finally, assuming that problem (3.3) has solutions as described above, then we are ready to introduce the following.Definition 3.5.
Anon-pivot projected dynamical system (NpPDS) is given by a mapping ϕ:ℝ+×K→K which solves the initial value problem ϕ̇(t,x)=πK(ϕ(t,x),-(J-1∘F)(ϕ(t,x))),a.a.t,ϕ(0,x)=x0∈K.To end this section we show how problem (3.3) can be equivalently (in the sense of solution set coincidence) formulated as a differential inclusion problem. Finally, in Subsection 3.3 we show that solutions for this new differential inclusion problem exist. We introduce the following differential inclusion:(3.5)ẋ(t)∈J-1(-F(x)-NK(x)),x(0)=x0∈K,
and we call x:ℐ⊂ℝ→X absolutely continuous a solution to (3.5) if(3.6)x(t)∈K,x(0)=x0∈K,∀t∈I,ẋ(t)∈J-1(-F(x)-NK(x)),a.a.t.
We introduce also the following differential inclusion:(3.7)ẋ(t)∈J-1(-F(x)-ÑK(x)),x(0)=x0∈K,
where(3.8)ÑK(x)={n∈NK(x)∣‖n‖≤‖F(x)‖}.Obviously, we callx:ℐ⊂ℝ→X absolutely continuous a solution to (3.7) if(3.9)x(t)∈K,x(0)=x0∈K,∀t∈I,ẋ(t)∈J-1(-F(x)-ÑK(x)),a.a.t.Proposition 3.6.
The solution set of problem (3.3) coincides with the solution set of problem (3.9).Proof.
(3.3)⇒(3.9). Let x(·) be an absolutely continuous function on K such that x(·) is a solution to (3.3). Then x(t)∈K, for all t∈I and ẋ(t)=πK(x(t),-(J-1∘F)(x(t))),a.e.onℐ; therefore using Corollary 2.10 we get ẋ(t)=-J-1(F(x))-J-1PNK(x)(-F(x)), a.e.∈I. Evidently, PNK(x)(-F(x))∈NK(x). Moreover as NK(x) is a closed, convex cone, we get that ∥PNK(x)(-F(x))∥X*≤∥-F(x)∥X*)NK0(x)=TK(x) and both contains 0). Therefore ∃ñK(x)∈ÑK(x),ñK(x):=PNK(x)(-F(x)) such that ẋ(t)=-J-1(F(x(t))-ñK(x)) for a.a t∈I, so we have ẋ(t)∈-J-1(F(x(t))-ÑK(x)) for a.a t∈I, and x(·) is a solution to (3.9).
(3.9)⇒(3.3). As the trajectory remains in K it is clear that ẋ(t)∈TK(x(t)). First we show that for almost all t∈I we have
(3.10)ẋ(t)∈NK⊥(x(t)).
Let us consider three different cases; first suppose that x(t)∈int(K), we have then NK(x(t))={0X*} and then NK⊥(x(t))=X* and (3.10) is automatically satisfied. Suppose now that x(t)∈∂K and in x(t), ∂K is smooth. In that case TK(x(t)) is flat and NK⊥(x(t))⊊TK(x(t)) with NK⊥(x(t)) not reduced to {0X*}, if ẋ(t)∉NK⊥(x(t)); then in a neighbourhood 𝒱(t) the trajectory x(t′),t′∈𝒱(t) goes in int(K), so we are in the first case and we can exclude time t. Suppose now that x(t)∈∂K and x(t) is in a corner point. In that case NK⊥(x(t))={0}; therefore if ẋ(t)=0, (3.10) is satisfied. If ẋ(t)≠0, it means that x(t′)≠x(t) for t′∈𝒱(t), with x(t′) in one of the two previous cases; as we can “exclude” time t, we have (3.10). As we can write ẋ(t)=J-1(-F(x)-ñK(x)), we have
(3.11)〈J(ẋ(t))-JJ-1(-F(x)),ẋ(t)〉=0.Using the polarity between NK(x(t)) and TK(x(t)) and the variational principle (2.14) we deduce (3.3).


## 3.3. Existence of NpPDS

In this section we show that problem (3.3) has solutions and consequently that NpPDSs exist in the sense of Definition 3.5, by showing that problem (3.7) has solutions, in the sense of Definition 3.4. To obtain the main result of this paper, we need some preliminary ones, according to the following steps.(1)
We first prove the existence of a sequence of approximate solutions with “good” properties such that(3.12)∀k≥k0,(xk(t),ẋk(t))∈graph(J-1(-F-ÑK))+M,
for any neighbourhood ℳ of 0 in X×X. This step constitutes Theorem 3.9.(2)
we prove next that the sequence obtained in the first step converges to a solution of problem (3.7) and that it has a weakly convergent subsequence whose derivative converges to ẋ(·).The methodology of the proofs is completely analogous to that used for pivot Hilbert spaces in [21]. Therefore we present the results with summary proofs, pointing out where they need to be updated for the case of a non-pivot H-space. The main difference in all proofs is made by the presence of the linear mapping J.The main result can be stated as follows.Theorem 3.7.
LetX be a Hilbert space and X* its topological dual and let K⊂X be a nonempty, closed and convex subset. Let F:K→X* be a Lipschitz continuous vector field with Lipschitz constant b. Let x0∈K. Then the initial value problem (3.3) has a unique solution on ℝ+.Proof Existence of a solution on an interval[0,l],l<∞
For this part of the proof, we need two major results, as follows.
Proposition 3.8.
LetX be a nonpivot H-space, let X* be its topological dual, and let K⊂X be a non-empty, closed and convex subset. Let F:K→X* be a Lipschitz continuous vector field with Lipschitz constant b, so that on K∩BX(x0,L), with L>0 and x0∈K arbitrarily fixed, we have ∥F(x)∥≤M:=∥F(x0)∥+bL.
Then the set-valued mapping𝒩p:K∩BX(x0,L)→ℝ given by
(3.13)x⟼〈F-ÑK(x),p〉
has a closed graph.
Proof .
The proof is similar to the one in [21].
We show first that the mapping𝒩p:K∩BX(x0,L)→ℝ given by x↦〈-ÑK(x),p〉 has a closed graph. It is clear that for each p∈X, the set-valued map 𝒩p:K∩BX(x0,L)→ℝ maps K∩BX(x0,L) into 2[-M∥p∥,M∥p∥]. Let {(xn,zn)}n∈graph(𝒩p) such that (xn,zn)→(x,z)∈X×2[-M∥p∥,M∥p∥]. We want to show that (x,y)∈graph(𝒩p). From zn∈graph(𝒩p), for all n, we deduce that there exists yn∈-ÑK(xn) such that zn=〈yn,p〉. Since the set -ÑK(x)⊂B¯X*(0,M) and B¯X*(0,M) is weakly compact, then there exists a subsequence ynk and y∈X* such that
(3.14)ynk⇀y
for the weak topology σ(X*,X**)=byreflexivityσ(X*,X), which is equivalent to
(3.15)〈ynk,β〉⟶〈y,β〉,∀β∈X.
Suppose now that y∉-ÑK(x). This implies that at least one of the following two alternatives should be satisfied.(1)
There existsw∈K such that 〈y,w-x〉<λ<0.(2)
∥y∥>μ>∥F(x)∥.In the first case as 〈ynk,β〉→〈y,β〉,∀β∈X for k>k0 we have 〈ynk,w-x〉<λ/2. But 〈ynk,w-xnk〉=〈ynk,w-x〉+〈ynk,x-xnk〉 and as xnk→x, there exists k1>0 such that ∀k≥k1, we have 〈ynk,x-xnk〉≤∥x-xnk∥∥ynk∥<(|λ|/4M)M=|λ|/4. Thus 〈ynk,w-xnk〉<λ/4<0, for all k>max(k0,k1). But this contradicts the fact that ynk∈-ÑK(xnk).
In the second case as〈ynk,β〉→〈y,β〉,∀β∈X, we have ([31, Proposition III.12]) ∥F(x)∥<∥y∥≤liminfk→∞∥ynk∥ which is a contradiction because yn∈-ÑK(xn),∀n∈ℕ. The continuity of F and the first part of the proof implies that
(3.16)x⟼〈F-ÑK(x),p〉
has non-empty, closed and convex values for each x∈K and has a closed graph.
The next result is constructing the sequence of approximate solutions for the problem (3.7).
Theorem 3.9.
LetX be a Hilbert space and X* its topological dual, and let K⊂X be a non-empty, closed and convex subset. Let F:K→X* be a Lipschitz continuous vector field so that on K∩BX(x0,L), with L>0 and x0∈K, we have ∥F(x)∥≤M:=∥F(x0)∥+bL. Let l:=L/M and ℐ:=[0,l]. Then there exists a sequence {xk(·)} of absolutely continuous functions defined on ℐ, with values in K, such that for all k≥0,xk(0)=x0 and for almost all t∈ℐ, {xk(t)} and {ẋk(t)} (the sequence of its derivatives) have the following property: for every neighbourhood ℳ of 0 in X×X there exists k0=k0(t,ℳ) such that
(3.17)∀k≥k0,(xk(t),ẋk(t))∈graph(-F-ÑK)+M.
Proof.
The proof, based on topological properties of the spaceX, can be found in [21]. However, given we are now working in non-pivot H-spaces, then instead of zp:=PK(x-hpF(x)) we now construct zp:=PK(x-hpJ-1∘F(x)).
Next we show that the sequence{xk(·)} built in Theorem 3.9 is uniformly convergent to some x(·). Again, following closely [21], by Theorem 3.9 there exists a pair (uk,-F(uk)-nk)∈graph(-F-ÑK) such that
(3.18)xk(t)-uk(t)=ϵ1,k(t),ẋk(t)+J-1(F(uk(t))+nk)=ϵ2,k(t),where ϵ1,k(t) and ϵ2,k(t) are vector functions, not necessarily continuous, satisfying ∥ϵ1,k(t)∥<ϵk and ∥ϵ2,k(t)∥<ϵk where ϵk→0 as k→∞ and nk∈ÑK(uk) and nm∈ÑK(um).
Letk,m be two indexes. Then we evaluate
(3.19)12ddt‖xk(t)-xm(t)‖2=〈J(ẋk(t)-ẋm(t)),xk(t)-xm(t)〉=〈-F(uk(t))+F(xk(t))+F(um(t))-F(xm(t)),xk(t)-xm(t)〉+〈-F(xk(t))+F(xm(t)),xk(t)-xm(t)〉+〈-nk+nm,uk(t)-um(t)〉+〈-nk+nm,-uk(t)+xk(t)+um(t)-xm(t)〉+〈J(ϵ1,k(t)-ϵ2,m(t)),xk(t)-xm(t)〉.
But using the monotonicity of x↦NK(x), the isometry property of J, and the b-Lipschitz continuity of F we get that
(3.20)12ddt‖xk(t)-xm(t)‖2≤b‖xk(t)-xm(t)‖2+(ϵk+ϵm)‖nk-nm‖+(1+b)(ϵk+ϵm)‖xk(t)-xm(t)‖.We now let ϕ(t):=∥xk(t)-xm(t)∥, so from the previous inequalities we get
(3.21)ϕ̇(t)ϕ(t)≤bϕ(t)2+(ϵk+ϵm)[(1+b)ϕ(t)+2M].
Using the same technique as in [21] we get
(3.22)ϕ(t)2≤ab(ϵk+ϵm)(e2bt-1)≤ab(ϵk+ϵm)(e2bl-1),
where l is the length of ℐ. So the Cauchy criteria are satisfied uniformly and we get the conclusion.
From the previous step we know that{xk(·)} is uniformly convergent to x(·) and as (xk(t),ẋk(t))∈graph(-F-ÑK)+ℳ, we now deduce that there exists a θ such that ∥ẋk(t)∥≤θ. Using the arguments in [21] and the result of [32], we deduce the existence of a subsequence of {ẋk}weakly*-convergent to ẋ(·)∈L∞(I,X).
Finally, we finish this part of the proof by showing thatx(·) is indeed a solution of the differential inclusion (3.7). From Theorem 3.9, for each k≥k0 and almost every t∈ℐ there exists a pair
(3.23)(uk(t),vk(t))∈graph(-F-ÑK)
such that ∥xk(t)-uk(t)∥<ϵk and ∥ẋk(t)-vk(t)∥<ϵk, where ϵk→0 when k→∞. Let p∈X arbitrarily fixed. Then for almost all t∈ℐ(3.24)(uk(t),〈vk(t),p〉)∈graph(〈-F-ÑK,p〉),‖〈ẋk(t),p〉-〈vk(t),p〉‖≤‖p‖ϵk.
So uk(t)→x(t) for every t∈ℐ and 〈vk(t),p〉→〈ẋk(t),p〉 for almost all t∈ℐ. By Proposition 3.8, we know that graph(〈-F-ÑK,p〉) is closed, so it follows that for almost all t∈ℐ,
(3.25)(x(t),〈ẋk(t),p〉)∈graph(〈-F-ÑK,p〉).
Since the set F(x(t))-ÑK(x(t)) is convex and closed, it follows that
(3.26)ẋ(t)∈J-1(-F(x(t)-ÑK))(x(t)).
By Proposition 3.6, x(t) is a solution of problem (3.3).
Uniqueness of Solutions on[0,l]
Step 1 (x(·) is the unique solution).
Suppose that we have two solutionsx1(·) and x2(·) starting at the same initial point. For any fixed t∈ℐ we get
(3.27)12ddt‖x1(t)-x1(t)‖2=〈J(ẋ1(t)-ẋ2(t)),x1(t)-x2(t)〉=〈J(ẋ1(t))-J(ẋ2(t)),x1(t)-x2(t)〉≤‖J-1(-F(x1(t))-n1+F(x2(t))+n2),x1(t)-x2(t)>‖≤b‖x1(t)-x2(t)‖2,
because the metric projection is a nonexpansive operator in X, J is a linear isometry, and F is b-Lipschitz. By Gronwall’s inequality we obtain ∥x1(t)-x2(t)∥2≤0, so we have x1(t)=x2(t) for any t∈ℐ.
Existence of Solutions onℝ+
From above we can assert the existence of a solution to problem (3.3) on an interval [0;l], with b>0 fixed and L>0 arbitrary. We note that we can choose L such that l≥1/(1+b) in the following way: if ∥F(x0)∥=0, we let L=1, and if ∥F(x0)∥≠0, then we let L≥∥F(x0)∥. In both cases we obtain l≥1/(1+b). Therefore beginning at each initial point x0∈K, problem (3.3) has a solution on an interval of length at least [0;1/(1+b)]. Now if we consider problem (3.3) with x0=x(1/(1+b)), applying again all the above, we obtain an extension of the solution on an interval of length at least 1/(1+b). By continuing this solution we obtain a solution on [0,∞).


## 3.4. Implicit PDS

In this section we consider a generic Hilbert spaceX, where generic is taken to mean that the dimensionality could be either finite or infinite, and the space could be either a pivot or a non-pivot space. Let us introduce the following definition.Definition 3.10.
LetX be a generic H-space and let K′⊂X be a non-empty, closed subset. Consider a pair (g,K) such that K is convex and g:K′→K=r(K′)⊂X, is continuous, injective, and g-1 is Lipschitz continuous.
ConsiderF:X→X* satisfying (F∘g)(y)=F(y),∀y∈K′. Then the pair (g,K) is called a convexification pair of(F,K′).Example 3.11.
Here is an example of such a convexification pair inℝ2. Let K′={(x,y)∈ℝ2∣0≤x≤1,0≤y≤x} and let g be the map of K′ into K=[0,1]×[0,1], namely:
(3.28)g(x,y)=(x,21+xy+1-x1+x).
We can easily check that g is continuous and monotone. Now take F to be F(x,y)=(x,a), where a is an arbitrary constant in ℝ. Then we have F∘g(x,y)=(x,a)=F(x,y).We now introduce another type of a projected equation as follows.Definition 3.12.
LetX be a generic H-space and let K′⊂X be a non-empty, closed subset. An implicit projected differential equation (ImPrDE) is a (PrDE) given by (3.2) where x(t):=g(y(t)),g:K′→K⊂X, that is:
(3.29)dg(y(t))dt=PTK(g(y(t)))(-J-1∘F∘g(y(t))).The motivation for the introduction of such an equation comes from the desire to study the dynamics on a setK′⊂X, where K′ could be nonconvex, and to study as well some dynamic problems on a so-called translated set (see Section 4 below).Considering now (3.29) and a convexification pair (g,K) of a nonempty, closed K′⊂X, then the Cauchy problem associated to (3.29) and the pair (g,K) is given by(3.30)dg(y(t))dt=πK(g(y(t)),-(J-1∘F|K′))(y(t)),g(y(0))=x0∈K.
Next we define what we mean by a solution for a Cauchy problem of type (3.30).Definition 3.13.
An absolutely continuous functiony:ℐ⊂ℝ→X, such that
(3.31)y(t)∈K′,g(y(0))=x0∈K,∀t∈I,dg(y(t))dt=πK(g(y(t)),-(J-1∘F|K′)(y(t))),a.e.on⁡I
is called a solution for the initial value problem (3.30).We claim that problem (3.30) has solutions by Theorem 3.9. It is obvious that by a change of variable x(·):=g(y(·)), problem (3.30) has solutions on K, in the sense of Definition 3.4. But since g is assumed continuous and strictly monotone, then g is invertible and so y(·)=g-1(x(·)); moreover, we see that such a y is a solution to problem (3.30) in the above sense.Now we are ready to introduce the following.Definition 3.14.
Animplicit projected dynamical system (ImPDS) is given by a mapping ϕ:ℝ+×K′→K which solves the initial value problem:
(3.32)ϕ̇(t,g(y(t)))=πK(ϕ(t,g(y(t))),-(J-1∘F))(ϕ(t,y(t))),a.a.t,ϕ(0,g(y(0)))=x0∈K,
where (g,K) is a convexification pair.Theorem 3.15.
LetX be a generic Hilbert space, and let K′ be a non-empty closed subset of X. Let K be non-empty, closed and convex, let g:K′→K be continuous and strictly monotone, and let F:K′→X be Lipschitz continuous such that (F∘g)|K′=F. Let also x0∈K and L>0 such that ∥x0∥≤L. Then the initial value problem (3.30) has a unique solution on the interval [0,l], where l=L/(∥F(x0)∥+bL).Proof.
The proof consists in the modification of a few easy steps of the proof given in [21] combined with the results of the present paper.


## 4. Applications

### 4.1. NpPDS, ImPDS, and Variational Inequalities

It is worth noting at this point that, as in the pivot case, a NpPDS is also related to a variational inequality (VI) problem. To show this relation, we first define what is meant by a critical point of NpPDS.Definition 4.1.
A pointx*∈K is called a critical point for (3.2) if
(4.1)πK(x*,-(J-1∘F)(x*))=0.Theorem 4.2.
Let X be a generic Hilbert space and letK⊂X be a non-empty, closed and convex subset. Let F:X→X* be a vector field. Consider the variational inequality problem:
(4.2)x∈K:〈F(x),v-x〉≥0,∀v∈K.
Then the solution set of (4.2) coincides with the set of critical points of the non-pivot projected dynamical system (3.2).Proof.
It follows from the decomposition Theorem2.8 (see also [23]).The relation between an ImPDS and a VI problem is more interesting, as has been considered before in the literature, but with superfluous conditions on the projection operatorPK we describe this relation next.Definition 4.3.
LetX be a generic H-space and let K′⊂X be a non-empty, closed subset. Let F:X→X* be a mapping. Then we call g-variational inequality on the set K′ the problem of
(4.3)findingy∈K′,〈F∘g(y),z-g(y)〉≥0,∀z∈K,
where (g,K) is a convexification pair of (F,K′).We highlight the importance of the relationF∘g(y)=F(y) from Definition 3.10 in order for (4.3) to make sense. Under (3.5) we can rewrite (4.3) as(4.4)findy∈K′,〈F(y),z-g(y)〉≥0,∀z∈K.Remark 4.4.
In [24], (4.4) is considered in a pivot H-space and is called a “general variational inequality.” We prefer to use the term “g-variational inequality” in relation to (4.4), in order to avoid confusion with the commonly accepted “generalized variational inequality” which involves multimappings.Theorem 4.5.
If the problems (4.4) and (3.30) admit a solution, then the equilibrium points of (4.4) coincide with the critical points of (3.30).Proof.
Supposey*∈K′ is a solution of (4.4); then by definition we have
(4.5)〈F(y*),z-g(y*)〉≥0,∀z∈K.
So by multiplying by a strictly positive constant λ and using the bilinearity of the inner product, we get
(4.6)〈-F(y*),y〉≤0,∀y∈TK(g(y*)).
So we deduce that -F(y*)∈NK(g(y*)); using the decomposition Theorem 2.8 we get PTK(g(y*))(-J-1F(y*))=0, and so y* is a critical point of (3.30).
Now suppose thaty* is a critical point of (3.30); then by definition we have
(4.7)PTK(g(y*))(-J-1F(y*))=0,
and by the decomposition theorem we get -F(y*)∈NK(g(y*)). By the definition of the normal cone to K in g(y*), the following inequality is satisfied:
(4.8)〈-F(y*),z-g(y*)〉≤0,∀z∈K,
which is exactly (4.4).


## 4.1. NpPDS, ImPDS, and Variational Inequalities

It is worth noting at this point that, as in the pivot case, a NpPDS is also related to a variational inequality (VI) problem. To show this relation, we first define what is meant by a critical point of NpPDS.Definition 4.1.
A pointx*∈K is called a critical point for (3.2) if
(4.1)πK(x*,-(J-1∘F)(x*))=0.Theorem 4.2.
Let X be a generic Hilbert space and letK⊂X be a non-empty, closed and convex subset. Let F:X→X* be a vector field. Consider the variational inequality problem:
(4.2)x∈K:〈F(x),v-x〉≥0,∀v∈K.
Then the solution set of (4.2) coincides with the set of critical points of the non-pivot projected dynamical system (3.2).Proof.
It follows from the decomposition Theorem2.8 (see also [23]).The relation between an ImPDS and a VI problem is more interesting, as has been considered before in the literature, but with superfluous conditions on the projection operatorPK we describe this relation next.Definition 4.3.
LetX be a generic H-space and let K′⊂X be a non-empty, closed subset. Let F:X→X* be a mapping. Then we call g-variational inequality on the set K′ the problem of
(4.3)findingy∈K′,〈F∘g(y),z-g(y)〉≥0,∀z∈K,
where (g,K) is a convexification pair of (F,K′).We highlight the importance of the relationF∘g(y)=F(y) from Definition 3.10 in order for (4.3) to make sense. Under (3.5) we can rewrite (4.3) as(4.4)findy∈K′,〈F(y),z-g(y)〉≥0,∀z∈K.Remark 4.4.
In [24], (4.4) is considered in a pivot H-space and is called a “general variational inequality.” We prefer to use the term “g-variational inequality” in relation to (4.4), in order to avoid confusion with the commonly accepted “generalized variational inequality” which involves multimappings.Theorem 4.5.
If the problems (4.4) and (3.30) admit a solution, then the equilibrium points of (4.4) coincide with the critical points of (3.30).Proof.
Supposey*∈K′ is a solution of (4.4); then by definition we have
(4.5)〈F(y*),z-g(y*)〉≥0,∀z∈K.
So by multiplying by a strictly positive constant λ and using the bilinearity of the inner product, we get
(4.6)〈-F(y*),y〉≤0,∀y∈TK(g(y*)).
So we deduce that -F(y*)∈NK(g(y*)); using the decomposition Theorem 2.8 we get PTK(g(y*))(-J-1F(y*))=0, and so y* is a critical point of (3.30).
Now suppose thaty* is a critical point of (3.30); then by definition we have
(4.7)PTK(g(y*))(-J-1F(y*))=0,
and by the decomposition theorem we get -F(y*)∈NK(g(y*)). By the definition of the normal cone to K in g(y*), the following inequality is satisfied:
(4.8)〈-F(y*),z-g(y*)〉≤0,∀z∈K,
which is exactly (4.4).


## 5. Examples and Applications

### 5.1. Weighted Traffic Problem

Let us introduce a network𝒩, that means a set 𝒲 of origin-destination pair (origin/destination node) and a set ℛ of routes. Each route r∈ℛ links exactly one origin-destination pair w∈𝒲. The set of all r∈ℛ which link a given w∈𝒲 is denoted by ℛ(w). For each time t∈(0,T) we consider vector flow F(t)∈ℝn. Let us denote by Ω an open subset of ℝ, by n=card(ℛ), a={a1,…,an}, and by a-1={a1-1,…,an-1} two families of weights such that for each 1≤i≤n,ai∈𝒞(Ω,ℝ+∖{0}). We introduce also the family of real time traffic densities s={s1,…,sn} such that for each 1≤i≤n, si∈𝒞(Ω,ℝ+∖{0}).Letri correspond to an element of a and s, newly to ai and si. If we denote by Vi=L2(Ω,ℝ,ai,si) and Vi*=L2(Ω,ℝ,ai-1,si), the space(5.1)V=∏i=1nVi
is a Hilbert space for the inner product(5.2)〈F,G〉a,s=∑i=1n∫ΩFi(ω)Gi(ω)ai(ω)si(ω)dω.
The space V*=∏i=1nVi* is a Hilbert space for the following inner product(5.3)〈F,G〉a-1,s=∑i=1n∫ΩFi(ω)Gi(ω)si(ω)ai(ω)dω,
and the following bilinear form defines a duality between V and V*:(5.4)V*×V⟶R,(5.5)〈f,x〉s=∑i=1n∫Ωfi(ω)xi(ω)si(ω)dω.
More exactly we have the following.Proposition 5.1.
The bilinear form (5.5) is defined over V*×V and defines a duality between V*×V. The duality mapping is given by J(F)=(a1F1,…,anFn).The feasible flows have to satisfy the time-dependent capacity constraints and demand requirements; namely, for allr∈ℛ, w∈𝒲 and for almost all t∈Ω,(5.6)λr(t)≤Fr(t)≤μr(t),∑r∈R(w)Fr(t)=ρw(t),
where 0≤λ≤μ are given in L2([0,T],ℝn), ρ∈L2([0,T],ℝm) where m=card(𝒲),Fr,r∈ℛ, denotes the flow in the route r. If Φ=(Φw,r) is the pair route incidence matrix, with w∈𝒲 and r∈ℛ, that is,(5.7)Φw,r:=χR(w)(r),
the demand requirements can be written in matrix-vector notation as(5.8)ΦF(t)=ρ(t).
The set of all feasible flows is given by(5.9)K:={F∈V∣λ(t)≤F(t)≤μ(t),a.e.inΩ;ΦF(t)=ρ(t),a.einΩ}.
We provide now the definition of equilibrium for the traffic problem. First we need to define the notion of equilibrium for a variational inequality. A variational inequality (VI) in a Hilbert space V is to determine(5.10)x∈K:〈C(x),y-x〉s≥0,∀y∈K,
where K is a closed convex subset of V, and C:K→V* is a mapping.Definition 5.2.
H∈V is an equilibrium flow if and only if
(5.11)H∈K,〈C(H),F-H〉s≥0,∀F∈K.It is possible to prove the equivalence between condition (5.11) and what we will call a weighted Wardrop condition (5.13).Theorem 5.3.
H∈K is an equilibrium flow in the sense of (5.11) if and only if
(5.12)∀w∈W,∀q,m∈R(w),a.e.inΩ,(5.13)sq(t)Cq(H(t))<sm(t)Cm(H(t)),⟹Hq(t)=μq(t)orHm(t)=λm(t).Proof.
see [20].Based on previous results [20], this solution coincides with the set of critical points of the associated projected dynamical system.


### 5.2. Quasivariational Inequalities on Translated Sets

#### 5.2.1. QVI

LetX be a generic H-space, D closed, convex, nonempty in X. Let 𝒦:D→2X with 𝒦(x) convex for all x∈D and F:𝒦→2X* a mapping.Let us introduce the following variational inequality:(5.14)findx∈K(x),〈F(x),y-x〉≥0,∀y∈K(x).
Note that in this case the set in which we are looking for the solution depends on x. For problem (5.14) we can provide the following existence result (see [17] or [33]).Theorem 5.4.
LetD be a closed convex subset in a locally convex Hausdorff topological vector space X. Let us suppose that(i)
𝒦:D→2D is a closed lower semicontinuous correspondence with closed, convex, and nonempty values,(ii)
C:D→2X′ is a monotone, finite continuous, and bounded single-valued map,(iii)
there exist a compact, convex, and nonempty setZ⊂D and a nonempty subset B⊂Z such that
(a)
𝒦(B)⊂Z,(b)
𝒦(z)∩Z≠∅,∀z∈Z,(c)
for everyz∈Z∖B, there exist ẑ∈𝒦(z)∩Z with 〈C(z),ẑ-z〉<0.
Then there existsx such that
(5.15)x∈K(x):〈C(x),y-x〉≥0,∀y∈K(x).In order to study the disequilibrium behavior of (5.14), we introduce now the following projected differential equation.Definition 5.5.
We call projected dynamical system associated to the quasivariational inequality (5.14) the solution set of the projected differential equation:
(5.16)dx(t)dt=PTK(x)(x)(-J-1F(x)),x(0)=x0∈K(x).Remark 5.6.
In general there are no existence results for problem (5.16). An existence result for a particular case of (5.16) has been given in [24], assuming the following fact.
Assumption 5.7.
LetXbe a pivot H-space. For allu,v,w∈X,P𝒦(u)satisfies the condition(5.17)‖PK(u)(w)-PK(v)(w)‖≤λ‖u-v‖,
where λ>0 is a constant.
However, this assumption fails to be true. One counterexample is as follows. We denote byC a closed convex set and we take u,v∈C; we denote by 𝒦(u)=TC(u) and by 𝒦(v)=TC(v) the tangent cones of C at u and v.
In fact,w∈X can only be chosen in one of the following four situations:(1)
w∈𝒦(u)∩𝒦(v),(2)
w∈𝒦(u)∖𝒦(v),(3)
w∈𝒦(v)∖𝒦(u),(4)
w∈X∖(𝒦(u)∪𝒦(v)).Suppose now that we have w∈𝒦(u)∖𝒦(v); then by Moreau’s decomposition theorem we get
(5.18)‖PK(u)(w)-PK(v)(w)‖=‖w-PK(v)(w)‖=‖PNC(v)(w)‖≤λ‖u-v‖,
where NC(v) is the normal cone of C at v. Consider now X=ℝ2, C=[0,ϵ]2, u=(0,0) and v=(ϵ,ϵ). It is clear that we have the following:
(5.19)TC(u)=R+2,TC(v)=R-2,NC(v)=R+2=TC(u).
So for any w∈NC(v) we get
(5.20)‖w‖≤λ‖u-v‖=2ϵλ.
Since w is arbitrary, let now w:=μw, for any μ>0. Then,
(5.21)‖μw‖≤λ‖u-v‖=2ϵλ
should be true for any μ>0. However this does not hold.Consider now the special case of a set-valued mapping𝒦 which is the translation of a closed, convex subset K:(5.22)K:x⟶K+v(x),wherev(x) is a vector linearly dependant on x; then problems (5.14) and (5.16) can be studied, under certain conditions, respectively, as a g-VI and an implicit PDS as shown below.If𝒦(x)=K+p(x) as done by Noor for type B PDS [24], we have the following equivalent formulations:(5.23)dx(t)dt=PTK+p(x)(x)(-J-1F(x))=PTK(g(x))(-J-1F(x)),x(0)=x0∈K,
where g(x)=x-p(x), assuming F(g(x))=F(x-p(x))=F(x). We can observe that if (dp(x))/dt=0, then (5.23) is equal to the implicit projected differential equation (3.29), and therefore Theorem 3.15 provides an existence result without assuming any kind of Lipschitz condition of the projection operator.


## 5.1. Weighted Traffic Problem

Let us introduce a network𝒩, that means a set 𝒲 of origin-destination pair (origin/destination node) and a set ℛ of routes. Each route r∈ℛ links exactly one origin-destination pair w∈𝒲. The set of all r∈ℛ which link a given w∈𝒲 is denoted by ℛ(w). For each time t∈(0,T) we consider vector flow F(t)∈ℝn. Let us denote by Ω an open subset of ℝ, by n=card(ℛ), a={a1,…,an}, and by a-1={a1-1,…,an-1} two families of weights such that for each 1≤i≤n,ai∈𝒞(Ω,ℝ+∖{0}). We introduce also the family of real time traffic densities s={s1,…,sn} such that for each 1≤i≤n, si∈𝒞(Ω,ℝ+∖{0}).Letri correspond to an element of a and s, newly to ai and si. If we denote by Vi=L2(Ω,ℝ,ai,si) and Vi*=L2(Ω,ℝ,ai-1,si), the space(5.1)V=∏i=1nVi
is a Hilbert space for the inner product(5.2)〈F,G〉a,s=∑i=1n∫ΩFi(ω)Gi(ω)ai(ω)si(ω)dω.
The space V*=∏i=1nVi* is a Hilbert space for the following inner product(5.3)〈F,G〉a-1,s=∑i=1n∫ΩFi(ω)Gi(ω)si(ω)ai(ω)dω,
and the following bilinear form defines a duality between V and V*:(5.4)V*×V⟶R,(5.5)〈f,x〉s=∑i=1n∫Ωfi(ω)xi(ω)si(ω)dω.
More exactly we have the following.Proposition 5.1.
The bilinear form (5.5) is defined over V*×V and defines a duality between V*×V. The duality mapping is given by J(F)=(a1F1,…,anFn).The feasible flows have to satisfy the time-dependent capacity constraints and demand requirements; namely, for allr∈ℛ, w∈𝒲 and for almost all t∈Ω,(5.6)λr(t)≤Fr(t)≤μr(t),∑r∈R(w)Fr(t)=ρw(t),
where 0≤λ≤μ are given in L2([0,T],ℝn), ρ∈L2([0,T],ℝm) where m=card(𝒲),Fr,r∈ℛ, denotes the flow in the route r. If Φ=(Φw,r) is the pair route incidence matrix, with w∈𝒲 and r∈ℛ, that is,(5.7)Φw,r:=χR(w)(r),
the demand requirements can be written in matrix-vector notation as(5.8)ΦF(t)=ρ(t).
The set of all feasible flows is given by(5.9)K:={F∈V∣λ(t)≤F(t)≤μ(t),a.e.inΩ;ΦF(t)=ρ(t),a.einΩ}.
We provide now the definition of equilibrium for the traffic problem. First we need to define the notion of equilibrium for a variational inequality. A variational inequality (VI) in a Hilbert space V is to determine(5.10)x∈K:〈C(x),y-x〉s≥0,∀y∈K,
where K is a closed convex subset of V, and C:K→V* is a mapping.Definition 5.2.
H∈V is an equilibrium flow if and only if
(5.11)H∈K,〈C(H),F-H〉s≥0,∀F∈K.It is possible to prove the equivalence between condition (5.11) and what we will call a weighted Wardrop condition (5.13).Theorem 5.3.
H∈K is an equilibrium flow in the sense of (5.11) if and only if
(5.12)∀w∈W,∀q,m∈R(w),a.e.inΩ,(5.13)sq(t)Cq(H(t))<sm(t)Cm(H(t)),⟹Hq(t)=μq(t)orHm(t)=λm(t).Proof.
see [20].Based on previous results [20], this solution coincides with the set of critical points of the associated projected dynamical system.


## 5.2. Quasivariational Inequalities on Translated Sets

### 5.2.1. QVI

LetX be a generic H-space, D closed, convex, nonempty in X. Let 𝒦:D→2X with 𝒦(x) convex for all x∈D and F:𝒦→2X* a mapping.Let us introduce the following variational inequality:(5.14)findx∈K(x),〈F(x),y-x〉≥0,∀y∈K(x).
Note that in this case the set in which we are looking for the solution depends on x. For problem (5.14) we can provide the following existence result (see [17] or [33]).Theorem 5.4.
LetD be a closed convex subset in a locally convex Hausdorff topological vector space X. Let us suppose that(i)
𝒦:D→2D is a closed lower semicontinuous correspondence with closed, convex, and nonempty values,(ii)
C:D→2X′ is a monotone, finite continuous, and bounded single-valued map,(iii)
there exist a compact, convex, and nonempty setZ⊂D and a nonempty subset B⊂Z such that
(a)
𝒦(B)⊂Z,(b)
𝒦(z)∩Z≠∅,∀z∈Z,(c)
for everyz∈Z∖B, there exist ẑ∈𝒦(z)∩Z with 〈C(z),ẑ-z〉<0.
Then there existsx such that
(5.15)x∈K(x):〈C(x),y-x〉≥0,∀y∈K(x).In order to study the disequilibrium behavior of (5.14), we introduce now the following projected differential equation.Definition 5.5.
We call projected dynamical system associated to the quasivariational inequality (5.14) the solution set of the projected differential equation:
(5.16)dx(t)dt=PTK(x)(x)(-J-1F(x)),x(0)=x0∈K(x).Remark 5.6.
In general there are no existence results for problem (5.16). An existence result for a particular case of (5.16) has been given in [24], assuming the following fact.
Assumption 5.7.
LetXbe a pivot H-space. For allu,v,w∈X,P𝒦(u)satisfies the condition(5.17)‖PK(u)(w)-PK(v)(w)‖≤λ‖u-v‖,
where λ>0 is a constant.
However, this assumption fails to be true. One counterexample is as follows. We denote byC a closed convex set and we take u,v∈C; we denote by 𝒦(u)=TC(u) and by 𝒦(v)=TC(v) the tangent cones of C at u and v.
In fact,w∈X can only be chosen in one of the following four situations:(1)
w∈𝒦(u)∩𝒦(v),(2)
w∈𝒦(u)∖𝒦(v),(3)
w∈𝒦(v)∖𝒦(u),(4)
w∈X∖(𝒦(u)∪𝒦(v)).Suppose now that we have w∈𝒦(u)∖𝒦(v); then by Moreau’s decomposition theorem we get
(5.18)‖PK(u)(w)-PK(v)(w)‖=‖w-PK(v)(w)‖=‖PNC(v)(w)‖≤λ‖u-v‖,
where NC(v) is the normal cone of C at v. Consider now X=ℝ2, C=[0,ϵ]2, u=(0,0) and v=(ϵ,ϵ). It is clear that we have the following:
(5.19)TC(u)=R+2,TC(v)=R-2,NC(v)=R+2=TC(u).
So for any w∈NC(v) we get
(5.20)‖w‖≤λ‖u-v‖=2ϵλ.
Since w is arbitrary, let now w:=μw, for any μ>0. Then,
(5.21)‖μw‖≤λ‖u-v‖=2ϵλ
should be true for any μ>0. However this does not hold.Consider now the special case of a set-valued mapping𝒦 which is the translation of a closed, convex subset K:(5.22)K:x⟶K+v(x),wherev(x) is a vector linearly dependant on x; then problems (5.14) and (5.16) can be studied, under certain conditions, respectively, as a g-VI and an implicit PDS as shown below.If𝒦(x)=K+p(x) as done by Noor for type B PDS [24], we have the following equivalent formulations:(5.23)dx(t)dt=PTK+p(x)(x)(-J-1F(x))=PTK(g(x))(-J-1F(x)),x(0)=x0∈K,
where g(x)=x-p(x), assuming F(g(x))=F(x-p(x))=F(x). We can observe that if (dp(x))/dt=0, then (5.23) is equal to the implicit projected differential equation (3.29), and therefore Theorem 3.15 provides an existence result without assuming any kind of Lipschitz condition of the projection operator.


## 5.2.1. QVI

LetX be a generic H-space, D closed, convex, nonempty in X. Let 𝒦:D→2X with 𝒦(x) convex for all x∈D and F:𝒦→2X* a mapping.Let us introduce the following variational inequality:(5.14)findx∈K(x),〈F(x),y-x〉≥0,∀y∈K(x).
Note that in this case the set in which we are looking for the solution depends on x. For problem (5.14) we can provide the following existence result (see [17] or [33]).Theorem 5.4.
LetD be a closed convex subset in a locally convex Hausdorff topological vector space X. Let us suppose that(i)
𝒦:D→2D is a closed lower semicontinuous correspondence with closed, convex, and nonempty values,(ii)
C:D→2X′ is a monotone, finite continuous, and bounded single-valued map,(iii)
there exist a compact, convex, and nonempty setZ⊂D and a nonempty subset B⊂Z such that
(a)
𝒦(B)⊂Z,(b)
𝒦(z)∩Z≠∅,∀z∈Z,(c)
for everyz∈Z∖B, there exist ẑ∈𝒦(z)∩Z with 〈C(z),ẑ-z〉<0.
Then there existsx such that
(5.15)x∈K(x):〈C(x),y-x〉≥0,∀y∈K(x).In order to study the disequilibrium behavior of (5.14), we introduce now the following projected differential equation.Definition 5.5.
We call projected dynamical system associated to the quasivariational inequality (5.14) the solution set of the projected differential equation:
(5.16)dx(t)dt=PTK(x)(x)(-J-1F(x)),x(0)=x0∈K(x).Remark 5.6.
In general there are no existence results for problem (5.16). An existence result for a particular case of (5.16) has been given in [24], assuming the following fact.
Assumption 5.7.
LetXbe a pivot H-space. For allu,v,w∈X,P𝒦(u)satisfies the condition(5.17)‖PK(u)(w)-PK(v)(w)‖≤λ‖u-v‖,
where λ>0 is a constant.
However, this assumption fails to be true. One counterexample is as follows. We denote byC a closed convex set and we take u,v∈C; we denote by 𝒦(u)=TC(u) and by 𝒦(v)=TC(v) the tangent cones of C at u and v.
In fact,w∈X can only be chosen in one of the following four situations:(1)
w∈𝒦(u)∩𝒦(v),(2)
w∈𝒦(u)∖𝒦(v),(3)
w∈𝒦(v)∖𝒦(u),(4)
w∈X∖(𝒦(u)∪𝒦(v)).Suppose now that we have w∈𝒦(u)∖𝒦(v); then by Moreau’s decomposition theorem we get
(5.18)‖PK(u)(w)-PK(v)(w)‖=‖w-PK(v)(w)‖=‖PNC(v)(w)‖≤λ‖u-v‖,
where NC(v) is the normal cone of C at v. Consider now X=ℝ2, C=[0,ϵ]2, u=(0,0) and v=(ϵ,ϵ). It is clear that we have the following:
(5.19)TC(u)=R+2,TC(v)=R-2,NC(v)=R+2=TC(u).
So for any w∈NC(v) we get
(5.20)‖w‖≤λ‖u-v‖=2ϵλ.
Since w is arbitrary, let now w:=μw, for any μ>0. Then,
(5.21)‖μw‖≤λ‖u-v‖=2ϵλ
should be true for any μ>0. However this does not hold.Consider now the special case of a set-valued mapping𝒦 which is the translation of a closed, convex subset K:(5.22)K:x⟶K+v(x),wherev(x) is a vector linearly dependant on x; then problems (5.14) and (5.16) can be studied, under certain conditions, respectively, as a g-VI and an implicit PDS as shown below.If𝒦(x)=K+p(x) as done by Noor for type B PDS [24], we have the following equivalent formulations:(5.23)dx(t)dt=PTK+p(x)(x)(-J-1F(x))=PTK(g(x))(-J-1F(x)),x(0)=x0∈K,
where g(x)=x-p(x), assuming F(g(x))=F(x-p(x))=F(x). We can observe that if (dp(x))/dt=0, then (5.23) is equal to the implicit projected differential equation (3.29), and therefore Theorem 3.15 provides an existence result without assuming any kind of Lipschitz condition of the projection operator.


## 6. Conclusions

We show in this paper that previous results of existence of projected dynamical systems can be generalized to two new classes, namely, thenon-pivot and the implicit PDS. The generalizations came as needed to study a more realistic traffic equilibrium problem, as well as to study the relations between an implicit PDS and a class of variational inequalities as previously introduced in [24] as an open problem.



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*Source: 508570-2012-06-19.xml*