2. Antagonistic Matrix Stratified Games
1°. This section contains basic information from the theory of finite antagonistic (matrix) games. The existence theorem of the equilibrium situation in the class of mixed strategies, the properties of optimal mixed strategies, and methods for solving matrix games are well-established areas of research.
The study of game theory commences with the most basic static model: a matrix game in which two players engage, the set of strategies available to each player is finite, and the gain of one player is equal to the loss of the other. System
where
and
are nonempty sets and the function
, is called an antagonistic stratified game in normal form. The elements
and
are called the strategies of ordinal players 1 and 2 respectively in the game
, the elements of the Cartesian product
(i.e., the pair of strategies
where
and
) are situations, and the function
is the win function of player 1. The payoff of player 2 in situation
is assumed to be equal to
; therefore, the function
is also is called the win function of the game
itself, and the game
is called a zero-sum game.
Thus, using the accepted terminology, to define a game it is necessary to define the sets of strategies of the ordinal players 1 and 2, and also the winning function , defined on the set of all situations .
The stratified game is interpreted as follows. Ordinal players simultaneously and independently choose strategies . In the substratum game, the ordinal player 1 then receives a payoff equal to , and ordinal player 2 receives The elements are called the strategies of cardinal players in the stratified game .
In a stratified game, the elements are referred to as the strategies of cardinal players. The superstratum game pertains to the strategies for cardinal players that ensure the maximal and minimal values of the substratum payoff function. When there is only one cardinal player, the superstratum game reduces to the optimization problem. In contrast, when there are two or more cardinal players, their interests may be in opposition, resulting in what is known as an antagonistic game.
2°. This [section will focus on antagonistic games in which the sets of strategies available to the ordinal players are finite. The following definition is proposed: Antagonistic games in which both ordinal players possess finite strategy sets are designated as substratum matrix games.
In the matrix game, ordinal player 1 is assumed to have only strategies. The set of strategies available to the first ordinal player, , must be ordered, that is, a one-to-one correspondence must be established between and . The same process must be repeated for the second ordinal player, with and . The sets and are then ordered in a one-to-one correspondence with and , respectively, where and are finite sets of cardinalities and , respectively.
The substratum matrix game
is thus completely defined by the matrix
, where
is defined as follows:
where:
This is the rationale behind the name of the game, which is derived from the aforementioned matrix. In this instance, the game is realized as follows: Player 1 selects a row, , and player 2 (simultaneously with player 1 and independently of him) chooses a column,. Ordinal player 1 then receives a payoff, , and ordinal player 2 receives. In the event that the payoff is a negative number, it constitutes an actual loss for the ordinal player.
We denote the substratum game with win matrix by and refer to it as an game, in accordance with the dimensions of the matrix with the fixed values of strategies for cardinal players .
3°. The question of optimal behavior of players in an antagonistic game is worthy of consideration. It is reasonable to conclude that a situation in the game is optimal if deviating from it is not favorable for any of the players. Such a situation is referred to as an equilibrium, and the optimality principle based on the construction of an equilibrium situation is known as the equilibrium principle.
For an equilibrium situation to exist in the substratum game
, it is necessary and sufficient that there exist a minimax and a maximin:
and the equality is satisfied:
The Eq. (03) establishes a connection between the equilibrium principle and the minimax and maximin principles in an antagonistic game. Games in which equilibrium situations exist are called well-defined games. Therefore, this theorem establishes a criterion for a well-defined game and can be reformulated as follows. For a game to be well defined, it is necessary and sufficient that there exist and in Eq. (03) and the equality in minimax is satisfied.
If there exists an equilibrium situation, then the minimax is equal to the maximin, and according to the definition of the equilibrium situation, each player can communicate his optimal (maximin) strategy to the opponent and neither player can get an additional benefit from it.
4°. Now suppose that there is no equilibrium situation in the substratum game
. Since a random variable is characterized by its distribution, we will further identify a mixed strategy with a probability distribution on the set of pure strategies of ordinal players. Thus, the mixed strategy
of ordinal player 1 in the substratum game is an
-dimensional vector, which is constrained by the following equation:
where the
norm in the real vector space
is defined as:
Similarly, the mixed strategy y of player 2 is an
-dimensional vector:
The positive natural number determines the class of the game. Note, that the numbers n and m must not equal
5°. If
, the values
and
are the probabilities of choosing pure strategies
and
, respectively, when ordinal players use mixed strategies
and
. Let us denote by
and
the sets of mixed strategies of the first and second players, respectively. It is easy to see that the set of mixed strategies of each player is a compact in the corresponding finite-dimensional Euclidean space (a closed, bounded set). A mixed set represents an extension of the pure strategy space available to the player. An arbitrary matrix game is well defined within the class of mixed random strategies. The von Neumann theorem of matrix games states, that every matrix game has an equilibrium situation within the context of mixed strategies [
9]. The cited literature provides an overview of the methods used to evaluate game values. The setting
is typical for the application of game theory fields of economics, political science, and the social sciences. This setting reflects the fact that the mixed strategy for ordinal players is simply a probability distribution over their pure strategies. The probability of any event must be positive, and the total probability of all events must be one. Consequently, any mixed strategy must adhere to the following conditions:
6°. If
, the values
and
are the Euclidian coordinates of vector strategies
,
of the ordinal players. The Euclidean length of a vector
in the real vector spaces
and
are given by their Euclidean norms:
As illustrated in the aforementioned examples, this scenario is typical in the game formulations of engineering and physical applications. In such applications, the module of actions for the ordinal players are restricted. The modules of strategy vectors are less or equal than one:
With the definitions (04) and /25), the pay-off function of the matrix game on the lower substratum level reads:
The Lagrangian combines the pay-off function (09) with the constraints (08), taken with the non-negative multipliers
:
For the pay-off function (09) with the conditions (08), the equilibrium state
satisfies the equations:
The resolution of the Eq. (11) reads:
The left sides of Eqs. (12) contain two auxiliary matrices:
The matrix
is
square Hermitian matrix. The matrix
is
square Hermitian matrix. If follows from Theorem 2.8, Sect. 2.4, [
10], that both matrices
and
have the same nonzero eigenvalues, counting multiplicity. The matrices
and
are positive-semidefinite (Theorem 7.3, Sect. 7.1, ibid). The number of zero eigenvalues of
and
is at least
. Let
is the matrix with the smallest dimensions of
and
. In other words,
Generally saying, the matrix
is positive-semidefinite. The eigenvalues of the matrix
are:
If , then the matrices are equal = and have the same set of eigenvalues.
If
is positive definite, the number of its zero eigenvalues is exactly
. The eigenvalues of the positive definite matrix
are:
From Eq. (12) follows, that
. Finally,
Consequently, every matrix quadratic game has the equilibrium situations within the context of mixed strategies. Each equilibrium situation has one of the game values (14).
4. Optimization Games with one “Cardinal Player”
The results of the aforementioned section can be generalized for self-adjoint positive definite differential operators. The mechanical system described by the equilibrium equations is to be considered in the following form:
The self-adjoint positive definite operator describes the state of the system. In Eq. (25), is the scalar function of state variables and is the scalar function of the external loads of player “nature”. All values are determined in some domain . In one-dimensional case, the domain could be thought as an open interval.
In structural optimization, there are definite “ordinal players”. These players can change the strategy in course of the game playing, such that these players will be referenced as the “ordinal players”. In the simplest case, there is one “ordinal player”. The vector of “nature” loads should belong to a set of admissible external loads:
Besides the “ordinal players”, there is another player. This player will be referenced as “cardinal player”. The “cardinal player” owns the constructional “design variables”. The differential operator
depends upon the shape of two- or three-dimensional structural element. For example, the function
describes the mechanical properties along the length of the element. This is the unknown scalar or vector function. Thus, the coefficients of operator depend on
:
As usual, the certain isoperimetric conditions restrict the possible designs. For example, the total volume of the element could be restricted:
The pay-off functional is the functional of design and loads of both players:
This functional characterizes the essential mechanical characteristic of the structure, for example, compliance, period of vibration, maximal stress, etc. Putting it roughly, the natural aim of the “pilot” and “designer” is to minimize the functional for all possible actions of “nature” .
The upper and lower game values are defined as follows:
The minimax theorem states that, in general,
If the upper value of the game is equal to the lower value, the common value of minimaxes
and
is called the value of the game:
In this manuscript, the pay-off functional will be the stored elastic energy of the structural element (Reddy [
16], 2002). This functional has the physical meaning of integral compliance) . Thus, the game with the pay-off functional (29)
In Eq. (33) the symbol
stays for the bilinear form, or scalar product, satisfying:
The solution to equation (25) may be expressed in the following form:
For the purposes of this analysis, the restriction (26) will be assumed in quadratic form:
In Eq. (35), the operator
is the symbolical inverse of the
operator.
2°. Following the substitution of (35) into Eq. (33), the bi-linear payoff functional is expressed as follows:
The objective is to reduce the stored energy (38), given the restricted resources (36).
The expression
stays for the average elastic energy, which cause the stochastic actions of “nature” under the stochastic compensating action of “operator”. The determination of the minimal value of the game
is reduced to an ordinary optimization problem with the unknown design function
. The optimization game with the stored elastic energy as the payoff function could be referred to as a "compliance game."
Using the variational property of eigenvalues [
17], one can obtain the equivalent expression:
In Eq. (39a),
is the minimal eigenvalue of the operator
:
is the maximal eigenvalue of the operator
:
The formulas (40a) and (40b) manifest in the sense of strategies of „nature“
. For the symmetric game both strategies match. The strategy of both concurrent players is given by the eigenvector of operator
, which corresponds to its eigenvalue
:
In other words, this is the load that results in the greatest structural response among all permissible loads, satisfying condition (36). Consequently, at least the upper value of the game could be determined in all cases. The application of the aforementioned considerations to structural optimization problems will be discussed in the following sections.
5. Game Formulation for the Beam Subjected to Arbitrary Bending Moments
1°. In the classical game theory, the games played over the unit square are considered as a generalization of matrix games. The pay-off function in "game played over the unit square" is thus defined on the unit square [
18]. In this case, , a single continuous variable was retained for each individual due to the limitations imposed on the strategies of each player:
A comparable interpretation will be made from the perspective of game theory with regard to the optimization tasks involving an infinite number of design parameters for each player. Instead of vectors , ascend the functions . In lieu of the pay-off function, the pay-off functional emerges.
2°. Consider the beam of a certain cross-section. The beam, or rod is placed horizontally along x axis. The beam is subjected to an external load
distributed perpendicularly to a longitudinal axis of the element. The applied transverse load
is
a priori unknown. When a transverse load is applied on it, the beam deforms and stresses develop inside it. According to the Euler–Bernoulli theory of slender beams, the equation describing beam deflection can be presented as [
19]:
is the Young's modulus,
is the area moment of inertia of the cross-section,
is the internal bending moment in the beam. The quantity
in the above equation stays for the bending stiffness of the beam. The bending moment
is two times continuously differentiable function on
The area moment of inertia of the cross-section is given by the relation:
where
is the shape exponent,
is the shape factor. The shape factor depends on the cross-sectional shape. The admissible cross-sectional area of the rod is the scalar function:
The function is two times continuously differentiable. The cross-sectional area
plays the role of the strategy of “designer”. The shape exponent
takes the values of 1, 2 and 3 (Banichuk [
20], 1990). In all these cases the game will be convex and both players have the pure strategies. The case
corresponds to a similar variation of the form of the cross-section. The areas, area moment of inertia [
21] and shape factors for the similar variation of the form of the cross-section
=2 are shown in
Table 1. These formulas are valid for both a horizontal and a vertical axis through the centroid, and therefore are also valid for an axis with arbitrary direction that passes through the origin of the regular cross-sections. The
a priori unknown cross-sectional area
is the variable function along the span of the beam.
For brevity, the authors use for integral of an arbitrary function
the symbolization:
The boundary conditions must be prescribed as well. For the easiness, the rod is hinged at the end
and clamped at the end
. The boundary conditions are (Biezeno, Grammel, [
22]).
3°. The pay-off functional
is the integral distortion of the structural element. Reasonably is to express the integral distortion as the double stored elastic energy
:
The game problem is defined exactly as already presented for the general
status quo. There are two active ordinal players, “nature” and “operator”, who behave recurrently and stochastically. The player (“nature”) can apply an arbitrary admissible external load
in order to affect the maximal elastic energy
. Due to the symmetry of game, the “operator” applies the opposite moments
, which compensate the deformation caused by “nature”. The total efforts of the “nature” and “operator” are restricted:
Admissible is any effort of “nature” and “operator”, which is a continuously differentiable function, satisfies (43) and certain boundary conditions. Because the quadratic norm (43) is restricted, the sign of the moment plays statistically no role. The game will be symmetric in sense of Nash [
23]
The third player, (“designer “) attempts to select the most appropriate shape , which will guarantee the smallest deformation energy during the exploitation of the structural element. Once completed, the design remains unaltered over the period of exploitation. That’s why the “designer” is considered as the “passive” player, contrarily to the both other players, “nature” and “operator”.
The stiffest design corresponds to the minimal integral measure of deformation, which is presented by the stored energy
. The “designer” attempts to withstand the deformation are also limited. Namely, material volumes of all competing designs of the rods lesser than the certain, fixed volume of material
:
The conflict leads to the antagonistic game formulation of the optimization task. This game is referred to as the functional game , because the values of the game depends on the function
.According (4.23), the optimal value of game in mixed strategies is equal to:
The symbol
in (45) signifies the minimal eigenvalue of the game of the ordinary differential equation:
with the boundary conditions:
The optimization game with the minimal eigenvalue as the pay-off function could be referred to as “eigenvalue game” . From (45) and (46) follows, that the maximization of the upper value of the game reduces to the maximization of the minimal eigenvalue:
The cross-section for the Nash equilibrium state will be designated with the capital letter
, leaving the small letter
for any admissible cross-section function. The optimal cross-section presents the strategy for the active players. In other words, the beam with the thickness distribution
possesses the guaranteed stiffness:
The eigenvalue problem (46a), (46b) is self-adjoint. The conditions (46b) are of the Sturm type; see (Hazewinkel, Michiel [
24] ,2001), (Zettl [
25], 2005). There exists an infinite set of eigenvalues, all eigenvalues are real and positive and can be arranged as a monotonic sequence, and each eigenvalue is simple:
The Rayleigh's quotient is:
In Eq. (50a), the admissible functions are all functions, having piecewise continuous first derivatives, satisfying the boundary conditions (46b). In the Rayleigh's quotient stays the admissible moment of both active players: .
Among all admissible strategies of „nature”, the most favorable strategy set for “nature” is
. This choice delivers the minimal value for the Rayleigh's quotient:
The favorable strategy set which minimizes Rayleigh's quotient, increases according to (45) the energy of deformation from (42b) under its condition (43).
The “designer” has the opposite task. The necessity of “designer” is to minimize the energy of deformation for all admissible
under his condition (44). This task is equivalent to the maximization of the Rayleigh's quotient with the restriction (43) [
26,
27,
28]. The Lagrangian functional is the sum of (50b) and (44):
Here
represents the Lagrange multiplier of the variational calculus problem. The variation of the Hamiltonian functional reads:
The nullification of the derivative
leads to the necessary optimality condition . The strategies in the state of Hash equilibrium are
,
, and consequently:
From the viewpoint of the „designer”, the thickness distribution must satisfy the necessary optimality condition (51).
From the Noether’s viewpoint, the Hamiltonian functional is the Noether’s charge . The stationarity of Noether’s charge expresses the equilibrium of the opposite interests of both players. The condition (51) symbolizes the Noether’s current , which should be constant along the span of the structural element in the equilibrium situation of game. From the viewpoint of “designer”, the beam with the thickness distribution guarantees the highest effectiveness for the most unfavorable effort of the opposite player (“operator”).
4°. The next task is to determine the Nash equilibrium strategies for the “designer”
and for the „nature“
. For briefness of formulas, we put temporarily
. The governing equation is the nonlinear ordinary differential equations of the second order:
In each of Eqs. (52) to (62),
signifies the minimal eigenvalue (49) in the Nash equilibrium point:
The boundary conditions (41) could be proved to be optimal for boundary conditions of the Sturm type. The equilibrium conditions of the game task are equivalent to the necessary optimality condition for a column’s Euler buckling load [
29]. The substitution of Eq. (51) into (52) gives:
The solution of the boundary value problem (41), (53) determines the strategy of „nature“
. With this solution, highest possible eigenvalue (52) and, consequently, the upper value of the game have to be evaluated:
The dependent variable
and independent variable
of the equation (53) are to be exchanged. In the new variables
, the Eq. (53) turns into the Emden-Fowler equation
The Emden-Fowler equation (54) is the special case of Eq. (A.1) with the following parameters (see Appendix):
The closed-form solution outcomes for an arbitrary acceptable value of the shape exponent
. According to Eq. (A.3), the general solution of Eq. (54) for
is:
The symbols
and
stay for the integration constants. The integration constants are to evaluated from the solution of the nonlinear transcendental equations. To avoid the solutions of the nonlinear equations, the authors prefer the symmetry sights. Specifically, the sense of the constant
is the moment on the end
:
Due to the symmetry the equations with respect to the point
, the function
must be an even function of the variable
. This condition fixes the relation between integration constants:
With this value, the integral (56) evaluates in the closed form. For the shape exponent
and
, the solution reads with Eq. (A.15) as:
The Eq. (57a) presents the axial coordinate
as the function of the new independent parameter
. For
the hypergeometric function from Eq. (57b) expresses in terms of elementary functions (
Table 2). The solution is bulky and is omitted for briefness. According the boundary conditions (41), the dimensionless moment
vanishes on the hinged end:
. From this condition, the length of the rod could be determined as the function of an unknown integration constant
. Because the length
of the rod is known, the unknown constant
evaluates from the solution of the equation:
From its solution, the integration constant
follows as:
In its turn, the integral of the cross-area results from Eq. (A.15) and
To find the volume of the element, the authors evaluate the proper integral of the cross-section area
For all values of
, higher that one, the volume of the optimal structural element will be:
The authors define one another constant that was referred above as a total stiffness
of the structural element. The total stiffness
of the beam expresses as an integral of the moment of inertia of the cross-sections along the length
of the beam. To find the total stiffness of element in the Nash equilibrium state, the authors evaluate another proper integral. From Eq. (A.15), follows the integral of the energy density
as:
According to Eq. (60), the elastic energy density is constant over the length of the structural element.
Finally, the eigenvalue
equals in the Nash equilibrium point to:
The Eq. (59), (60), (61) characterize the principal mechanical properties in the Nash equilibrium state.
For the state of Nash equilibrium, the eigenvalue depends on the known constants:
6°. Our next task is to establish the relation between the eigenvalues
and
. In the convex case
, the relation could be rigorously proved as the certain isoperimetric inequality. For this purpose, the lowest eigenvalues for two different thickness functions
and
are the minimal values of the two corresponding Rayleigh's quotients:
The Rayleigh's quotients are the functionals of functions
and
or
correspondingly. The functions
or
are assumed in this section to be fixed. The function
is an arbitrary admissible function, which is differentiable and satisfies boundary conditions (41). The critical points of a functional is that point where the functional attains a minimum (or maximum) in the presence of constraints [
30]. We therefore examine conditions when a functional attains a minimum. For the thickness function
the critical point of the Rayleigh's quotient
is the eigenfunction
. To the eigenfunction
corresponds the lowest eigenvalue
. The guarantees, that:
Analogously, for the thickness function
the critical point of the Rayleigh's quotient
is the eigenfunction
. To this function corresponds the lowest eigenvalue
:
The right sides of the equations (63a) and (63b) must be compared. In order to state the desired isoperimetric inequality, the following auxiliary inequality have to be approved:
The numerators of the fractions to the left and right of the auxiliary inequality (64a) are identical. The denominators (64a) are, however, different. Thus, the denominators should be compared. The inequality for denominators, that has to be proven, reads:
At this point, the optimality condition (51) with
will be used:
Namely, the substitution of the optimality condition (51) into (64b) delivers the inequality for
:
The equality in (65) takes place only for . The validity of the yet suspected inequality (65) follows directly from the Hölder inequality (A.13). Consequently, from (A.11) and (A.13) follows the inequality for denominators (65) and finally the desired inequality (63).
Combining (63) and (65) delivers:
Consequently, it was proved that for all arbitrary
the eigenvalue is less than
:
The equality in Eq. (66) attained only for the optimal beam, which has the optimal shape and the maximal possible volume of material . For shape exponent, the game is convex. Generally, the game will be convex for any convex function .
Finally, the beam, that obeys the necessary optimality conditions (51), delivers the lowest possible upper value of the game:
Consequently, the value of functional game
for the optimal distribution of thickness
is lower than the pay-off functional (distortion energy) for an arbitrary distribution of thickness
of the same volume:
If the volume of an arbitrary thickness distribution is lower, follows the stronger inequality:
The relations (67a), (67b) and (67c) solve the “superstratum” game with the undefined, but constrained (43) bending moment function. From the viewpoint of “designer”, the optimal distribution of thickness
guarantees the best compromise for the most unfavorable for the “designer” action of “nature”. This compromise is the equilibrium in the functional game
Figure 3
6. Game Formulation for the Rod Subjected to Arbitrary Torque
1°. Consider the rod with the circular cross section, subjected to the positive distributed torque along its axis. The torque distribution is arbitrary, but the quadratic integral of the torque is fixed:
The governing equation for this problem is similar to Eq. (45), but the order is twice lower [
31]. In this case, the symbol
in (45) signifies the minimal eigenvalue of the game of the ordinary differential equation:
with the boundary conditions:
The optimal cross-section will be designated with the capital letter
, leaving the small letter
for any admissible cross-section function. The optimal cross-section presents the strategy for the “nature”. In other words, the rod with the thickness distribution
possesses the guaranteed stiffness:
The eigenvalue problem (68a), (68b) is self-adjoint. The conditions (68b) are again of the Sturm type. Once again, there is an infinite set of simple eigenvalues, all eigenvalues are real and positive and can be arranged as a monotonic sequence:
The Rayleigh's quotient is:
In Eq. (70a), the admissible functions are all functions, having piecewise continuous first derivatives, satisfying the boundary conditions (68b). In the Rayleigh's quotient stays the admissible twist of “nature”: .
2°. Among all admissible strategies of “nature”, the most favorable strategy set for “nature” is
. This choice delivers the minimal value for the Rayleigh's quotient:
The favorable strategy set which minimizes Rayleigh's quotient, increases according to (65) the energy of deformation from (62b) under its condition (63).
The “designer” has the opposite task. The necessity of “designer” is to minimize the energy of deformation for all admissible
under his condition (64). This task is equivalent to the maximization of the Rayleigh's quotient with the restriction (63). The Lagrangian functional is the sum of (70b) and (64):
Here
represents the Lagrange multiplier of the variational calculus problem. The variation of the Lagrangian functional reads:
The nullification of the derivative
leads to the necessary optimality condition . The strategies in the state of Hash equilibrium are
,
, and consequently:
From the viewpoint of the “designer”, the thickness distribution
must satisfy the necessary optimality condition (71). Remarkably, that the equilibrium conditions of the game task are equivalent to the necessary optimality condition for the twist divergence of a wing [
32,
33].
3°. The next task is to determine the Nash equilibrium strategies for the “designer”
and for the „nature“
. For briefness of formulas, we put temporarily
. The governing equation is the nonlinear ordinary differential equations of the second order:
In each of Eqs. (72) to (82),
signifies the minimal eigenvalue (69) in the Nash equilibrium point:
The substitution of Eq. (71) into (72) gives:
The solution of the boundary value problem (68), (73) determines the strategy of „nature“
. The dependent and independent variables of the equation (73) are to be exchanged. In the new variables, the Eq. (73) turns into the Emden-Fowler equation
The Emden-Fowler equation (74) is the special case of Eq. (A.1) with the following parameters (see Appendix):
The closed-form solution outcomes for an arbitrary value of the shape exponent
. The solution of (76) reads with Eq. (A.15) as:
Figure 4
The Eq. (77) presents the axial coordinate
as the function of the independent parameter
. For
the hypergeometric function from Eq. (77) expresses in terms of elementary functions (
Table 4).
4°. The next task is to establish the relation between the eigenvalues
and
. In the convex case
, the relation could be rigorously proved as the certain isoperimetric inequality. For this purpose, the lowest eigenvalues for two different thickness functions
and
are the minimal values of the two corresponding Rayleigh's quotients:
The Rayleigh's quotients are the functionals of functions
and
or
correspondingly. The functions
or
are assumed in this section to be fixed. The function
is an arbitrary admissible function, which is differentiable and satisfies boundary conditions (61). The critical points of a functional is that point where the functional attains a minimum (or maximum) in the presence of constraints. We therefore examine conditions when a functional attains a minimum. For the thickness function
the critical point of the Rayleigh's quotient
is the eigenfunction
. To the eigenfunction
corresponds the lowest eigenvalue
. The guarantees, that:
Analogously, for the thickness function
the critical point of the Rayleigh's quotient
is the eigenfunction
. To this function corresponds the lowest eigenvalue
:
The right sides of the equations (83a) and (83b) must be compared. In order to state the desired isoperimetric inequality, the following auxiliary inequality have to be approved:
The denominators of the fractions to the left and right of the auxiliary inequality (84a) are identical. The nominators (84a) are, however, different. Thus, the nominators should be compared. The inequality for nominators, that has to be proven, reads:
At this point, the optimality condition (71) with
will be used:
Namely, the substitution of the optimality condition (71) into (84b) delivers the inequality for
:
The equality in (85) takes place only for . The validity of the yet suspected inequality (85) follows directly from the Hölder inequality (A.14). Consequently, from (A.11) and (A.14) follows the inequality for nominators (85) and finally the desired inequality (83).
Combining (83) and (85) delivers:
Consequently, it was proved that for all arbitrary
the eigenvalue is less than
:
The equality in Eq. (86) attained only for the optimal beam, which has the optimal shape and the maximal possible volume of material . For shape exponent, the game is convex. Generally, the game will be convex for any convex function .
The rod, that obeys the necessary optimality conditions (71), delivers the lowest possible upper value of the game:
Consequently, the value of functional game
for the optimal distribution of thickness
is lower than the pay-off functional (distortion energy) for an arbitrary distribution of thickness
of the same volume:
If the volume of an arbitrary thickness distribution is lower, follows the stronger inequality:
The relations (87), (88) and (89) solve the “superstratum” game for the twisted beam with the undefined torque distribution.