Let’s say I have three classes A, B and C.
All have a Now from C’s I tried 

You can’t even use reflection. Something like
would lead to an It seems you need help from the B class (e.g. by defining a That said, it looks like a design problem if you try something like this, so it would be helpful to give us some background: Why you need to do this? 

You can’t – because it would break encapsulation. You’re able to call your superclass’s method because it’s assumed that you know what breaks encapsulation in your own class, and avoid that… but you don’t know what rules your superclass is enforcing – so you can’t just bypass an implementation there.


You can’t do it in a simple manner. This is what I think you can do: Have a bool in your class B. Now you must call B’s foo from C like Hope this helps. 

To quote a previous answer “You can’t – because it would break encapsulation.” to which I would like to add that: However there is a corner case where you can,namely if the method is Having a For This is the corner case I am exploring in my answer:
The answer remains still No, but just wanted to show a case where you can, although it probably wouldn’t make any sense and is just an exercise. 

Yes you can do it. This is a hack. Try not to design your program like this.


It’s not possible, we’re limited to call the superclass implementations only.


I smell something fishy here. Are you sure you are not just pushing the envelope too far “just because you should be able to do it”? Are you sure this is the best design pattern you can get? Have you tried refactoring it?


I had a problem where a superclass would call an top class method that was overridden. This was my workaround… //THIS WOULD FAIL CALLING SUPERCLASS METHODS AS a1() would invoke top class METHOD
//THIS ENSURES THE RIGHT SUPERCLASS METHODS ARE CALLED //the public methods only call private methods so all public methods can be overridden without effecting the superclass’s functionality.
I hope this helps. ðŸ™‚


Before using reflection API think about the cost of it. It is simply easy to do. For instance: C subclass of B and B subclass of A. Both of three have method methodName() for example.
Run class C Output will be: Class A Class C Instead of output: Class A Class B Class C 

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stackoverflow.com
This page provides information for learning the ZZ method. ZZ is a modern method for speed solving the Rubik’s Cube, originally proposed by Zbigniew Zborowski in 2006. Michal Hordecki’s ZZ Page provides a good background and description of the ZZ method for those who aren’t already familiar with it.
Interpretation of the algorithms presented here requires familiarity with standard cube notation.
If you’re new to ZZ, the stripped down beginner version shows only the topics required to get you started.
Contents
 EOLine
 F2L
 LL
 Example Solves
 References
 List of Terms
EOLine
This stage orients all edges on the cube (EO) while simultaneously placing the DF and DB edges (Line). This reduces the cube to the group, meaning the rest of the cube can be solved by turning only the L, R and U faces.
EOLine takes an average of ~6.127 moves and a maximum of 9 moves, but is without doubt the most difficult part of the ZZ method. For this reason, it is often divided into two substages: (1) Edge orientation followed by (2) placement of the line edges. Also known as “EO+Line”.
Contents
 Edge Orientation Detection
 Edge Orientation Strategy
 Edge Orientation Cases
 Combining the EO and Line Phases
 Optimising EOLine
 Fingertricks and Looking Ahead Into F2L
 Example EOLine Solves
F2L – The First Two Layers
This stage completes the first two layers by building two 1x2x3 blocks on either side of the Line made in the previous stage. Because all edges are now oriented it is possible to complete this stage using just R, U and L moves.
Contents
 Basic Block Building Strategy
 1x2x2 Blocks
 1x1x2 Blocks
 Algorithms for Special Cases
 Dlayer 1x1x2s
 Corner Already Placed
 Edge Already Placed
 Connected Cubies
 Badly Connected Blocks
 Multiblocking
 Openslotting
 ZZF2L Lookahead / General Tips
LL – The Last Layer
Because edge orientation is solved during EOLine and preserved during F2L, the last layer edges will always be oriented. This provides great number of options, ranging from a simple 20 algorithm 2look system, all the way up to a 1look system with up to 493 algorithms to learn.
Contents
 OCLL/PLL
 COLL/EPLL
 OCELL/CPLL
 ZBLL
 ZZLL
 Winter Variation
 ZZCT
 MGLS
 Blah’s Method
Example Solves
These YouTube links below show examples of ZZ in action:
 3x3x3 17.58s Lodz Open 2011 – by Zbigniew Zborowski
 8.13 OH single – by Phil Yu
 10.88 Official Average, 9.40 Single – by Eli Lifland
 11.90 ZZ2h 10 of 12 – by Phil Yu
 9.95s ZZ solve – by Sam Hanna
 3x3x3 Rubik’s Cube 15.06 average – by Mateusz Kurek
 Two ZZ Solves – 14.37, 15.91 – by yurivish
 15.82 ZZ solve – by nnitay6669
 16.73 Avg of 12, with reconstructions – by Conrad Rider
Click here for some detailed ZZ walkthroughs…
References & Resources
 Zbigniew Zborowski’s polish ZZ page (applet warning!)
 Michal Hordecki’s ZZ Page
 speedsolving.com: ZZ/ZB Home Thread
 speedsolving.com: ZZ Speedcubing Method
 speedsolving.com: ZZ Cubers
 speedsolving.com wiki: ZZ Method
 speedsolving.com: New method?
 YouTube: ZZ method tutorial
 YouTube: EOLine tutorial
 Bernard Helmstetter’s Move Count Statistics
 Cube Explorer
 Lucas Garron’s Algorithm Animator
 ZZF2L Move Count Analysis (by Lars Vandenbergh)
 speedsolving.com: ZZF2L Move Count
List of Terms
 bad edge A misoriented edge
 DFace The lower surface of the cube
 DLayer The lower 1x3x3 block of cubies
 DB The downback edge
 DF The downfront edge
 EO Edge Orientation
 EO+Line Edge Orientation followed by Line placement
 EOLine Edge Orientation and Line placement executed as a single step
 F2L First Two Layers: The lower 2x3x3 block of the cube
 HTM Half Turn Metric: Defines a quarter or half turn of any face as a single move
 line edges The DF and DB edges. When placed they form a line on the Dface
 LL Last Layer: The Ulayer
 midslice The middle horizontal layer of the cube, sandwiched between the U and D layers (also known as the Eslice)
 ZZ Zbigniew Zborowski: A Polish speedcuber and original proposer of the ZZ method
cube.crider.co.uk
This article is about asymptotic stability of nonlinear systems. For stability of linear systems, see
exponential stability
.
Various types of stability may be discussed for the solutions of differential equations or difference equations describing dynamical systems. The most important type is that concerning the stability of solutions near to a point of equilibrium. This may be discussed by the theory of Lyapunov. In simple terms, if the solutions that start out near an equilibrium point xe{displaystyle x_{e}} stay near xe{displaystyle x_{e}} forever, then xe{displaystyle x_{e}} is Lyapunov stable. More strongly, if xe{displaystyle x_{e}} is Lyapunov stable and all solutions that start out near xe{displaystyle x_{e}} converge to xe{displaystyle x_{e}}, then xe{displaystyle x_{e}} is asymptotically stable. The notion of exponential stability guarantees a minimal rate of decay, i.e., an estimate of how quickly the solutions converge. The idea of Lyapunov stability can be extended to infinitedimensional manifolds, where it is known as structural stability, which concerns the behavior of different but “nearby” solutions to differential equations. Inputtostate stability (ISS) applies Lyapunov notions to systems with inputs.
History
Lyapunov stability is named after Aleksandr Mikhailovich Lyapunov, a Russian mathematician who defended the thesis The General Problem of Stability of Motion at Moscow University in 1892. A. M. Lyapunov was a pioneer in successful endeavoring to develop the global approach to the analysis of the stability of nonlinear dynamical systems by comparison with the widely spread local method of linearizing them about points of equilibrium. His work, initially published in Russian and then translated to French, received little attention for many years. The mathematical theory of stability of motion, founded by A. M. Lyapunov, considerably anticipated the time for its implementation in science and technology. Moreover, he did not have doctoral students who followed the research in the field of stability and his own destiny was terribly tragic because of Russian revolution of 1917. For several decades the theory of stability sank into complete oblivion. The RussianSoviet mathematician and mechanician Nikolay Gurâ€™yevich Chetaev was first who realized the incredible magnitude of the discovery made by A. M. Lyapunov. Actually, his figure as a great scientist is comparable to the one of A. M. Lyapunov. The contribution to the theory made by N. G. Chetaev was so significant that many mathematicians, physicists and engineers consider him a Lyapunovâ€™s direct successor and the nextinline scientific descendant in the creation and development of the mathematical theory of stability.
The interest in it suddenly skyrocketed during the Cold War period when the socalled “Second Method of Lyapunov” (see below) was found to be applicable to the stability of aerospace guidance systems which typically contain strong nonlinearities not treatable by other methods. A large number of publications appeared then and since in the control and systems literature. More recently the concept of the Lyapunov exponent (related to Lyapunov’s First Method of discussing stability) has received wide interest in connection with chaos theory. Lyapunov stability methods have also been applied to finding equilibrium solutions in traffic assignment problems.
Definition for continuoustime systems
Consider an autonomous nonlinear dynamical system
xË™=f(x(t)),x(0)=x0{displaystyle {dot {x}}=f(x(t)),;;;;x(0)=x_{0}},
where x(t)âˆˆDâŠ†Rn{displaystyle x(t)in {mathcal {D}}subseteq mathbb {R} ^{n}} denotes the system state vector, D{displaystyle {mathcal {D}}} an open set containing the origin, and f:Dâ†’Rn{displaystyle f:{mathcal {D}}rightarrow mathbb {R} ^{n}} continuous on D{displaystyle {mathcal {D}}}. Suppose f{displaystyle f} has an equilibrium at xe{displaystyle x_{e}} so that f(xe)=0{displaystyle f(x_{e})=0} then
 This equilibrium is said to be Lyapunov stable, if, for every Ïµ>0{displaystyle epsilon >0}, there exists a Î´>0{displaystyle delta >0} such that, if â€–x(0)âˆ’xeâ€– such that if â€–x(0)âˆ’xeâ€–Î±>0,Î²>0,Î´>0{displaystyle alpha >0,beta >0,delta >0} such that if â€–x(0)âˆ’xeâ€–
en.wikipedia.orgThis article is about asymptotic stability of nonlinear systems. For stability of linear systems, see
exponential stability
.
Various types of stability may be discussed for the solutions of differential equations or difference equations describing dynamical systems. The most important type is that concerning the stability of solutions near to a point of equilibrium. This may be discussed by the theory of Lyapunov. In simple terms, if the solutions that start out near an equilibrium point xe{displaystyle x_{e}} stay near xe{displaystyle x_{e}} forever, then xe{displaystyle x_{e}} is Lyapunov stable. More strongly, if xe{displaystyle x_{e}} is Lyapunov stable and all solutions that start out near xe{displaystyle x_{e}} converge to xe{displaystyle x_{e}}, then xe{displaystyle x_{e}} is asymptotically stable. The notion of exponential stability guarantees a minimal rate of decay, i.e., an estimate of how quickly the solutions converge. The idea of Lyapunov stability can be extended to infinitedimensional manifolds, where it is known as structural stability, which concerns the behavior of different but “nearby” solutions to differential equations. Inputtostate stability (ISS) applies Lyapunov notions to systems with inputs.
History
Lyapunov stability is named after Aleksandr Mikhailovich Lyapunov, a Russian mathematician who defended the thesis The General Problem of Stability of Motion at Moscow University in 1892. A. M. Lyapunov was a pioneer in successful endeavoring to develop the global approach to the analysis of the stability of nonlinear dynamical systems by comparison with the widely spread local method of linearizing them about points of equilibrium. His work, initially published in Russian and then translated to French, received little attention for many years. The mathematical theory of stability of motion, founded by A. M. Lyapunov, considerably anticipated the time for its implementation in science and technology. Moreover, he did not have doctoral students who followed the research in the field of stability and his own destiny was terribly tragic because of Russian revolution of 1917. For several decades the theory of stability sank into complete oblivion. The RussianSoviet mathematician and mechanician Nikolay Gurâ€™yevich Chetaev was first who realized the incredible magnitude of the discovery made by A. M. Lyapunov. Actually, his figure as a great scientist is comparable to the one of A. M. Lyapunov. The contribution to the theory made by N. G. Chetaev was so significant that many mathematicians, physicists and engineers consider him a Lyapunovâ€™s direct successor and the nextinline scientific descendant in the creation and development of the mathematical theory of stability.
The interest in it suddenly skyrocketed during the Cold War period when the socalled “Second Method of Lyapunov” (see below) was found to be applicable to the stability of aerospace guidance systems which typically contain strong nonlinearities not treatable by other methods. A large number of publications appeared then and since in the control and systems literature. More recently the concept of the Lyapunov exponent (related to Lyapunov’s First Method of discussing stability) has received wide interest in connection with chaos theory. Lyapunov stability methods have also been applied to finding equilibrium solutions in traffic assignment problems.
Definition for continuoustime systems
Consider an autonomous nonlinear dynamical system
xË™=f(x(t)),x(0)=x0{displaystyle {dot {x}}=f(x(t)),;;;;x(0)=x_{0}},
where x(t)âˆˆDâŠ†Rn{displaystyle x(t)in {mathcal {D}}subseteq mathbb {R} ^{n}} denotes the system state vector, D{displaystyle {mathcal {D}}} an open set containing the origin, and f:Dâ†’Rn{displaystyle f:{mathcal {D}}rightarrow mathbb {R} ^{n}} continuous on D{displaystyle {mathcal {D}}}. Suppose f{displaystyle f} has an equilibrium at xe{displaystyle x_{e}} so that f(xe)=0{displaystyle f(x_{e})=0} then
 This equilibrium is said to be Lyapunov stable, if, for every Ïµ>0{displaystyle epsilon >0}, there exists a Î´>0{displaystyle delta >0} such that, if â€–x(0)âˆ’xeâ€– such that if â€–x(0)âˆ’xeâ€–Î±>0,Î²>0,Î´>0{displaystyle alpha >0,beta >0,delta >0} such that if â€–x(0)âˆ’xeâ€–
en.wikipedia.org
 This equilibrium is said to be Lyapunov stable, if, for every Ïµ>0{displaystyle epsilon >0}, there exists a Î´>0{displaystyle delta >0} such that, if â€–x(0)âˆ’xeâ€– such that if â€–x(0)âˆ’xeâ€–Î±>0,Î²>0,Î´>0{displaystyle alpha >0,beta >0,delta >0} such that if â€–x(0)âˆ’xeâ€–