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Solve and minimize quadratic system of inequations with multiple objectives


Solve Quadratic Equation in C++Solving non concave quadratic function in matlab with constraintsMatlab - Using IFELSE statement to solve quadratic formulaSolve maximization problem of quadratic equation in MATLABsolving a quadratic equation with matrices in matlabquadratic optimization in R with both equality and inequality constraintsSolving quadratic congruenceFast quadratic minimizerMinimizing quadratic function subject to norm inequality constraintIs it possible to vectorize a function script in matlab?






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0















I have a set of inequalities that I have to solve.
These inequalities are related to circle packing, where I have N circles and all of them are interrelated between the rest of the circles.
Which functions could I use to solve this?
I have tried using solve in MATLAB but this doesn't work with inequalities.
E.g. given 3 circles, the number of inequalities is (N*(N-1))/2, so with 3 cirlces I have 3 equations.
Let's call them A, B and C, with radius 200, 300 and 400 respectively.
With A,B and C I have 3 different inequalities:



dist(a,b) >= 200+300
dist(b,c) >= 300+400
dist(c,a) >= 200+400


Where



dist(p1,p2) = sqrt((p1.x - p2.x)^2 + (p1.y - p2.y)^2)


I would like to get p1.x, p1.y, p2.x, p2.y, p3.x, p3.y such as their distances are the closest to the right part of the inequalities.



With 3 circles there's no problem because it'll be always a solution that satisfies and minimizes the inequalities, but the problem comes when I have a lot of circles, p.e. 10 circles.



The objectives in this case are:



dist(a,b) = 200+300
dist(b,c) = 300+400
dist(a,c) = 200+400


Now I will show you what is my idea to solve this:



syms x1 x2 x3 x4 x5 y1 y2 y3 y4 y5

//What I want to get
unknowns = [ x1 x2 x3 x4 x5 y1 y2 y3 y4 y5]

//Equations that must be satisfied
eqns(1) = sqrt((x1-x2)^2 + (y1-y2)^2) >= 300; // dist(1,2)>=300
eqns(2) = sqrt((x1-x3)^2 + (y1-y3)^2) >= 400; // dist(1,3)>=400
eqns(3) = sqrt((x1-x4)^2 + (y1-y4)^2) >= 350; // dist(1,4)>=350
eqns(4) = sqrt((x1-x5)^2 + (y1-y5)^2) >= 200; // dist(1,5)>=200
eqns(5) = sqrt((x3-x2)^2 + (y3-y2)^2) >= 320; // dist(2,3)>=320
eqns(6) = sqrt((x4-x2)^2 + (y4-y2)^2) >= 300; // dist(2,4)>=300
eqns(7) = sqrt((x5-x2)^2 + (y5-y2)^2) >= 330; // dist(2,5)>=330
eqns(8) = sqrt((x3-x4)^2 + (y3-y4)^2) >= 130; // dist(3,4)>=230
eqns(9) = sqrt((x3-x5)^2 + (y3-y5)^2) >= 120; // dist(3,5)>=400
eqns(10) = sqrt((x5-x4)^2 + (y5-y4)^2) >= 100; // dist(4,5)>=130

// My objective functions I want to optimize
objs(1) = sqrt((x1-x2)^2 + (y1-y2)^2) == 300; // dist(1,2)=300
objs(2) = sqrt((x1-x3)^2 + (y1-y3)^2) == 400; // dist(1,3)=400
objs(3) = sqrt((x1-x4)^2 + (y1-y4)^2) == 350; // dist(1,4)=350
objs(4) = sqrt((x1-x5)^2 + (y1-y5)^2) == 200; // dist(1,5)=200
objs(5) = sqrt((x3-x2)^2 + (y3-y2)^2) == 320; // dist(2,3)=320
objs(6) = sqrt((x4-x2)^2 + (y4-y2)^2) == 300; // dist(2,4)=300
objs(7) = sqrt((x5-x2)^2 + (y5-y2)^2) == 330; // dist(2,5)=330
objs(8) = sqrt((x3-x4)^2 + (y3-y4)^2) == 130; // dist(3,4)=230
objs(9) = sqrt((x3-x5)^2 + (y3-y5)^2) == 120; // dist(3,5)=400
objs(10) = sqrt((x5-x4)^2 + (y5-y4)^2) == 100; // dist(4,5)=130

// And now.. what can I use??

//Solve doesn't work...
solve(eqns, unknowns, IgnoreAnalyticConstraints)


I would expect that the result satisfies all the equations at the same time, and the distance between every pair of circles is the closest to the desired, but NEVER smaller than it.



Here it's a graphic example:
If I have this input,
Input with 6 terminal nodes



The output should be something like this, where the distances between every pair of terminal nodes are respected:
Output of 6 circles fixed



It's a circle packing problem with constraints, because all the circles must be as close between them as they are drawn in the input



The heuristic should be like:



1.- Add first circle



2.- Add second circle that is connected with the first and satisfying the required distance



3.- Add third circle that is connected with the first and second and satisfies the required distances



4.- ....



Maybe could I try also with bruteforce? I'm not sure



Thank you so much










share|improve this question


























  • solve will give you an exact solution not the most optimized solution. So if your equalities lead to an overdetermined system with no exact solution, solve will of course failed. For optimization with constraint there is the fmincon function. Take also into account that if you have multiple objectif functions that need to be optimized at the same time matlab cannot guess which function should be the more optimized ! I guess that there is certainly a better approach

    – obchardon
    Mar 27 at 16:35











  • Are these objective functions equally weighted? Are these being solved in a sequential order? Are you wanting to minimize the sum of all 10 objective functions? (or the weighted sum) Perhaps you could edit the question and add some clarification.

    – SecretAgentMan
    Mar 27 at 19:00











  • Thank you both of you! @obchardon I tried yesterday with genetic algorithm ga but nothing worked so I will try with fmincon function. @SecretAgentMan I edited it adding more information, thanks for your advices

    – Daniel Bermejo Sánchez
    Mar 28 at 10:16


















0















I have a set of inequalities that I have to solve.
These inequalities are related to circle packing, where I have N circles and all of them are interrelated between the rest of the circles.
Which functions could I use to solve this?
I have tried using solve in MATLAB but this doesn't work with inequalities.
E.g. given 3 circles, the number of inequalities is (N*(N-1))/2, so with 3 cirlces I have 3 equations.
Let's call them A, B and C, with radius 200, 300 and 400 respectively.
With A,B and C I have 3 different inequalities:



dist(a,b) >= 200+300
dist(b,c) >= 300+400
dist(c,a) >= 200+400


Where



dist(p1,p2) = sqrt((p1.x - p2.x)^2 + (p1.y - p2.y)^2)


I would like to get p1.x, p1.y, p2.x, p2.y, p3.x, p3.y such as their distances are the closest to the right part of the inequalities.



With 3 circles there's no problem because it'll be always a solution that satisfies and minimizes the inequalities, but the problem comes when I have a lot of circles, p.e. 10 circles.



The objectives in this case are:



dist(a,b) = 200+300
dist(b,c) = 300+400
dist(a,c) = 200+400


Now I will show you what is my idea to solve this:



syms x1 x2 x3 x4 x5 y1 y2 y3 y4 y5

//What I want to get
unknowns = [ x1 x2 x3 x4 x5 y1 y2 y3 y4 y5]

//Equations that must be satisfied
eqns(1) = sqrt((x1-x2)^2 + (y1-y2)^2) >= 300; // dist(1,2)>=300
eqns(2) = sqrt((x1-x3)^2 + (y1-y3)^2) >= 400; // dist(1,3)>=400
eqns(3) = sqrt((x1-x4)^2 + (y1-y4)^2) >= 350; // dist(1,4)>=350
eqns(4) = sqrt((x1-x5)^2 + (y1-y5)^2) >= 200; // dist(1,5)>=200
eqns(5) = sqrt((x3-x2)^2 + (y3-y2)^2) >= 320; // dist(2,3)>=320
eqns(6) = sqrt((x4-x2)^2 + (y4-y2)^2) >= 300; // dist(2,4)>=300
eqns(7) = sqrt((x5-x2)^2 + (y5-y2)^2) >= 330; // dist(2,5)>=330
eqns(8) = sqrt((x3-x4)^2 + (y3-y4)^2) >= 130; // dist(3,4)>=230
eqns(9) = sqrt((x3-x5)^2 + (y3-y5)^2) >= 120; // dist(3,5)>=400
eqns(10) = sqrt((x5-x4)^2 + (y5-y4)^2) >= 100; // dist(4,5)>=130

// My objective functions I want to optimize
objs(1) = sqrt((x1-x2)^2 + (y1-y2)^2) == 300; // dist(1,2)=300
objs(2) = sqrt((x1-x3)^2 + (y1-y3)^2) == 400; // dist(1,3)=400
objs(3) = sqrt((x1-x4)^2 + (y1-y4)^2) == 350; // dist(1,4)=350
objs(4) = sqrt((x1-x5)^2 + (y1-y5)^2) == 200; // dist(1,5)=200
objs(5) = sqrt((x3-x2)^2 + (y3-y2)^2) == 320; // dist(2,3)=320
objs(6) = sqrt((x4-x2)^2 + (y4-y2)^2) == 300; // dist(2,4)=300
objs(7) = sqrt((x5-x2)^2 + (y5-y2)^2) == 330; // dist(2,5)=330
objs(8) = sqrt((x3-x4)^2 + (y3-y4)^2) == 130; // dist(3,4)=230
objs(9) = sqrt((x3-x5)^2 + (y3-y5)^2) == 120; // dist(3,5)=400
objs(10) = sqrt((x5-x4)^2 + (y5-y4)^2) == 100; // dist(4,5)=130

// And now.. what can I use??

//Solve doesn't work...
solve(eqns, unknowns, IgnoreAnalyticConstraints)


I would expect that the result satisfies all the equations at the same time, and the distance between every pair of circles is the closest to the desired, but NEVER smaller than it.



Here it's a graphic example:
If I have this input,
Input with 6 terminal nodes



The output should be something like this, where the distances between every pair of terminal nodes are respected:
Output of 6 circles fixed



It's a circle packing problem with constraints, because all the circles must be as close between them as they are drawn in the input



The heuristic should be like:



1.- Add first circle



2.- Add second circle that is connected with the first and satisfying the required distance



3.- Add third circle that is connected with the first and second and satisfies the required distances



4.- ....



Maybe could I try also with bruteforce? I'm not sure



Thank you so much










share|improve this question


























  • solve will give you an exact solution not the most optimized solution. So if your equalities lead to an overdetermined system with no exact solution, solve will of course failed. For optimization with constraint there is the fmincon function. Take also into account that if you have multiple objectif functions that need to be optimized at the same time matlab cannot guess which function should be the more optimized ! I guess that there is certainly a better approach

    – obchardon
    Mar 27 at 16:35











  • Are these objective functions equally weighted? Are these being solved in a sequential order? Are you wanting to minimize the sum of all 10 objective functions? (or the weighted sum) Perhaps you could edit the question and add some clarification.

    – SecretAgentMan
    Mar 27 at 19:00











  • Thank you both of you! @obchardon I tried yesterday with genetic algorithm ga but nothing worked so I will try with fmincon function. @SecretAgentMan I edited it adding more information, thanks for your advices

    – Daniel Bermejo Sánchez
    Mar 28 at 10:16














0












0








0








I have a set of inequalities that I have to solve.
These inequalities are related to circle packing, where I have N circles and all of them are interrelated between the rest of the circles.
Which functions could I use to solve this?
I have tried using solve in MATLAB but this doesn't work with inequalities.
E.g. given 3 circles, the number of inequalities is (N*(N-1))/2, so with 3 cirlces I have 3 equations.
Let's call them A, B and C, with radius 200, 300 and 400 respectively.
With A,B and C I have 3 different inequalities:



dist(a,b) >= 200+300
dist(b,c) >= 300+400
dist(c,a) >= 200+400


Where



dist(p1,p2) = sqrt((p1.x - p2.x)^2 + (p1.y - p2.y)^2)


I would like to get p1.x, p1.y, p2.x, p2.y, p3.x, p3.y such as their distances are the closest to the right part of the inequalities.



With 3 circles there's no problem because it'll be always a solution that satisfies and minimizes the inequalities, but the problem comes when I have a lot of circles, p.e. 10 circles.



The objectives in this case are:



dist(a,b) = 200+300
dist(b,c) = 300+400
dist(a,c) = 200+400


Now I will show you what is my idea to solve this:



syms x1 x2 x3 x4 x5 y1 y2 y3 y4 y5

//What I want to get
unknowns = [ x1 x2 x3 x4 x5 y1 y2 y3 y4 y5]

//Equations that must be satisfied
eqns(1) = sqrt((x1-x2)^2 + (y1-y2)^2) >= 300; // dist(1,2)>=300
eqns(2) = sqrt((x1-x3)^2 + (y1-y3)^2) >= 400; // dist(1,3)>=400
eqns(3) = sqrt((x1-x4)^2 + (y1-y4)^2) >= 350; // dist(1,4)>=350
eqns(4) = sqrt((x1-x5)^2 + (y1-y5)^2) >= 200; // dist(1,5)>=200
eqns(5) = sqrt((x3-x2)^2 + (y3-y2)^2) >= 320; // dist(2,3)>=320
eqns(6) = sqrt((x4-x2)^2 + (y4-y2)^2) >= 300; // dist(2,4)>=300
eqns(7) = sqrt((x5-x2)^2 + (y5-y2)^2) >= 330; // dist(2,5)>=330
eqns(8) = sqrt((x3-x4)^2 + (y3-y4)^2) >= 130; // dist(3,4)>=230
eqns(9) = sqrt((x3-x5)^2 + (y3-y5)^2) >= 120; // dist(3,5)>=400
eqns(10) = sqrt((x5-x4)^2 + (y5-y4)^2) >= 100; // dist(4,5)>=130

// My objective functions I want to optimize
objs(1) = sqrt((x1-x2)^2 + (y1-y2)^2) == 300; // dist(1,2)=300
objs(2) = sqrt((x1-x3)^2 + (y1-y3)^2) == 400; // dist(1,3)=400
objs(3) = sqrt((x1-x4)^2 + (y1-y4)^2) == 350; // dist(1,4)=350
objs(4) = sqrt((x1-x5)^2 + (y1-y5)^2) == 200; // dist(1,5)=200
objs(5) = sqrt((x3-x2)^2 + (y3-y2)^2) == 320; // dist(2,3)=320
objs(6) = sqrt((x4-x2)^2 + (y4-y2)^2) == 300; // dist(2,4)=300
objs(7) = sqrt((x5-x2)^2 + (y5-y2)^2) == 330; // dist(2,5)=330
objs(8) = sqrt((x3-x4)^2 + (y3-y4)^2) == 130; // dist(3,4)=230
objs(9) = sqrt((x3-x5)^2 + (y3-y5)^2) == 120; // dist(3,5)=400
objs(10) = sqrt((x5-x4)^2 + (y5-y4)^2) == 100; // dist(4,5)=130

// And now.. what can I use??

//Solve doesn't work...
solve(eqns, unknowns, IgnoreAnalyticConstraints)


I would expect that the result satisfies all the equations at the same time, and the distance between every pair of circles is the closest to the desired, but NEVER smaller than it.



Here it's a graphic example:
If I have this input,
Input with 6 terminal nodes



The output should be something like this, where the distances between every pair of terminal nodes are respected:
Output of 6 circles fixed



It's a circle packing problem with constraints, because all the circles must be as close between them as they are drawn in the input



The heuristic should be like:



1.- Add first circle



2.- Add second circle that is connected with the first and satisfying the required distance



3.- Add third circle that is connected with the first and second and satisfies the required distances



4.- ....



Maybe could I try also with bruteforce? I'm not sure



Thank you so much










share|improve this question
















I have a set of inequalities that I have to solve.
These inequalities are related to circle packing, where I have N circles and all of them are interrelated between the rest of the circles.
Which functions could I use to solve this?
I have tried using solve in MATLAB but this doesn't work with inequalities.
E.g. given 3 circles, the number of inequalities is (N*(N-1))/2, so with 3 cirlces I have 3 equations.
Let's call them A, B and C, with radius 200, 300 and 400 respectively.
With A,B and C I have 3 different inequalities:



dist(a,b) >= 200+300
dist(b,c) >= 300+400
dist(c,a) >= 200+400


Where



dist(p1,p2) = sqrt((p1.x - p2.x)^2 + (p1.y - p2.y)^2)


I would like to get p1.x, p1.y, p2.x, p2.y, p3.x, p3.y such as their distances are the closest to the right part of the inequalities.



With 3 circles there's no problem because it'll be always a solution that satisfies and minimizes the inequalities, but the problem comes when I have a lot of circles, p.e. 10 circles.



The objectives in this case are:



dist(a,b) = 200+300
dist(b,c) = 300+400
dist(a,c) = 200+400


Now I will show you what is my idea to solve this:



syms x1 x2 x3 x4 x5 y1 y2 y3 y4 y5

//What I want to get
unknowns = [ x1 x2 x3 x4 x5 y1 y2 y3 y4 y5]

//Equations that must be satisfied
eqns(1) = sqrt((x1-x2)^2 + (y1-y2)^2) >= 300; // dist(1,2)>=300
eqns(2) = sqrt((x1-x3)^2 + (y1-y3)^2) >= 400; // dist(1,3)>=400
eqns(3) = sqrt((x1-x4)^2 + (y1-y4)^2) >= 350; // dist(1,4)>=350
eqns(4) = sqrt((x1-x5)^2 + (y1-y5)^2) >= 200; // dist(1,5)>=200
eqns(5) = sqrt((x3-x2)^2 + (y3-y2)^2) >= 320; // dist(2,3)>=320
eqns(6) = sqrt((x4-x2)^2 + (y4-y2)^2) >= 300; // dist(2,4)>=300
eqns(7) = sqrt((x5-x2)^2 + (y5-y2)^2) >= 330; // dist(2,5)>=330
eqns(8) = sqrt((x3-x4)^2 + (y3-y4)^2) >= 130; // dist(3,4)>=230
eqns(9) = sqrt((x3-x5)^2 + (y3-y5)^2) >= 120; // dist(3,5)>=400
eqns(10) = sqrt((x5-x4)^2 + (y5-y4)^2) >= 100; // dist(4,5)>=130

// My objective functions I want to optimize
objs(1) = sqrt((x1-x2)^2 + (y1-y2)^2) == 300; // dist(1,2)=300
objs(2) = sqrt((x1-x3)^2 + (y1-y3)^2) == 400; // dist(1,3)=400
objs(3) = sqrt((x1-x4)^2 + (y1-y4)^2) == 350; // dist(1,4)=350
objs(4) = sqrt((x1-x5)^2 + (y1-y5)^2) == 200; // dist(1,5)=200
objs(5) = sqrt((x3-x2)^2 + (y3-y2)^2) == 320; // dist(2,3)=320
objs(6) = sqrt((x4-x2)^2 + (y4-y2)^2) == 300; // dist(2,4)=300
objs(7) = sqrt((x5-x2)^2 + (y5-y2)^2) == 330; // dist(2,5)=330
objs(8) = sqrt((x3-x4)^2 + (y3-y4)^2) == 130; // dist(3,4)=230
objs(9) = sqrt((x3-x5)^2 + (y3-y5)^2) == 120; // dist(3,5)=400
objs(10) = sqrt((x5-x4)^2 + (y5-y4)^2) == 100; // dist(4,5)=130

// And now.. what can I use??

//Solve doesn't work...
solve(eqns, unknowns, IgnoreAnalyticConstraints)


I would expect that the result satisfies all the equations at the same time, and the distance between every pair of circles is the closest to the desired, but NEVER smaller than it.



Here it's a graphic example:
If I have this input,
Input with 6 terminal nodes



The output should be something like this, where the distances between every pair of terminal nodes are respected:
Output of 6 circles fixed



It's a circle packing problem with constraints, because all the circles must be as close between them as they are drawn in the input



The heuristic should be like:



1.- Add first circle



2.- Add second circle that is connected with the first and satisfying the required distance



3.- Add third circle that is connected with the first and second and satisfies the required distances



4.- ....



Maybe could I try also with bruteforce? I'm not sure



Thank you so much







matlab optimization quadratic






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Mar 28 at 10:15







Daniel Bermejo Sánchez

















asked Mar 27 at 15:44









Daniel Bermejo SánchezDaniel Bermejo Sánchez

186 bronze badges




186 bronze badges















  • solve will give you an exact solution not the most optimized solution. So if your equalities lead to an overdetermined system with no exact solution, solve will of course failed. For optimization with constraint there is the fmincon function. Take also into account that if you have multiple objectif functions that need to be optimized at the same time matlab cannot guess which function should be the more optimized ! I guess that there is certainly a better approach

    – obchardon
    Mar 27 at 16:35











  • Are these objective functions equally weighted? Are these being solved in a sequential order? Are you wanting to minimize the sum of all 10 objective functions? (or the weighted sum) Perhaps you could edit the question and add some clarification.

    – SecretAgentMan
    Mar 27 at 19:00











  • Thank you both of you! @obchardon I tried yesterday with genetic algorithm ga but nothing worked so I will try with fmincon function. @SecretAgentMan I edited it adding more information, thanks for your advices

    – Daniel Bermejo Sánchez
    Mar 28 at 10:16


















  • solve will give you an exact solution not the most optimized solution. So if your equalities lead to an overdetermined system with no exact solution, solve will of course failed. For optimization with constraint there is the fmincon function. Take also into account that if you have multiple objectif functions that need to be optimized at the same time matlab cannot guess which function should be the more optimized ! I guess that there is certainly a better approach

    – obchardon
    Mar 27 at 16:35











  • Are these objective functions equally weighted? Are these being solved in a sequential order? Are you wanting to minimize the sum of all 10 objective functions? (or the weighted sum) Perhaps you could edit the question and add some clarification.

    – SecretAgentMan
    Mar 27 at 19:00











  • Thank you both of you! @obchardon I tried yesterday with genetic algorithm ga but nothing worked so I will try with fmincon function. @SecretAgentMan I edited it adding more information, thanks for your advices

    – Daniel Bermejo Sánchez
    Mar 28 at 10:16

















solve will give you an exact solution not the most optimized solution. So if your equalities lead to an overdetermined system with no exact solution, solve will of course failed. For optimization with constraint there is the fmincon function. Take also into account that if you have multiple objectif functions that need to be optimized at the same time matlab cannot guess which function should be the more optimized ! I guess that there is certainly a better approach

– obchardon
Mar 27 at 16:35





solve will give you an exact solution not the most optimized solution. So if your equalities lead to an overdetermined system with no exact solution, solve will of course failed. For optimization with constraint there is the fmincon function. Take also into account that if you have multiple objectif functions that need to be optimized at the same time matlab cannot guess which function should be the more optimized ! I guess that there is certainly a better approach

– obchardon
Mar 27 at 16:35













Are these objective functions equally weighted? Are these being solved in a sequential order? Are you wanting to minimize the sum of all 10 objective functions? (or the weighted sum) Perhaps you could edit the question and add some clarification.

– SecretAgentMan
Mar 27 at 19:00





Are these objective functions equally weighted? Are these being solved in a sequential order? Are you wanting to minimize the sum of all 10 objective functions? (or the weighted sum) Perhaps you could edit the question and add some clarification.

– SecretAgentMan
Mar 27 at 19:00













Thank you both of you! @obchardon I tried yesterday with genetic algorithm ga but nothing worked so I will try with fmincon function. @SecretAgentMan I edited it adding more information, thanks for your advices

– Daniel Bermejo Sánchez
Mar 28 at 10:16






Thank you both of you! @obchardon I tried yesterday with genetic algorithm ga but nothing worked so I will try with fmincon function. @SecretAgentMan I edited it adding more information, thanks for your advices

– Daniel Bermejo Sánchez
Mar 28 at 10:16













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