I'm trying to solve the below problem.
We define a budget for our long short allocation (Sum(W) == 0). Suppose one of the asset in that portfolio get weight of 25% and this means if we are allocating the 10 million then that asset will get 1 Mil but out margin constraints should limit this position to 500k USD. Now if I add a constraint that w[Asset] < 500k USD/ 10 Million. This can assign the weight of Asset as 0.25% but this won't utilize the complete margin. Now to solve this we normalize this weights as W[Assset] / Sum(abs(w)). But this will again break the 500k Usd constraint?
Is there any way to solve above?
I'm expecting to add constraint which will solve this.
Can this problem be solved in the MVO frameworks as complex problem or i have to shift to non convex optimization solvers?
Related
This is my first data science project and I need to select some data. Of course, I know that I can not just select all the data available because this will result in overfitting. I am currently investigating house prices in the capital of Denmark for the past 10 years and I wanted to know which type of houses I should select in my data:
Owner-occupied flats and houses (This gives a dataset of 50000 elements)
Or just owner-occupied flats (this gives a dataset of 43000 elements)
So as you can see there are a lot more owner-occupied flats sold in the capital of Denmark. My opinion is that I should select just the owner-occupied flats because then I have the "same" kind of elements in my data and still have 43000 elements.
Also, there are a lot higher taxes involved if you own a house rather than owning an owner-occupied flat. This might affect the price of the house and skew the data a little bit.
I have seen a few projects where both owner-occupied flats and houses are selected for the data and the conclusion was overfitting, so that is what I am looking to avoid.
This is an classic example of over-fitting due to lack of data or insufficient data.
Let me example the selection process to resolve this kind of problem. I will example using the example of credit card fraud then relate that with your question or any future problem of prediction with classified data.
In ideal world credit card fraud are not that common. So, if you look at the real data you will find only 2% data which resulted in fraud. So, if you train a model with this datasets it would be biased as you don't have normal distribution of the class (i.e fraud and none fraud transaction in your case its Owner-occupied flats and houses). There are 4 a way to tackle this issue.
Let's Suppose Datasets has 90 none fraud data points and 10 fraud data points.
1. Under sampling majority class
In this we just select 10 data points from 90 and train model with 10:10 so distribution is normalised (In your case using only 7000 of 43000 flats). This is not ideal way as we would be throughout a huge amount of data.
2. Over sampling minority class by duplication
In this we duplicate the 10 data points to make it 90 data point distribution is normalised (In your case duplicating 7000 house data to make it 43000 i.e equal to that of flat). While this work there is a better way.
3. Over sampling minority class by SMOTE (recommended)
Synthetic Minority Over-sampling Technique is a technique we use k nearest neigbors algo to generate the minority class in your case the housing data. There is module named imbalanced-learn (here) which can be use to implement this.
4. Ensemble Method
In this method you divide your data into multiple datasets to make it balance for example dividing 90 into 9 sets so that each set can have 10 fraud class data and 10 none fraud class data. In your case diving 43000 in batch of 7000. After that training each one separately and using majority vote mechanism to predict.
So now I have created the following diagram. The green line shows the price per square meter of owner occupied flats and the red line shows price per square meter of houses (all prices in DKK). I was wondering if there is imbalanced classification? The maximum deviation of the prices is atmost 10% (see for example 2018). Is 10% enough to say that the data is biased and hence therefore is imbalanced classified?
I'm applying LSTM on time series forecasting with 20 lags. Suppose that we have two cases. The first one just using five lags and the second one (like my case) is using 20 lags. Is it correct that for the second case we need more units compared to the former one? If yes, how can we support this idea? I have 2000 samples for training the model, so this is the main limitation for increasing number of units here.
It is very difficult to give an exact answer as the relationship between timesteps and number of hidden units is not an exact science. For example, following factors can affect the number of units required.
Short term memory problem vs long-term memory problem
If your problem can be solved with relatively less memory (i.e. requires to remember only a few time steps) you wouldn't get much benefit from adding more neurons while increasing the number of steps.
The amount of data
If you don't have enough data for the model to learn from (which I feel like you will run into with 2000 data points - but I could be wrong), then increasing the number of timesteps won't help you much.
The type of model you use
Depending on the type of model you use (e.g. LSTM / GRU ) you might get different results (this is not always true but can happen for certain problems)
I'm sure there are other factors out there, but these are few that came to my mind.
Proving more units give better results while having more time steps (if true)
That should be relatively easy as you can try few different options,
5 lags with 10 / 20 / 50 hidden units
20 lags with 10 / 20 / 50 hidden units
And if you get better performance (e.g. lower MSE) with 20 lags problem than 5 lags problem (when you use 50 units), then you have gotten your point across. And you can reinforce your claims by showing results with different types of models (e.g. LSTMs vs GRUs).
I have a VRP in which I would like to include fuel consumption as soft constraint and that it is different between vehicles based on type. So I would want the engine to select the vehicles with the least fuel consumption.
I thought about adding a multiplier to the vehicle type so that it is multiplied with distance as soft constraint, is it possible? and would it affect the result negatively?
Thanks,
Yes, that's possible.
You distances can be in km. Then your score rule just multiplies each distance (= km) driven by a vehicle by that vehicle's vehicle.getCostPerKm().
You can even also keep track of driving time in seconds for each distance and build one big weighted function:
addSoft(..., - ($distanceInKm * $vehicle.getCostPerKm() + $distanceInSeconds * $vehicle.getDriverWagePerSecond()));
Consider a problem whose solution maximizes an objective function.
Problem : From 500 elements, 15 needs to be selected (candidate solution), Value of Objective function depends on the pairwise relationships between the elements in a candidate solution and some more.
The steps for solving such a problem is described here:
1. Generate a set of candidate solutions in guided random manner(population) //not purely random the direction is given to generate the population
2. Evaluating the objective function for current population
3. If the current_best_solution exceeds the global_best_solution, then replace the global_best with current_best
4. Repeat steps 1,2,3 for N (arbitrary number) times
where size of population and N are smaller (approx 50)
After N iterations it returns a candidate solution stored in global_best_solution
Is this the description of a well-known algorithm?
If it is, what is the name of that algorithm or if not under which category these type of algorithms fit?
What you have sounds like you are just fishing. Note that you might as well get rid of steps 3 and 4 since running the loop 100 times would be the same as doing it once with an initial population 100 times as large.
If you think of the objective function as a random variable which is a function of random decision variables then what you are doing would e.g. give you something in the 99.9th percentile with very high probability -- but there is no limit to how far the optimum might be from the 99.9th percentile.
To illustrate the difficulty, consider the following sort of Travelling Salesman Problem. Imagine two clusters of points A and B, each of which has 100 points. Within the clusters, each point is arbitrarily close to every other point (e.g. 0.0000001). But -- between the clusters the distance is say 1,000,000. The optimal tour would clearly have length 2,000,000 (+ a negligible amount). A random tour is just a random permutation of those 200 decision points. Getting an optimal or near optimal tour would be akin to shuffling a deck of 200 cards with 100 read and 100 black and having all of the red cards in the deck in a block (counting blocks that "wrap around") -- vanishingly unlikely (It can be calculated as 99 * 100! * 100! / 200! = 1.09 x 10^-57). Even if you generate quadrillions of tours it is overwhelmingly likely that each of those tours would be off by millions. This is a min problem, but it is also easy to come up with max problems where it is vanishingly unlikely that you will get a near-optimal solution by purely random settings of the decision variables.
This is an extreme example, but it is enough to show that purely random fishing for a solution isn't very reliable. It would make more sense to use evolutionary algorithms or other heuristics such as simulated annealing or tabu search.
why do you work with a population if the members of that population do not interact ?
what you have there is random search.
if you add mutation it looks like an Evolution Strategy: https://en.wikipedia.org/wiki/Evolution_strategy
I have a set of vertices with some profit defined between each pair of vertices such that profit(i,j) may not be equal to profit(j,i). Moreover there exist positive weight cycles and the profit may be negative.
This is a NP-hard problem to find the maximum profit, so the problem is to maximize the profit visiting each city at most one( all cities need not to be visited).
I have tried following algorithms to find this:
Greedy algorithm on complete set of vertices.
Greedy with brute force: First find the greedy sequence of vertices. This gives the approximate sets of vertices which nearly form clusters. Now take consecutive set of say 8 cities and rearrange them to find maximum profit using brute force.
But these do not give very good results when tried on 100 vertices.
Are there any other probabilistic or approximate methods to maximize the cost?
Im not sure i understand the question but Can't you sort the edges and thn find the minimum spanning tree. Something how kruskals algo works?
EDIT
If there exist negative weight edges, you can stop the moment the weight becomes negative.