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\ No newline at end of file
diff --git a/Markowitz/STD/RANDOM_SEED_9_NUM_ASSETS_5_MARKOWITZ_POI_STD_LIMIT.mof.json.mof.json b/Markowitz/STD/RANDOM_SEED_9_NUM_ASSETS_5_MARKOWITZ_POI_STD_LIMIT.mof.json.mof.json
new file mode 100644
index 0000000000000000000000000000000000000000..048441c156739158dc69f35549075c4571d050be
--- /dev/null
+++ b/Markowitz/STD/RANDOM_SEED_9_NUM_ASSETS_5_MARKOWITZ_POI_STD_LIMIT.mof.json.mof.json
@@ -0,0 +1 @@
+{"name":"MathOptFormat Model","version":{"major":1,"minor":7},"variables":[{"name":"σ_max"},{"name":"x[1]"},{"name":"x[2]"},{"name":"x[3]"},{"name":"x[4]"},{"name":"x[5]"},{"name":"t"}],"objective":{"sense":"max","function":{"type":"ScalarAffineFunction","terms":[{"coefficient":0.13636466604650632,"variable":"x[1]"},{"coefficient":0.10805854908268851,"variable":"x[2]"},{"coefficient":0.09398724317967545,"variable":"x[3]"},{"coefficient":0.05785800502257411,"variable":"x[4]"},{"coefficient":0.05730259617876898,"variable":"x[5]"}],"constant":0.0}},"constraints":[{"name":"c1","function":{"type":"ScalarAffineFunction","terms":[{"coefficient":1.0,"variable":"x[1]"},{"coefficient":1.0,"variable":"x[2]"},{"coefficient":1.0,"variable":"x[3]"},{"coefficient":1.0,"variable":"x[4]"},{"coefficient":1.0,"variable":"x[5]"}],"constant":0.0},"set":{"type":"EqualTo","value":1.0}},{"name":"c1_1","function":{"type":"ScalarAffineFunction","terms":[{"coefficient":-1.0,"variable":"σ_max"},{"coefficient":1.0,"variable":"t"}],"constant":0.0},"set":{"type":"LessThan","upper":0.0}},{"name":"c1_2","function":{"type":"VectorAffineFunction","terms":[{"output_index":1,"scalar_term":{"coefficient":1.0,"variable":"t"}},{"output_index":2,"scalar_term":{"coefficient":0.06393394011315544,"variable":"x[1]"}},{"output_index":3,"scalar_term":{"coefficient":0.040906715862699866,"variable":"x[1]"}},{"output_index":3,"scalar_term":{"coefficient":0.036604310760424176,"variable":"x[2]"}},{"output_index":4,"scalar_term":{"coefficient":0.050088232771437396,"variable":"x[1]"}},{"output_index":4,"scalar_term":{"coefficient":0.022194127029407207,"variable":"x[2]"}},{"output_index":4,"scalar_term":{"coefficient":0.011865813866082079,"variable":"x[3]"}},{"output_index":5,"scalar_term":{"coefficient":0.031782650899104405,"variable":"x[1]"}},{"output_index":5,"scalar_term":{"coefficient":0.00619180811639755,"variable":"x[2]"}},{"output_index":5,"scalar_term":{"coefficient":0.02620606387722192,"variable":"x[3]"}},{"output_index":5,"scalar_term":{"coefficient":0.0035112269537722353,"variable":"x[4]"}},{"output_index":6,"scalar_term":{"coefficient":0.040920779545494335,"variable":"x[1]"}},{"output_index":6,"scalar_term":{"coefficient":0.031052870608861256,"variable":"x[2]"}},{"output_index":6,"scalar_term":{"coefficient":-0.0006060488440043858,"variable":"x[3]"}},{"output_index":6,"scalar_term":{"coefficient":-0.02226765018551729,"variable":"x[4]"}},{"output_index":6,"scalar_term":{"coefficient":0.0005990584661817441,"variable":"x[5]"}}],"constants":[0.0,0.0,0.0,0.0,0.0,0.0]},"set":{"type":"SecondOrderCone","dimension":6}},{"function":{"type":"Variable","name":"x[1]"},"set":{"type":"GreaterThan","lower":0.0}},{"function":{"type":"Variable","name":"x[2]"},"set":{"type":"GreaterThan","lower":0.0}},{"function":{"type":"Variable","name":"x[3]"},"set":{"type":"GreaterThan","lower":0.0}},{"function":{"type":"Variable","name":"x[4]"},"set":{"type":"GreaterThan","lower":0.0}},{"function":{"type":"Variable","name":"x[5]"},"set":{"type":"GreaterThan","lower":0.0}},{"function":{"type":"Variable","name":"t"},"set":{"type":"GreaterThan","lower":0.0}},{"function":{"type":"Variable","name":"σ_max"},"set":{"type":"Parameter","value":0.04}}]}
\ No newline at end of file
diff --git a/Markowitz/STD/markowitz_std.jl b/Markowitz/STD/markowitz_std.jl
new file mode 100644
index 0000000000000000000000000000000000000000..2d61789832479f8bfb1ef4a7b049826a4085c42c
--- /dev/null
+++ b/Markowitz/STD/markowitz_std.jl
@@ -0,0 +1,74 @@
+using JuMP
+using MathOptInterface
+using Ipopt
+
+using COSMO, LinearAlgebra
+
+# ================
+# Problem setup
+# ================
+
+# User-defined number of assets
+n = 5  # <-- change this as needed
+for n in [5, 10, 20, 50, 100, 200, 500, 1000]
+    for i in 1:10
+        # 1) Create a conic model
+        model = JuMP.Model(COSMO.Optimizer)
+        set_silent(model)
+
+        # 2) Parameter: standard deviation limit (σ_max)
+        @variable(model, σ_max in Parameter(0.04))  # e.g. limit = 0.04
+
+        # 3) Decision variables: x (portfolio weights)
+        @variable(model, x[1:n] >= 0)
+        @constraint(model, sum(x) == 1)
+
+        # 4) Randomly generate covariance & returns
+
+        # Generate a random matrix A
+        A = rand(n, n)
+        # Build a PSD matrix by Sigma = A' * A
+        Sigma = A' * A
+
+        # Optionally scale down the matrix to keep variances in a smaller range
+        Sigma .*= 0.002  # adjust as needed
+
+        # Random expected returns (e.g., range ~ [0, 0.15])
+        mu = 0.05 .+ 0.10 .* rand(n)
+
+        # Perform a Cholesky factorization of Sigma
+        # (For numerical stability, Sigma is guaranteed PSD)
+        L = cholesky(Symmetric(Sigma)).L
+
+        # 5) Objective: maximize mu' * x
+        @objective(model, Max, sum(mu[i] * x[i] for i in 1:n))
+
+        # 6) Conic constraint for the variance limit:
+        #     || L*x || <= σ_max
+        # which enforces x' * Sigma * x <= (σ_max)^2
+        @variable(model, t >= 0)
+        @constraint(model, [t; L * x] in SecondOrderCone())
+        @constraint(model, t <= σ_max)
+
+        # Solve once for initial σ_max
+        optimize!(model)
+
+        termination_status(model) == MOI.OPTIMAL || error("Optimization unsuccesful.")
+
+        obj = objective_value(model)
+
+        write_to_file(model, "RANDOM_SEED_$(i)_NUM_ASSETS_$(n)_MARKOWITZ_POI_STD_LIMIT.mof.json.mof.json")
+
+        # Check if the file was written correctly
+
+        model_test = read_from_file("RANDOM_SEED_$(i)_NUM_ASSETS_$(n)_MARKOWITZ_POI_STD_LIMIT.mof.json.mof.json"; use_nlp_block = false)
+
+        set_optimizer(model_test, COSMO.Optimizer)
+
+        JuMP.optimize!(model_test)
+
+        termination_status(model_test) == MOI.OPTIMAL || error("Optimization unsuccesful.")
+
+        objective_value(model_test) ≈ obj || error("Objective values do not match.")
+    end
+end
\ No newline at end of file